Control Design Issues Research Articles

Output feedback control for non-strict feedback nonlinear systems: an adaptive fuzzy backstepping scheme

The adaptive fuzzy tracking control design issue is considered in this paper for a class of non-strict feedback nonlinear systems subject to external disturbances. A fuzzy high-gain state observer is constructed to reconstruct the unmeasurable states of the system by leveraging the universal approximation property of fuzzy logic systems for arbitrary unknown nonlinear functions. Within the framework of backstepping control design and in conjunction with adaptive techniques, an adaptive fuzzy control approach is presented. Subsequently, to work out the inherent ‘complexity explosion’ problem of the proposed control approach, dynamic surface control technique is introduced, leading to a simplified adaptive fuzzy output feedback control scheme. Stability analysis shows that the two proposed control schemes can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, meanwhile the system tracking error can converge to a small neighborhood around the origin. Ultimately, a numerical simulation example is provided to validate the feasibility of the proposed control scheme.

Robust State Feedback Control with D-Admissible Assurance for Uncertain Discrete Singular Systems

This study addresses the state feedback control associated with D-admissible assurance for discrete singular systems subjected to parameter uncertainties in both the difference term and system matrices. Firstly, a refined analysis criterion of D-admissible assurance is presented, where the distinct form embraces multiple slack matrices and has lessened linear matrix inequalities (LMIs) constraints, which may be beneficial for reducing the conservatism of admissibility analysis. In consequence, by hiring the state feedback control, controller design issues with pole locations, which directly dominate the system performance, are mainly treated. For all the presented criteria can be formulated by the strict LMIs, they are thus suitably solved via current LMI solvers to conduct a state feedback controller with specific poles’ locations of system’s performance requirements. Finally, two numerical examples illustrate that the presented results are efficient and practicable.

Event‐triggered adaptive neural‐network control of nonlinear MIMO systems

SummaryThis article investigates an adaptive neural networks (NNs) tracking control design issue for nonlinear multi‐input and multi‐output (MIMO) systems involving the sensor‐to‐controller event‐triggered mechanism (ETM). In the design, NNs are utilized to approximate the unknown nonlinear functions. A sensor‐to‐controller ETM is designed to save unnecessary transmission and communication resources. Subsequently, a first‐order filter technique is presented to solve the problem that the virtual control function is not differentiable. Furthermore, an event‐triggered adaptive NNs control strategy is presented by constructing Lyapunov functions and using adaptive backstepping recursive design. It is demonstrated that the presented scheme can ensure the whole closed‐loop signals are uniformly ultimately bounded without exhibiting the Zeno behavior. Finally, a numerical simulation example confirms the effectiveness of the presented adaptive event‐triggered control (ETC) approach.

Estimator-based adaptive prescribed performance cooperative bipartite containment control of nonlinear multiagent system against DoS attacks

This paper studies the prescribed performance bipartite containment control design issue for nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks. First, an attack compensator is built for each agent in order to obtain the system output when the DoS attacks appear. Then, a fuzzy estimator is developed to estimate the unknown states based on the attack compensator. Second, a prescribed performance control (PPC) approach is utilized to ensure the bipartite containment error within a preassigned boundary, and the restriction related to the initial condition is removed. Finally, a set of switching laws is designed by combining the average dwell time method with the limited conditions of DoS attacks. Under this framework, an appealing feature of the designed controller is that the output signals of followers converge to a convex hull formed by leaders and all the signals in the closed-loop system remain bounded. A simulation example is given to demonstrate the effectiveness of the control strategy.

Cross-partisan discussions reduced political polarization between UK voters, but less so when they disagreed

Can brief, unmoderated cross-partisan discussions reduce political polarization, even when partisans disagree on the issue under discussion? This article reports results from an experiment that matched 582 UK Labour and Conservative party voters for a ten-minute, unmoderated chat discussion about a contentious issue in a wait-list control design. Issue disagreement between discussing partisans randomly varied, and was visible throughout the discussion. Results show that after the discussion, out-partisan sympathy and willingness to have cross-partisan discussions increased. There was no statistically significant effect on opinions. The effect on sympathy was lower when partisans’ issue opinions were further apart. Treatment effects correlate with reported experiences of perspective-getting, inclusion in the discussion, commonality and reduced misperceptions. Conservative respondents about to discuss immigration softened their views just prior to the discussion.

Fuzzy Finite-Time ${H_\infty }$ Hybrid-Triggered Dynamic Positioning Control of Nonlinear Unmanned Marine Vehicles Under Cyber-Attacks

This paper studies the fuzzy finite-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${H_\infty }$</tex-math></inline-formula> hybrid-triggered dynamic positioning (DP) control design issue of the nonlinear unmanned marine vehicles against cyber-attacks and ocean disturbances. The motion dynamics are firstly represented by the Takagi-Sugeno (T-S) fuzzy DP models. Secondly, for the purpose of obtaining the surge position, sway position, yaw angle and velocity of unmanned marine vehicles, a fuzzy state observer is proposed to estimate them. Then, a hybrid-triggered mechanism described by a Bernoulli variable is designed to save the communication resources. To overcome the impacts of cyber-attacks and ocean disturbances, a fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${H_\infty }$</tex-math></inline-formula> hybrid-triggered DP output feedback controller based on finite-time theory is developed. The stability criterions of the considered systems are formulated through establishing finite-time Lyapunov-Krasovskii functional. Finally, the simulation and comparison results validate the feasibility and superiority of the proposed scheme.

Impact of Discrete-Time Modeling on Dual Input Buck-SEPIC Converter

This paper addresses aspects of a two-input Buck-SEPIC dc-dc converter. This integrated DC-DC converter processes power from two sources, two switches, and four different energy storage elements. The designed converter is processing 48 volts from two sources Vg1 = 36 V and Vg2 = 60 V. The transfer function modeling of this converter plays an important role in addressing several crucial aspects like optimal parameter design, controller design, stability, and robustness issues. Here, two types of transfer function modeling aspects are considered: continuous-time and discrete-time. The discrete-time transfer function is derived by considering trailing-edge as well as leading-edge digital pulse-width modulation scheme (DPWM) and for each of these cases time-delays in the control loop are also included. The designed converter parameters L1, L2, C1, and C2 are based on the design equations. The transfer functions are obtained in continuous-time and discrete-time for the TI-BS converter in the MATLAB environment. The experimental validation of the TI-BS dc-dc converter is performed through Hardware in Loop (HIL) using the real-time environment of the OPAL RT. For TIBS converter polezero configurations and frequency response characteristics are plotted. Using these plots important characteristics related to the deviation in phase angle of frequency response at higher frequencies due to RHP zeros are observed. The simulation studies are performed considering a 36 V / 60 V to 48 V, 500 W, prototype, DC power distribution system. The detailed modeling aspects in continuous-time as well as in discrete time are discussed considering a two-input Buck-SEPIC converter. The mathematical derivations of four different transfer function matrices using discrete-time modeling (trailing-edge and leading-edge modulation) are discussed in detail along with timing diagrams considering interval-1 and interval-2 sampling. The frequency response of the TI-BS dc-dc converter is affected when it is modeled using the discrete-time models and such frequency response deviation in phase response is observed for the TI-BS converter. The nonminimum phase response of this converter is shown using the Bode frequency response in the MATLAB environment and verified using the pole-zero map.

Robust control scheme for uncertain semi‐Markov jump systems subject to digital communication constraint and incomplete sojourn‐time information

AbstractThis paper deals with the robust control problem for networked uncertain semi‐Markov jump systems over digital communication channel, where some complicated factors are considered in this framework, such as false data injection attacks, incomplete sojourn‐time information, and actuator fault. In network environment, these problems frequently occurred: (1) Data must be quantified before it is transmitted via digital communication channels, and (2) there is always the risk of cyber‐attack. Traditional controllers are not effective to solve the robust control design issue of uncertain semi‐Markov jump systems since the signal quantization and cyber‐attack will degrade control performance evidently. Then, a resilient controller is designed to guarantee the stable running of system with incomplete sojourn‐time information and the system resiliency to unsecure shared data. With the help of Lyapunov stability theory, sufficient conditions are derived in the form of linear matrix inequality, which can determine the gains of the resilient controller. Finally, the simulation examples about single‐machine infinite‐bus power system are given to explain the effectiveness and feasibility of the proposed control approach.

Finite-Time Control of Markov Jump Lur'e Systems With Singular Perturbations

This paper investigates the asynchronous controller design issue for Markov jump Lur'e systems with singular perturbations within a fixed time interval. For broader practical applications, the variation of system modes is regulated by a switched nonhomogeneous Markov process that corresponded to both lower-level nonhomogeneous stochastic jumping and higher-level deterministic switching. By resorting to the hidden nonhomogeneous-Markov model, an asynchronous control law that relies on the detected mode information is proposed. In light of the mode-dependent average dwell time strategy and mode-dependent stochastic system theory, the singularly perturbed parameter-independent conditions are attained to ensure the stochastic finite-time boundedness of the resulting systems. With respect to a linear matrix inequality optimization problem, a solution to the maximum singular perturbation parameter is obtained. Finally, the feasibility and applicability of the devised theoretical results are verified by simulation examples.

Power system frequency profile improvement using satin bowerbird optimized tilt fractional cascade controller with reduced data based cost function

The objectives of the automatic generation control (AGC) are attained via secondary control mechanism with optimal controller gains. The best solutions of these gains are identified by population search-based algorithms with the help of performance metrices. The controller design issues of AGC treat these performance metrics as a cost function. This paper introduces a new performance measure implemented with dominated samples information extracted from the frequency and tie line power changes. This new integral square error (NISE) is incorporated in fitness function and tested on isolated and multi area interconnected power system scenarios with satin bowerbird optimizer to tune the controller gains. Additionally, integral-tilt proportional-fractional order derivative controller is proposed in this paper and comparisons are made with popular controllers to show the merits in terms of AGC specifications. Optimal gain parameters of all controllers are achieved with existing integral square error and the proposed NISE cost functions and extensive simulations are carried out to illustrate the merits of the work. Studies are also expanded to nonlinear systems in order to evaluate the adaptability, performance improvement, and effectiveness of the suggested cost function, controller, and tuning method.

Boundary Fuzzy Output Tracking Control of Nonlinear Parabolic Infinite-Dimensional Dynamic Systems: Application to Cooling Process in Hot Strip Mills

This paper utilizes a combination of integral control, fuzzy control, and observer-based output feedback control to deal with the issue of nonlinear output tracking control (OTC) design for nonlinear infinite-dimensional dynamic systems. The system dynamics model is represented by a semi-linear parabolic partial differential equation (PDE) with boundary control and non-collocated boundary measurement. Initially, a Takagi-Sugeno (T-S) fuzzy parabolic PDE model is constructed to surmount the OTC design difficulty from the infinite-dimensional nonlinear system dynamics. Subsequently, a fuzzy-observer-based OTC law is proposed via the T-S fuzzy PDE model and the integral control approach. Here, the integral control ensures asymptotic output regulation, and the observer-based output feedback control is employed to conquer the stabilizing control design difficulty caused by the non-collocation between control actuation and measurement. It is shown via the Lyapunov technique with variants of vector-valued Poincaré-Wirtinger's inequality that the suggested fuzzy OTC law drives the measurement output to asymptotically track the desired reference signal and ensures the boundedness of the resulting closed-loop system, provided that a sufficient condition given in the form of linear matrix inequalities is fulfilled. Moreover, the proposed fuzzy-model-based OTC design is also revised for the exponential stabilization case. Finally, extensive simulation results for a numerical example and a cooling process in hot strip mills are provided to examine the effectiveness and merit of the proposed fuzzy OTC scheme.

Flight and Vibration Control of Flexible Air-Breathing Hypersonic Vehicles Under Actuator Faults.

The issue of modeling and fault-tolerant control (FTC) design for a class of flexible air-breathing hypersonic vehicles (FAHVs) with actuator faults is investigated in this article. Different from previous research, the shear deformation of the fuselage is considered, and an ordinary differential equations-partial differential equations (ODEs-PDEs) coupled model is established for the FAHVs. A feedback control is proposed to ensure flight stable and an adaptive FTC method is designed to deal with actuator faults while suppressing the system's vibrations. Besides, the stability analysis of the closed-loop system is given via the Lyapunov direct method and an algorithm that transfers the bilinear matrix inequalities (BMIs) feasibility problem to the linear matrix inequalities (LMIs) feasibility problem is provided for determining the control gains. Finally, the numerical simulation results show that the proposed controller can stabilize the flight states and suppresses the vibration of the fuselage efficiently.

A Novel Full Errors Fixed-Time Control for Constraint Nonlinear Systems

In this paper, the issue of adaptive control design with full error constraints for multi-input and multi-output (MIMO) nonlinear systems is investigated. By combining nonlinear filters and an adaptive back-stepping control scheme, a novel dynamic surface control (DSC) scheme is presented. Different from the DSC scheme with the traditional linear filter, the proposed DSC not only solves the inherent problem of computational complexity explosion, but also enhances the control property. In addition, in order to further enhance the tracking performance understructure the fixed-time prescribed performance, a novel barrier Lyapunov function is proposed by considering the transform error constraint based on the fixed-time prescribed performance functions (FTPPF). The design control scheme can ensure that all the dynamic errors can be converged in predefined bounds within a fixed time. By choosing the barrier Lyapunov function, the stability of the closed-loop system is proof. Finally, the validity and innovation of the proposed strategy are verified by numerical practical simulation examples.

Indirect adaptive neural tracking control of USVs under injection and deception attacks

This paper investigates the tracking control issue of unmanned surface vessels (USVs) under internal/external uncertainties as well as injection and deception attacks, and proposes a novel adaptive neural network tracking control solution. In this work, the backstepping design framework is used to solve the control design issue. To resolve the time-varying gain caused by the injection and deception attacks, indirect adaptive neural and single-parameter learning approaches are employed. In the kinematic channel, internal and external uncertainties as well as the additional terms caused by the injection and deception attacks are regarded as heterogeneous uncertainties. And then, the heterogeneous uncertain term is transformed into a linearly parameterized form with only one parameter with the aid of the neural network technique and single-parameter learning approach. Furthermore, an adaptive law is constructed to update the unknown parameter, and an indirect adaptive neural learning approach is developed. Finally, a novel indirect adaptive neural tracking control solution is proposed to implement the suppression of heterogeneous uncertainties including internal and external uncertainties as well as attacks. The theoretical analysis indicates that all signals in the closed-loop tracking control system of USVs are bounded under the presented scheme. Simulation results demonstrate the effectiveness of the proposed scheme.

Analysis and Design of Secure Sampled-Data Control Subject to Denial-of-Service Attacks

This study addresses the issue of secure control design for cyber-physical systems (CPS) against denial of service (DoS) attacks. We take into account a continuous-time linear system with a convex quadratic performance measure and a sampled linear state feedback control. DoS attacks impose constraints on the CPS, where packets may be jammed between the sensor and controller by a malicious entity, potentially resulting in system instability and performance degradation. We assume that the attacker can perform DoS attacks with a limited time and frequency due to energy restrictions. We devise an efficient procedure using the linear matrix inequality approach to compute an upper bound on the performance degradation brought on by the DoS attack. We also propose a redesign of the controller to minimize this performance degradation. Finally, a simulation example illustrates the computation of the performance degradation under a bounded DoS attack and the design of a secure controller. Simulation results show that the designed controller effectively keeps the feedback loop’s performance and stability under attack.

Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Model predictive control and linear control of DC–DC boost converter in low voltage DC microgrid: An experimental comparative study

It is challenging to select the best control strategy to regulate the output voltage of a DC–DC boost converter in DC MicroGrids (MGs). Indeed, the controller must cope with the standard requirements: robustness, steady-state and dynamic performance. Moreover, it also needs to handle the boost converter’s intrinsic Non-Minimum Phase behavior that significantly increases the difficulty from the control point of view. This paper compares four control methods (Linear Controller-based Voltage-Mode Control, Linear Controller-based Averaged Current-Mode Control, Finite Control Set Model Predictive Controller-based Voltage-Mode Control, and Finite Control Set Model Predictive Controller-based Current-Mode Control) based on experimental results. To achieve a fair comparison of the different control methods, an optimal design of the power converter parameters is proposed to minimize their influence on the controller’s performance. It provides some guidelines to select the appropriate controller. Several tests are performed on the experimental setup to evaluate the four control strategies, considering the features of DC MGs. Qualitative and quantitative comparisons are made regarding converter models, controller design, performance evaluation and implementation issues. The experimental results show that Finite Control Set Model Predictive Controller-based Current-Mode Control and Linear Controller-based Averaged Current-Mode Control are the best compromises.

Control for systems with prescribed performance guarantees: An alternative interval theory-based approach

This paper investigates the issue of control design for a class of nonlinear systems with guaranteed prescribed performance. Different from the existing prescribed performance control approaches, a switching control scheme is introduced to ensure that the errors do not violate the performance bound functions. Bounded stability is proved by means of the cooperative systems theory. It is shown that the proposed control paradigm establishes a unified prescribed performance control framework which covers bounded, asymptotic, finite and fixed-time stabilities, only by assigning adequately the performance bound functions. An inverted pendulum example demonstrates the effectiveness of the proposed approach.

Fixed-Time Filtered Adaptive Parameter Estimation and Attitude Control for Quadrotor UAVs

In this article, a fixed-time filtered adaptive parameter estimation and control scheme is proposed for attitude tracking of quadrotor unmanned aerial vehicles. A nonsingular fixed-time sliding mode surface is constructed to avoid the existence of the singularity issue resulted from the differentiation of the sliding mode variable. Through designing auxiliary filtered matrices, a fixed-time parameter update law is developed to achieve the fixed-time parameter convergence. Then, an adaptive neural controller is designed to ensure the fixed-time convergence of attitude tracking errors in the presence of the model uncertainties. Instead of using any piecewise continuous functions, the possible singularity issue in the conventional fixed-time controller design can be overcome by constructing auxiliary functions, and thus the fixed time stability analysis becomes more concise. The efficiency of the presented scheme is validated through experiments on a practical Quanser quadrotor platform.

Admissibility and admissibilization for T–S fuzzy descriptor systems with partially unknown membership functions

In this paper, the admissibility analysis and controller design issues for a class of nonlinear descriptor systems described by the T–S descriptor fuzzy model with partially unknown membership functions are discussed. New sufficient conditions are first given to ensure the admissibility of such fuzzy descriptor systems. To fully utilise the information of membership functions, the known and unknown parts of membership functions are handled by staircase functions and the bounds of the unknown part, respectively. Different from the traditional parallel distribution compensation (PDC) method, the state-feedback controller design condition is given under an imperfect premise matching method via linear matrix inequalities (LMIs). Some examples are given to show the validity of the proposed methods.