Modelling and Adaptive Control

‘Colleagues, coworkers, former students and friends of Professor Liu Hsu, from all over the world, join this special issue to celebrate his 70th birthday and recognize his extraordinary achievements during his long career as a researcher, educator and academic leader. Many of us have remained in touch with our dear friend Liu for decades, benefited from his support, admired his many talents and enjoyed his contagious joie de vivre. We have been inspired not only by his research vision and originality, but also by his humanity, broad culture and his love of music. In his quiet and modest manner he has been able to share his intellectual riches with all of us. The remarkable academic career of Professor Liu Hsu will serve as a role model for many generations of researchers and educators in our field.’ Petar Kokotovic ‘Jubilee gives a good chance to express admiration for our good friend Professor Liu Hsu, a brilliant scientist and a charming personality. His research results in several areas of control theory and applications are well known to international control community. Professor Hsu has been a core figure in establishing international cooperation in sliding mode control, being a member of our IEEE Technical Committee and one of the organizers of our biennial international workshops within the last several decades. His own presentations and comments always caused interesting discussions. Not only scientific component attracts colleagues to participate in them, but his friendly manner of communications, tolerant reaction to doubtful arguments along with soft humor. Dear Liu, it is your decision to retire, but keep in mind that we need you and hope, that joy of contacts with you will be with us for many years.’ Vadim Utkin As highlighted in the recent special issues 1, 2, the field of adaptive control has grown and evolved over the past 50years – its concepts, methods, and tools are by now well established cornerstones of many new fields and technical branches. A great deal of attention has been given to overcome the intrinsic limitations of classical adaptive control approaches. Thanks to the effort of many researchers, a novel class of strategies has appeared proposing new theoretical frameworks and reporting many successful technological applications. Professor Liu Hsu is one of the important names in the field of adaptive control. He has made major contributions in this area proposing new control strategies of uncertain plants with guaranteed stability, robustness, and adaptability. Among his ground-breaking contributions, one finds the proof of existence of bursting phenomena in model reference adaptive controllers (MRAC) with leaky estimators, the so-called sigma modification. Then, he was able to develop a globally stable adaptive notch filter to determine online the frequency of a sine wave with unknown amplitude, a particularly useful practical result in a wide variety of engineering applications. He and co-authors provided important contributions towards the solution of the longstanding problem of multivariable MRAC with unknown high-frequency gain matrix. In early works, an innovative combination of adaptive control and variable structure systems resulted in the pioneering variable structure (VS) MRAC. Later on, to improve the transient properties and robustness of sliding mode control, with the important advantage of having a continuous control signal free of chattering, he proposed the novel binary MRAC. These control strategies have been successfully applied to robot visual servoing and dynamic positioning of remotely operated underwater vehicles. In the last years, an open problem of global exact tracking was solved using a hybrid control version of the VS-MRAC and higher order sliding modes for chattering suppression. In addition, novel adaptive extremum-seeking controllers and nonlinear high-gain control strategies free of peaking were also proposed by him. In his most recent work, generalized passivity is being investigated to obtain fast adaptation and to reduce the complexity of adaptive controllers, opening a new avenue of research. Professor Liu Hsu has made significant and fundamental contributions to the areas of adaptive control and variable structure sliding mode control and their application to robotics. Although we personally knew all these results, it was heartening to hear high praise for his work from central figures at many controls conferences. His contributions are documented in over 250 technical papers. The scholarly accomplishments go beyond being an innovative researcher, but also an inspiring mentor and dedicated teacher. He has graduated more than 25 PhD students. Most of them hold academic positions in Brazil and abroad. For his contributions to engineering education and research, Professor Liu Hsu has been recognized with the highest faculty awards in Brazil: 2008 Grand-cross medal by ONMC (Brazilian National Order of Scientific Merit) and 2005 Commander medal by ONMC. In 2011, he received from CAPES (Brazilian Coordination for the Improvement of Higher Level Personnel) the National Award of Best Thesis Advisor in Electrical Engineering. Over long periods, he performed, with efficiency and objectivity, organizing duties in IEEE CSS Technical Committee on Variable Structure Systems and Sliding Mode Control and also in Brazilian Academy of Sciences. In what follows, we briefly recall the contents of the 23 contributions of this double special issue. The list of collaborators includes well-known researchers in adaptive control and variable structure systems, which are colleagues, co-authors, and former doctoral students of Professor Liu Hsu. The paper 3 by Zhu, Krstic, Su, and Xu presents a variation on adaptive backstepping output feedback control design for uncertain minimum-phase linear systems. Unlike the traditional nonlinear design, the proposed control method is linear and Lyapunov based without utilizing overparameterization, tuning functions, or nonlinear damping terms to address parameter estimation error. Local stability of the closed-loop system and trajectory tracking are guaranteed. Hypersonic missile control in the terminal phase is addressed by Yu, Shtessel, and Edwards in 4 using continuous adaptive higher order sliding mode (AHOSM) control with adaptation. The AHOSM self-tuning controller is proposed and studied. The double-layer adaptive algorithm is based on equivalent control concepts and ensures non-overestimation of the control gain to help mitigating control chattering. In 5, Barkana has developed adaptive controllers to guarantee stability and asymptotically perfect tracking under ideal conditions. In particular, the simple adaptive control methodology has been developed to avoid the use of identifiers, observer-based controllers, and in general, to avoid using large-order adaptive controllers in the control loop. This paper revisits and modifies the use of various components of the simple adaptive control approach and shows how one can use passivity concepts such that, while it maintains robustness with disturbances, it also allows asymptotically perfect tracking in ideal conditions. The paper 6 by Geromel, Deaecto, and Colaneri introduces and focuses on a new control strategy for continuous-time Markov jump linear systems-denominated minimax control. It generalizes switching and linear parameter varying control strategies and is determined such as to preserve stochastic stability and guaranteed performance. The special classes of Markov mode-dependent and mode-independent control are considered. The design methodology is characterized by minimax problems for which the existence of a saddle point is the central issue to be taken into account. In the paper 7 by Bartolini, Estrada, and Punta, the output-tracking problem for a class of non-affine nonlinear systems with unstable zero-dynamics is addressed. The system output must track a signal, which is the sum of a known number of sinusoids with unknown frequencies amplitudes and phases. The non-minimum phase nature of the considered systems prevents the direct tracking by standard sliding mode methods, which are known to generate unstable behaviors of the internal dynamics. The proposed adaptive indirect method relies on the properties of differentially flat systems between the original output and a suitably designed flat output. In the paper 8 by Oliveira, Peixoto, and Nunes, it is proposed an adaptive output-feedback controller for uncertain linear systems without a priori knowledge of the plant high-frequency gain sign. To deal with parametric uncertainties and unmodeled dynamics, the authors consider a robust adaptive strategy named binary model reference adaptive control. The effective way of tackling unknown high-frequency gain sign is employing monitoring functions. The developed adaptive control guarantees global exponential stability of the closed-loop error system with respect to a compact residual set. Wen, Tao, and Liu have developed in 9 adaptive control schemes for uncertain multivariable systems with unmatched input disturbances and are applied to an aircraft flight turbulence compensation problem. Key relative degree conditions from system input and disturbance are derived in terms of system interactor matrices for the design of a nominal state or output feedback control law that ensures desired asymptotic output tracking and disturbance rejection. All closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system and disturbance parameter uncertainties. Unlike previous works on high-gain observers, the focus of the paper 10 by Prasov and Khalil is the effect measurement noise has on the tracking error, not the estimation error. Although a tradeoff exists between the speed of state reconstruction and the bound on the steady-state estimation error, such a compromise is not evident in the tracking error of the first state. This work provides the relationship between the high-gain observer parameter and the tracking error and its subsequent derivatives. The paper 11 by Cardim, Teixeira, Assunção, Ribeiro, Covacic, and Gainois concerns with the design of variable structure controllers for uncertain switched linear plants. The proposed method is based on Lyapunov–Metzler inequalities and on properties of strictly positive real (SPR) systems, with the advantage that it can be applied in control of uncertain switched linear system. Examples illustrate the effectiveness of the robust control system, including applications of the proposed methods in the design of switching control strategies for active suspensions systems in road vehicles. In the work 12 by Leite and Lizarralde, the 3D visual tracking problem is considered for a robot manipulator with uncertainties in the kinematic and dynamic models. The visual feedback is provided by a fixed and uncalibrated camera located above the robot workspace. Adaptive visual servoing schemes, based on a kinematic approach, are developed for image-based look-and-move systems allowing for both depth and planar tracking of a reference trajectory, without using image velocity and depth measurements. In order to include the robot dynamics in the presented solution, a cascade control strategy is developed based on an indirect/direct adaptive method. The paper 13 by Incremona and Ferrara addresses the design of a model-based event-triggered sliding mode control strategy of adaptive type. The overall proposal can be regarded as a networked control scheme, because one of the design objectives is to reduce the number of transmissions of the plant state over the network used to construct the control loop. The key idea consists in using the actual plant state or the state of a suitably updated nominal model of the plant to generate the control variable, depending on the magnitude of the sliding variable. A variable structure model-reference adaptive control of impedances and admittances – driving-point (DP) functions – is proposed in 14 by Cunha and Costa. Only voltage and current measurements are required to implement the controllers. The inclusion of a prefilter in the reference model allows the synthesis of quite general DP functions, even with nonminimum phase zeros and unstable poles. It is shown that the stability of the closed-loop system depends only on the source DP function and the chosen reference model. In the paper 15 by Liu, Yang, and Lin, an adaptive output feedback control scheme is proposed for a class of nonlinear systems with possible actuator failures. The system not only involves unknown parameters but also takes nonlinear terms linear in the unmeasured states into account and is preceded by hysteretic actuators whose nonlinearities are characterized by the saturated Prandtl–Ishlinskii model. By developing a high-gain observer with one dynamic gain, the closed-loop stability and arbitrarily small tracking error can be guaranteed. The paper 16 by Kallakuri, Keel, and Bhattacharyya presents new methodologies to design a set of controllers such that every controller in the set preserves closed-loop stability of a given multivariable plant under prescribed loop failures. The methods are strictly based on frequency response data of the plant that can be easily measured by experiments. In the paper 17, Julius, Zhang, Qiao, and Wen present a new multi-input adaptive notch filter algorithm that can be used to extract the periodic components from multiple circadian signals simultaneously. Once the periodic components are extracted, the next step is to relate their phases with the circadian phase. For this, the authors propose a nonlinear observer, which is based on a model of the circadian phase dynamics widely used in the study of biological oscillators. The work 18 by Dias, Queiroz, Araujo, and Dias proposes a control structure to be applied to robotic manipulators. The proposed controller can be divided into two parts. The first one is a left inverse system, which is used to decouple the dynamic behavior of the joints. The second is a sliding mode controller, which is applied for each decoupled joint. The proposed structure used only input/output measurements, reduces the control signal chattering, and it is robust to uncertainties. The paper 19 by Alves, Teixeira, De Oliveira, Cardim, Assunção, and De Souza considers a class of uncertain nonlinear systems described by Takagi–Sugeno (T-S) fuzzy models with matched uncertainties and disturbances. Considering the plant is subject to actuator saturation, a switched control design method is proposed such that the equilibrium point of the controlled system is locally asymptotically stable, for all initial conditions in a region obtained in the design procedure. An exact representation of the minimum function using signal functions is presented. Therefore, it is offered a bridge between the switched control and variable structure control laws, because they are usually based on minimum and signal functions, respectively. In the paper 20 by Bobtsov, Pyrkin, and Ortega, a new class of estimators for permanent magnet synchronous motors is proposed. Using a novel representation of the permanent magnet synchronous motor dynamics and some suitable filtering, the authors obtain new solutions to the problems of identification of the stator resistance–inductance and flux estimation with known electrical parameters. The paper 21 by Bhaya and Kaszkurewicz views the classical Chiu–Jain algorithm, originally proposed for congestion control of network links, as a decentralized algorithm for the fair allocation of a total of units of a shared resource among users. A new analysis is given of the general case of additive increase and multiplicative decrease (AIMD) dynamics, from the perspective of virtual equilibria and variable structure systems, leading to a better understanding of the Chiu–Jain algorithm, which is one example of AIMD dynamics. Subsequently, a new adaptive version of the algorithm, called adaptive AIMD, is described, with the same property of converging to the fair share, without assuming that it is known. The paper 22 by Menon, Edwards, and Shtessel considers the problem of reconstructing state information in all the nodes of a complex network of dynamical systems. A supervisory adaptive sliding mode observer configuration is proposed for estimating the states. A linear matrix inequality (LMI) approach is suggested to synthesize the gains of the sliding mode observer. Although deployed centrally to estimate all the states of the complex network, the design process depends only on the dynamics of an individual node of the network. The main contribution of the paper 23 by Chriette, Plestan, Castañeda, Pal, Guillo, Odelga, Rajappa, and Chandra is to propose a scheme of attitude controller for a class of unmanned aerial vehicles based on an adaptive version of the super-twisting algorithm. The adaptive gain allows to design the controller without knowing bounds of the uncertainties and perturbations. This controller is validated by experimental results. The paper 24 by García-Carrillo, Vamvoudakis, and Hespanha proposes a new approximate dynamic programming algorithm to solve the infinite-horizon optimal control problem for weakly coupled nonlinear systems. The algorithm is implemented as a three-critic/four-actor approximators structure, where the critic approximators are used to learn the optimal costs, while the actor approximators are used to learn the optimal control policies. An adaptive second-order sliding mode output feedback controller is developed by Negrete–Chávez and Moreno in 25 to deal with the case that the bound of the uncertainty/perturbation is unknown. The control structure consists in a twisting controller and a super-twisting observer to estimate the unmeasured variable. The gains of the controller and observer are parameterized in terms of a scalar gain, such that increasing these two gains, it is always possible to find values to (finite-time) stabilize the closed-loop system. The main technical contribution of the paper is to give a sound and non-trivial Lyapunov analysis of this otherwise intuitively simple idea. To conclude this editorial, we thank the Managing Editor Professor Mike Grimble for all kind support and Martin Wells for their timely help with the logistics of paper handling. Last but not least, we are also grateful to all anonymous reviewers for their prompt assistance to this special issue.

The widespread deployment of the advanced computer technology in business and industries has demanded the high standard on quality of service (QoS). For example, many Internet applications, i.e. online trading, e-commerce, and real-time databases, etc., execute in an unpredictable general-purpose environment but require performance guarantees. Failure to meet performance specifications may result in losing business or liability violations. As systems become distributed and complex, it has become a challenge for QoS design. The ability of on-line identification and auto-tuning of adaptive control systems has made the adaptive control theoretical design an attractive approach for QoS design. However, there is an inherent constraint in adaptive control systems, i.e. a conflict between asymptotically good control and asymptotically good on-line identification. This paper first identifies and analyzes the limitations of adaptive control for network QoS by extensive simulation studies. Secondly, as an approach to mitigate the limitations, we propose an adaptive dual control framework. By incorporating the existing uncertainty of on-line prediction into the control strategy and accelerating the parameter estimation process, the adaptive dual control framework optimizes the tradeoff between the control goal and the uncertainty, and demonstrates robust and cautious behavior. The experimental study shows that the adaptive dual control framework mitigate the limitations of the conventional adaptive control framework. Compared with the conventional adaptive control framework under the medium uncertainty, the adaptive dual control framework reduces the deviation from the desired hit-rate ratio from 40% to 13%.

This paper presents the application of an adaptive output feedback control design for an aeroelastic genetic transport model. The adaptive design uses a novel parameter dependent Riccati equation approach. The adaptive controller is intended to augment a nominal, fixed g ain, observer based output feedback control law. Although the formulation is in the setting of model following adaptive control, the realization of the adaptive controller does not require implementing the reference model. In this regard, the cost of implementing the adaptive controller, above that of a fixed gain control law, i s far less than that of other methods. In addition, it is applicable to output feedback adaptive control design for non-minimum phase plants. I. Introduction Research in adaptive output feedback control of uncertain nonlinear systems is motivated by the many emerging applications that employ novel actuation devices for active control of flexible structures and fluid flows. These applications include actuators such as piezo-electric films and s ynthetic jets, which are typically nonlinearly coupled to t he plant dynamics they are intended to control. Models for these applications vary from accurate low frequency models to models that crudely approximate the true dynamics even at low frequencies. Examples of applications include active damping of flexible structures, control of aeroservoelasti c aircraft, and active control of flows. Adaptive control can be used to satisfy performance requirements in the presence of large scale parameter uncertainty, and improved safety in the event of actuator failure. The adaptive output feedback approach used in this paper is taken from Ref. 1. It assumes that a state observer is employed in the nominal controller design. The observer design is modified and employed in the adaptive part of the design. This is combined with a novel adaptive weight update law. The weight update law ensures that estimated states follow both the reference model states and the true st ates so that both state estimation errors and state tracking errors are bounded. Although the formulation is in the setting of model following adaptive control, the realization of the adaptive controller uses the observer of the nominal controller in place of the reference model to generate an error signal. Thus the only components that are added by the adaptive controller are the realizations of the basis functions and the weight adaptation law. The realization is even less complex than that of implementing a model reference adaptive controller in the case of state feedback. The stabi lity analysis employs a Lyapunov candidate function that entails the solution of a parameter dependent Riccati equation (rather than a Lyapunov equation) to show that all error signals are uniformly ultimately bounded (UUB). It is shown how the upper limit for the Riccati equation parameter is employed in the design of the adaptive law, and also influen ces the ultimate bounds for the state estimate error and the adapted weight error.

Special issue on ‘new results on neuro‐fuzzy adaptive control systems’

Oscillations in systems with relay feedback.- Compatibility of stochastic and worst case system identification: Least squares, maximum likelihood and general cases.- Some results for the adaptive boundary control of stochastic linear distributed parameter systems.- LMS is H? optimal.- Adaptive control of nonlinear systems: A tutorial.- Design guidelines for adaptive control with application to systems with structural flexibility.- Estimation-based schemes for adaptive nonlinear state-feedback control.- An adaptive controller inspired by recent results on learning from experts.- Stochastic approximation with averaging and feedback: faster convergence.- Building models from frequency domain data.- Supervisory control.- Potential self-tuning analysis of stochastic adaptive control.- Stochastic adaptive control.- Optimality of the adaptive controllers.- Uncertain real parameters with bounded rate of variation.- Averaging methods for the analysis of adaptive algorithms.- A multilinear parametrization approach for identification of partially known systems.- Adaptive filtering with averaging.

An adaptive dynamic inversion control formulation is presented that takes advantage of the inherent dynamic structure of the state-space description of a large class of systems. The formulations impose the exact kinematic differential equations, thereby restricting the adaptation process that compensates for model errors to the acceleration level. The utility of this formulation is demonstrated for the problem of fault tolerance to actuator failures on redundantly actuated systems. The approach incorporates an actuator failure model in the controller formulation, so that actuator failure can be identified as a change in the parameters of the failure model. Tracking of reference trajectories is imposed, and initial error conditions and structured parametric uncertainties are incorporated explicitly in both the plant parameters and the control influence matrix. A numerical example consisting of a nonlinear model of an F-16 type aircraft with thrust vectoring is presented. Simulation results show that the fault-tolerant adaptive controller is capable of simultaneously handling parametric uncertainties, large initial condition errors, and actuator failures while maintaining adequate tracking performance. N recent years, there has been much interest in the development of reconfigurable control systems that can accommodate actuator failures without compromising mission integrity. There has been substantial progress in the development of real-time failure detection and isolation algorithms, system identification after failure, and control reconfiguration techniques in aerospace applications. In Ref. 1, a survey of various reconfigurable flight control methodologies is presented and it is shown that most traditional reconfiguration flight control approaches rely on failure detection and isolation. The complexity of such a system with this feature grows with the increase in the number of failures, and there tends to be a significant possibility of false alarms. 1,2 A different approach to reconfigurable flight control is based on adaptive control theory, in which the adaptive control structure implicitly reconfigures the control law using adaptive estimates of the altered dynamics after failure. 3 In Ref. 3, an adaptive control scheme is presented that uses a linear approximation of the plant model to compute the control, and a neural network based adaptive control law for flight reconfiguration has been developed and successfully flight tested. 4−6 A robust fault-tolerant controller has also been developed to reject state-dependent disturbances. 7 The approach presented in this paper uses a structured nonlinear adaptive dynamic inversion control methodology. Instead of using an explicit failure detection and isolation algorithm, this methodology is based on the adaptive control theory where the controller is constantly updating itself. This methodology is applicable to a general class of nonlinear systems that are affine in the control with uncertain parameters appearing linearly. Fault-tolerance capability is introduced by incorporating a failure model in the controller so that a failure can be identified and compensated for by a change in the parameters of the failure model. First, model reference adaptive control, structured model reference adaptive control, and structured adaptive model inversion

In this paper we decribe our initial development and testing of the framework referred to as Algorithm Design & Validation for Adaptive Nonlinear Control Enhancement (ADVANCE). The key elements of ADVANCE are suitable performance metrics for adaptive control systems, and an automated tuning procedure using our in-house developed Automatic Tuner for non-Linear and Adaptive Systems (ATLAS). The paper describes a comparison study of the state-of-the-art adaptive flight control algorithms on two challenging testbeds. The development of the ADVANCE framework involved several steps: (i) Development of suitable performance comparison metrics for adaptive control systems; (ii) Development of a “plug-and-play” capability that enables rapid implementation and simulation testing of different advanced adaptive control algorithms; and (iii) Comprehensive simulation studies and performance comparison of the state-of-the-art adaptive control algorithms on a high-fidelity simulation of miniature tail-sitter UAV with significant nonlinearities and uncertainty, and a semi-nonlinear simulation of F/A-18 dynamics. Results presented in the paper demonstrate the feasibility and potential of the ADVANCE framework, and further development of the algorithms and testing procedures is expected to give rise to a set of recommendations and guidelines regarding the use, tuning and implementation of different adaptive flight control algorithms to different problems in flight control. This will also facilitate flight certification of adaptive flight control algorithms.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

A varied method for producing adaptive control is developed in this paper. It involves developing a simplified version of the current Nonlinear Direct Model Reference Adaptive Control (NDMRAC) method. This simplified model of NDMRAC is called Adaptive Output Feedback (AOF) Control. Both types of controllers have varying applications, but in this paper the AOF controller was applied to control the rigid body equations of motion. It was realized that the AOF controller could have an advantage over the NDMRAC controller in the sense of ease of implementation, and also in being better in computational expense. The adaptive method was found to perform better than the standard Full State Feed Back (FSFB) method in this application in the aspect of system response and settling time. The adaptive method was particularly better in the cases when the system being controlled was instantaneously changed during simulation. The adaptive method compensated for the system change with little too no change in trajectory or settling time whereas the FSFB method deviated in both. Control systems of some form can be found in most autonomous systems. The type of control that is used in a system varies primarily on the need for robustness or ease of implementation for the given application. For example, it can be safely concluded that your home thermostat does not need the same type of control as the auto pilot for a fighter jet. This is the reason why there are different types of controllers and many sub-variations of each type of controller. Each one although not necessarily as wide in applicability as the example given, still gives the engineer options in picking a controller that is best suited for the task at hand. With that in mind, this paper takes a look at a type of adaptive control in application to the rigid body equations of motion. The developed controller and the one it is drawn from may or may not be more suited for this particular application, but still add a new potential selection for the engineer in another application. Adaptive control methods are getting more attention since they attempt to deal with new complex control scenarios in an efficient way. Each type of adaptive controller is different in its own way, but fundamentally each one tries to compensate for a system which is dynamically changing, unknown, or even random (in stochastic applications), in an adaptive way. Within adaptive control there exist various subdivisions of the theory. The main classifications of adaptive control fall under direct adaptive control, indirect adaptive control, and robust adaptive control. The indirect adaptive method relies on approximating the plant online, which the controller then adapts too. This system can become very computationally expensive when the order of the plant gets too big in larger systems. Direct adaptive control (DAC) is different in that it requires little to no knowledge of how the plant changes with time, but instead relies on trying to adaptively track the output of a user defined reference model. Indirect adaptive control although more computationally expensive is still a major source of research and types of it can be found in application. It relies on, as mentioned before, on the online identification of the plant. The plant originally was assumed to change slowly with time, but was extended by Tsakalis and Ioannou 1 for faster changing plants, which extends its application range. Ruznik, Guez, Bar-Kana, and Steinberg

Recently, an adaptive control approach has been proposed. This approach, named L 1 adaptive control, involves the insertion of a low-pass filter at the input of the Model Reference Adaptive Control (MRAC). This controller has been designed to overcome several limitations of classical adaptive controllers such as (i) the initialization of estimated parameters, (ii) the stability problems with high adaptation gains, and (iii) the appropriate parameter excitation. In this paper, a new design of the filter is presented, used for L 1 adaptive control, for which the desired performances are guaranteed (appropriate values of the control during start-up, a high filtering of noises, a reduced time lag, and a reduced energy consumption). Parameters of the new proposed filter have been optimised by genetic algorithms. The proposed L 1 adaptive fractional control is applied to a polyarticulated robotic system. Simulation results show the efficiency of the proposed control approach with respect to the classical L 1 adaptive control in the nominal case and in the presence of a multiplicative noise.

Virtually every existing adaptive attitude control solution is based on the certainty-equivalence principle, which permits the adaptive controller structure to be based upon the deterministic feedback control algorithm (controller design based on nominal system information without any inertia-parameter uncertainty) and to be used in conjunction with a suitable adaptive parameter-estimation algorithm. However, one of the main drawbacks of the certainty-equivalence-based adaptive control methodology is the arbitrary degradation in closed-loop performance due to the adaptation (parameter-estimation) process, which acts like a forcing disturbance (uncertain parameter effect) imposed onto the deterministic closed-loop control dynamics. In this paper, we significantly deviate from the classical certainty-equivalence-based adaptive control framework and develop, for the first time (to our best knowledge), a noncertainty-equivalent adaptive attitude control algorithm. This novel control design process eliminates the deleterious performance-degradation effects of the certainty-equivalence controller through the introduction of a stable attracting manifold into the adaptation process, such that the resulting closed-loop adaptive attitude control dynamics recover the deterministic (ideal) case of closed-loop attitude controller performance (i.e., no uncertain parameter effects). In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, we present numerical simulation results that not only illustrate the various features of the new noncertainty-equivalent controller design methodology but also highlight the ensuing closed-loop-performance benefits when compared with the conventional certainty-equivalence-based adaptive control schemes.

Robust and adaptive control: fidelity or an open relationship?

Background/Objectives : In this research work, digital circuit implementation on FPGA of an adaptive feedback control methodology for a new 3 – D chaotic system is proposed Methods/Statistical analysis: The chaos synchronization is achieved using adaptive feedback control method. The new adaptive controllers are designed to achieve the chaos synchronization for the identical new chaotic system. The FPGA implementation of chaos synchronization using numerical methods induces artificial suppression in the chaotic system or chaotic behavior can be dead in very short-time. In this research work, the FPGA implementation of chaos synchronization is achieved with the help of automatic code generator like System generator in Matlab simulink. The adaptive feedback control for identical new chaotic system is coded with VHDL with 32 bit fixed point number, 12 for the entire and 20 for the fraction. Findings: In this paper, we designed a new 3D chaotic system and its chaotic behavior is verified using Lyapunov exponents, stability analysis and Poincare map. The complete synchronization for proposed chaotic system is achieved using adaptive feedback control methodology. The digital circuit realization of adaptive feedback control for the synchronization of identical chaotic system based on FPGA is achieved for the various applications of digital information systems. Simulation results and FPGA outputs illustrate the effectiveness of our proposed method. Novelty/Applications: The digital implementation of adaptive feedback control has many engineering applications such as digital data transmission, digital modulation, video encryption, digital cryptosystem etc. Keywords: Chaotic system; complete synchronization; adaptive feedback control; FPGA implementation; digital implementation

The accurate modeling of wind turbines is an extremely challenging problem due to the tremendous complexity of the machines and the turbulent and unpredictable conditions in which they operate. Adaptive control techniques are well suited to nonlinear applications, such as wind turbines, which are difficult to accurately model and which have effects from poorly known operating environments. In this paper, we extended the direct model reference adaptive control (DMRAC) approach to track a reference point and to reject persistent disturbances. This approach was then used to design an adaptive collective pitch controller for a high‐fidelity simulation of a variable‐speed horizontal axis wind turbine. The objective of the adaptive pitch controller was to regulate generator speed in Region 3 and to reject step disturbances. The control objective was accomplished by collectively pitching the turbine blades.The turbine simulation models the controls advanced research turbine (CART) of the National Renewable Energy Laboratory in Golden, Colorado. The CART is a utility‐scale wind turbine that has a well‐developed and extensively verified simulator. This novel application of adaptive control was compared in simulations with a classical proportional integrator (PI) collective pitch controller. In the simulations, the adaptive pitch controller showed improved speed regulation in Region 3 when compared with the PI pitch controller. Copyright © 2008 John Wiley & Sons, Ltd.

An adaptive inverse controller design for a micro-permanent magnet synchronous motor control system is proposed. The adaptive inverse controller is constructed by using an adaptive model and an adaptive controller. The parameters of the adaptive model and adaptive controller are on-line tuned. By using the proposed adaptive inverse controller, the transient responses, load disturbance responses and tracking responses of the control system are improved. To detect the shaft rotor position, a micro-encoder is attached with the micro-permanent magnet synchronous motor. The micro-encoder provides only 100 pulses/revolution because of its space limitation. As a result, the resolution of the position signal and speed signal is not good enough. In order to improve the resolution, a state estimator is proposed here. By using the proposed state estimator, the control system can be operated from 1 to 25 000 r/min. The adaptive inverse control algorithm and the state estimation algorithm are executed by a digital signal processor, TMS320F28335. In addition, the proposed adaptive inverse control algorithm can be applied to the position control for the micro-permanent magnet synchronous motor as well. Several experimental results validate the theoretical analysis. The experimental results show that the proposed system has good performance including transient responses, load disturbance responses, and tracking responses.