Adaptive Model-following Control Research Articles

Model following adaptive control for nodes in complex dynamical network via the state observer of links

Geometrically, a complex dynamical network (CDN) can be regarded as the interconnected system composed of the node subsystem (NS) and the link subsystem (LS) coupled with each other. Guided by this idea, in order to achieve the goal of each node following asymptotically its own reference target in a CDN, this paper investigates the model following adaptive control (MFAC) problem of NS via the dynamics of links, which implies that the LS plays the important dynamic auxiliary role in the MFAC realization of nodes. Meanwhile, we focus on the condition that the links state information is unavailable, due to sensor practical application and measurement cost constraints. To obviate this restriction, we construct the asymptotical state observer for the LS. Next, to achieve the control goal of this paper, an appropriate Lyapunov candidate function is selected, by which the MFAC scheme for NS is synthesized based on the state observer of LS. Finally, the simulation example is performed to demonstrate the theoretical results in this paper.

Model following adaptive control for the complex dynamic network with the dynamic matrix‐relation links

SummaryThis article formulates and solves a problem of model following adaptive control (MFAC) of nodes in the complex dynamic network (CDN) with the dynamic matrix‐relation links. From the perspective of composite systems, the CDN with the dynamic matrix‐relation links is considered as an interconnected system composed of intercoupling node subsystem (NS) and link subsystem (LS). Based on this idea, employing the Riccati matrix differential equation as the dynamic equation of LS, and the vector differential equation with the second derivative term as the dynamic equation of NS. Then, to remove the restriction that the state of LS is unavailable, an adaptive control scheme is proposed for NS with the help of constructed dynamic auxiliary tracking target for LS, such that the problem of MFAC of nodes is solved. Besides, it is proved mathematically that the state of NS is asymptotically tracking the state of the given reference model when the state of LS is asymptotically tracking the constructed dynamic auxiliary tracking target. Finally, the numerical simulation is given to verify the effectiveness of the theoretical results in this article.

Double Model Following Adaptive Control for a Complex Dynamical Network

This paper formulates and solves a new problem of the double model following adaptive control (MFAC) of nodes and links in a complex dynamical network (CDN). This is different from most existing studies on CDN and MFAC. Inspired by the concept of composite systems, the CDN with dynamic links is regarded as an interconnected system composed of an interconnected node group (NG) and link group (LG). Guided by the above-mentioned new idea of viewing a CDN from the perspective of composite systems, by means of Lyapunov theory and proposed related mathematical preliminaries, a new adaptive control scheme is proposed for NG. In addition, to remove the restriction that the states of links in a CDN are unavailable due to physical constraints, technical restraints, and expensive measurement costs, we synthesize the coupling term in LG with the proposed adaptive control scheme for NG, such that the problem of double MFAC of nodes and links in CDN is solved. Finally, a simulation example is presented to verify the theoretical results.

Combining Input Shaping and Adaptive Model-Following Control for Vibration Suppression

Vibration suppression in servo systems is significant in high-precision motion control. This paper describes a vibration-suppression method based on input shaping and adaptive model-following control. First, a zero vibration input shaper is used to suppress the vibration caused by an elastic load to obtain an ideal position output. Then, a configuration that combines input shaping with model-following control is developed to suppress the vibration caused by changes of system parameters. Finally, analyzing the percentage residual vibration reveals that it is effective to employ the sum of squared position error as a criterion. Additionally, a golden-section search is used to adjust the parameters of a compensator in an online fashion to adapt to the changes in the vibration frequency. A comparison with other input shaper methods shows the effectiveness and superiority of the developed method.

A new design of adaptive model following control system for lateral motion of aircraft

In this paper, a new design of an adaptive model following control (AMFC) system is proposed and analysed for a class of multi-input multi-output (MIMO) linear time-varying plants with unknown bounded parameters, and sufficiently slowly varying with time. We then show that the new approach based on combined Lyapunov method and hyper-stability criteria tries to overcome the problems of stability and zero tracking error. The main difficulty of Lyapunov methods is in finding an appropriate Lyapunov control function for the closed-loop system, therefore different choices of Lyapunov functions may result in different control structures and control performance. We show that the design of AMFC for the class of linear time-varying systems could be composed of a stable linear block in the feed forward path which turns out to be easily solvable by Lyapunov equation and a nonlinear time-varying block in the feedback path can be handled with using adaptive control techniques, such that the closed-loop dynamics of the system approaches the reference model. This structure results in greater flexibility in the choice of the adaptive laws. Finally, a typical numerical example to an aircraft control problem is analysed.

A new design of adaptive model following control system for lateral motion of aircraft

In this paper, a new design of an adaptive model following control (AMFC) system is proposed and analysed for a class of multi-input multi-output (MIMO) linear time-varying plants with unknown bounded parameters, and sufficiently slowly varying with time. We then show that the new approach based on combined Lyapunov method and hyper-stability criteria tries to overcome the problems of stability and zero tracking error. The main difficulty of Lyapunov methods is in finding an appropriate Lyapunov control function for the closed-loop system, therefore different choices of Lyapunov functions may result in different control structures and control performance. We show that the design of AMFC for the class of linear time-varying systems could be composed of a stable linear block in the feed forward path which turns out to be easily solvable by Lyapunov equation and a nonlinear time-varying block in the feedback path can be handled with using adaptive control techniques, such that the closed-loop dynamics of the system approaches the reference model. This structure results in greater flexibility in the choice of the adaptive laws. Finally, a typical numerical example to an aircraft control problem is analysed.

Artificial-Immune-System-Based Aircraft Failure Evaluation over Extended Flight Envelope

This paper describes the design, development, and flight-simulation testing of an artificial-immune-system-based approach for the evaluation of different aircraft subsystem failures/damages. The evaluation consists of the estimation of the magnitude/severity of the failure and the prediction of the achievable states, leading to an overall assessment of the effects of the failure on reducing the flight envelope. A supersonic fighter model is used, which includes model-following adaptive control laws based on nonlinear dynamic inversion and artificial neural network augmentation. Data collected from a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the proposed approach. Example results are presented for failure-magnitude evaluation and flight-envelope-reduction prediction for abnormal conditions affecting sensors, actuators, engine, and wing structure. Successful failure detection and identification are assumed before evaluation. The results show the capabilities of the artificial-immune-system-based scheme to evaluate the severity of the failure and to predict the reduction of the flight envelope in a general manner.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing “nominal model patients” (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing “realistic model patients” (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Adaptive model-following control for slim voice coil motor type optical image stabilization actuator

An autofocusing optical image stabilization actuator (AFOISA) will become standard equipment quickly in megapixel resolution mobile phone cameras (MPCs). A slim AFOISA composed of two orthogonal directions has been developed based on magnetic analysis and it is an effective solution to address image quality of a charged coupled device to compensate 10–20Hz hand jitter in taking photos. However, environmental disturbance often occur irregular shake, such as when a photographer holds a MPC while walking or in a vehicle. Therefore, this paper develops an adaptive model-following control (AMFC) law including two proportional-integral-derivative controllers with position and speed feedback control, which is shown to be able to compensate for irregular shake of MPCs and to obtain fast response. Although irregular environmental shake cause parameter variation, an adaptation mechanism synthesizes an auxiliary input signal to assure that an AFOISA plant exactly behaves as a reference model. The time response by utilizing AMFC includes 30ms settling time and 5μm steady state error without any overshoot. Furthermore, a compensatory force can quickly cancel irregular disturbance. As a result, the present slim AFOISA satisfies the optical demands of MPCs.

A Model Reference Adaptive Variable Structure Controller for Reconfigurable Flight Control Systems

This paper presents a new design of reconfigurable flight controller, applying both variable structure control and model reference adaptive control. In case of control effectors failures, aircraft model will vary, and unknown disturbances will affect the characteristics of controller. To study this problem, a model reference adaptive variable structure control law is designed, which composes a model-following adaptive control part and a variable structure control part. According to the model matching condition, an update scheme is used in the adaptive control part to eliminate system errors caused by model mismatch. With an adaptive sliding-mode gain the variable structure control part can deal with uncertain bounded disturbances of flight system. An aircraft example with stabilsator failure is presented to demonstrate the feasibility of the proposed reconfigurable control law. Simulation results show the control law is effective for reconfigurable flight control systems.

Flight data reduction methodology for performance evaluation and comparison of model-following adaptive control laws

AbstractEven small differences in atmospheric and/or flight conditions can potentially impact significantly the evaluation of the performance of the control laws and prevent a correct comparison, especially in the case of reduced size aircraft (autonomous or remotely piloted). Consistent deterministic control inputs can only be guaranteed through some form of computer-based on-board excitation system. In this paper, a methodology is proposed for flight data reduction with the purpose of accounting for non-homogeneous atmospheric conditions and inconsistent pilot inputs. The method is developed for the specific purpose of comparing model-following adaptive control laws. Performance evaluation parameters based on angular rate tracking errors are defined and used for the comparison. As a result of this approach, an additive correction is applied to the angular rate measurements to compensate for non-homogeneous turbulence effects. A multiplicative correction factor is applied to the angular rate tracking error to take into account non-identical pilot inputs. The procedure is validated with simulation and flight data obtained in the process of designing a set of fault tolerant control laws based on non-linear dynamic inversion with neural network augmentation for the reduced size WVU YF-22 aircraft model.

Model-following neuro-adaptive control design for non-square, non-affine nonlinear systems

A new model-following adaptive control design technique for a class of non-affine and non-square nonlinear systems using neural networks is proposed. An appropriate stabilising controller is assumed available for a nominal system model. This nominal controller may not be able to guarantee stability/satisfactory performance in the presence of unmodelled dynamics (neglected algebraic terms in the mathematical model) and/or parameter uncertainties present in the system model. In order to ensure stable behaviour, an online control adaptation procedure is proposed. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture matched unmodelled (neglected) dynamics or model uncertainties because of parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a desired nominal model. The neural network weight update rule is derived using Lyapunov theory, which guarantees both stability of the error dynamics (in a practical stability sense) and boundedness of the weights of the neural networks. The proposed adaptation procedure is independent of the technique used to design the nominal controller, and hence can be used in conjunction with any known control design technique. Numerical results for two challenging illustrative problems are presented, which demonstrate these features and clearly bring out the potential of the proposed approach.

Adaptive control of a nonlinear dc motor drive using recurrent neural networks

A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system.

Low power consumption focusing actuator for a mini video camera

A novel low power consumption autofocusing actuator in a mini video camera is constructed in accordance with the result of a systematic design procedure of voice coil motors (VCM). This paper emphasizes the position control of such a VCM. The position feedback signals are provided by a magnetoresistive (MR) encoder. The position estimation algorithm (PEA) is developed to precisely decode the MR signals for the position of the moving part of the VCM. Different postures change the loading of the moving part of the VCM, so that an adaptive model-following control system based on the PEA is proposed to compensate for the loading variation. The experiments verify the fast dynamic performance and high power efficiency of the VCM.

A toggle mechanism servo system with variable-structure model-following control

A toggle mechanism, which is driven by a permanent-magnet synchronous servo motor, with variable-structure model-following control (VSMFC) is proposed in this study. The rod and crank of the toggle mechanism are assumed to be rigid. The Hamilton principle and L agrange multiplier methods are applied to formulate the equation of motion. Based on the principles of the adaptive model-following control and the variable-structure control, a VSMFC system is developed to control the position of a slider of the toggle mechanism servo system. Numerical and experimental results show that the dynamic behaviour of the proposed controller-motor-mechanism system is robust to parametric variations and external disturbances.

Decentralized variable structure model-following adaptive control for interconnected systems with series nonlinearities

The problem of model-referenced control for interconnected systems with series non-linearities is investigated. By applying variable structure control theory, we propose a controller which is able to drive the error subsystem between the model state and the plant state to zero. The presented variable structure controller ensures the global reaching condition of the sliding mode of the error system. It shows that the uncertain nonlinear dynamic system also possesses the property of insensitivity to uncertainties and disturbances as a linear system does. Furthermore, the sliding mode can be designed to converge within a specified exponential speed.

Driving circuit for ultrasonic motor servo drive with variable-structure adaptive model-following control

A novel driving circuit is proposed to provide two-phase balance voltage for the travelling-wave type ultrasonic motor (USM). Moreover, the variable-structure adaptive model-following control is applied for the position control of the USM. First, the single-phase equivalent model of the USM is described. Next, the novel driving circuit, which is a two-phase chopper–inverter combination, is designed. Since the lumped dynamic model of the USM is difficult to obtain and the motor parameters are time varying, the variable-structure adaptive model-following control is applied to design the position controller of the USM to reduce the influence of parameter variations and external disturbances. The effectiveness of the proposed controller is demonstrated by experimental results.

Direct tracking control using time-optimal trajectories

Direct tracking control which is capable of high positioning speed and accuracy is developed using adaptive model-following control (AMFC). Direct tracking control relieves the difficulty and inconvenience of selecting the switching points in conventional hybrid control composed of bang-bang, velocity, and position control. AMFC can make the controlled plant follow, as closely as possible, a desired reference model where the time optimized positioning signal is generated. Direct tracking control using AMFC is applied to a tracking system of a magneto-optical disk drive to improve its slow tracking performance. Compared with the hybrid control system, the tracking time is much decreased in the direct tracking control system. According to the simulation and experimental results, an average access time as small as 19 ms is achieved.

Adaptive Servocompensation of Input Disturbances

In this paper a novel control problem of direct adaptive compensation for a class of a priori uncertain input disturbances modelled by an exosystem of the known order but with unknown parameters is posed and resolved. New model-following adaptive control systems in both the state-feed back and output-feedback form are constructed and simulated.

Adaptive control for time optimal tracking systems

To relieve the difficulty and inconvenience of selecting the switching point in time optimal control, an adaptive model following control (AMFC) is developed. The AMFC can make the controlled plant follow as closely as possible a desired reference model whose switching point can be calculated easily and accurately, ensuring that the error between the reference model and the real system approaches zero. The AMFC is applied to the tracking actuator of a magneto-optical disk drive to improve its slow tracking performance. From the simulation and experimental results, an average accessing time as small as 20 ms is obtained.