Output Feedback Controller Design Research Articles

Finite-Time Dynamic Event-Triggered Fuzzy Output Fault-Tolerant Control for Interval Type-2 Fuzzy Systems

The finite-time dynamic event-triggered fuzzy output feedback fault-tolerant control problem is studied in this article for the interval type-2 (IT2) Takagi–Sugeno fuzzy system with parameter uncertainties and actuator faults. A fuzzy state observer is first developed to solve the immeasurable state problem. Second, by using the sampled estimating states and measured output signals, a dynamic event-triggered mechanism is formulated via integrating sensor-to-observer with observer-to-controller. Third, an observer-based finite-time event-triggered fuzzy fault-tolerant controller is synthesized via the nonparallel distribution compensation design principle. Consequently, the finite-time stable conditions of the addressed IT2 fuzzy system are established by constructing an appropriate Lyapunov function. Furthermore, an output feedback control design algorithm of solving control and observer gains is given in terms of the established sufficient finite-time stable conditions. Finally, a practical example of a nonlinear tunnel diode circuit system is provided to verify the effectiveness of the proposed IT2 fuzzy control scheme.

Observer-Based Multiagent Bipartite Consensus With Deterministic Disturbances and Antagonistic Interactions.

This article studies the multiagent bipartite consensus in networks with deterministic disturbances and antagonistic interactions. An observer-based output-feedback controller design is provided to guarantee the bipartite consensus with deterministic disturbances that satisfy the matching condition. Then, by considering that the bandwidths of communication channels are limited in practical systems, the event-triggered scenario of the proposed output controller for the bipartite consensus is further studied; the node-based broadcast updating fashion is utilized and the Zeno behavior is ruled out. Simulations are also offered to support the theoretical results of protocol designs.

LMI-Based Delayed Output Feedback Controller Design for a Class of Fractional-Order Neutral-Type Delay Systems Using Guaranteed Cost Control Approach

In this research work, we deal with the stabilization of uncertain fractional-order neutral systems with delayed input. To tackle this problem, the guaranteed cost control method is considered. The purpose is to design a proportional–differential output feedback controller to obtain a satisfactory performance. The stability of the overall system is described in terms of matrix inequalities, and the corresponding analysis is performed in the perspective of Lyapunov’s theory. Two application examples verify the analytic findings.

Finite time adaptive learning for tracking control of constraint nonlinear systems via command filtered output feedback

AbstractThis article is concerned with the adaptive learning finite‐time output feedback control design for a class of high‐order nonlinear systems involving full state constraints. First, a state observer is designed to estimate the unmeasurable states and the radial basis function neural networks are used to identify unknown functions in the system. Then based on the state observer, we develop an adaptive learning finite‐time output feedback controller by combining the command filtered technique and error‐tracking method. In addition, the “explosion of complexity” problem can be avoided and the filtered errors caused by dynamic surface control can be compensated by the newly proposed command filter control. By constructing a novel log‐type barrier Lyapunov function, it can be guaranteed that the full state constraints are not violated and all the variables in the controlled systems are bounded in finite time. Finally, two simulation examples are carried out to demonstrate the effectiveness of the proposed algorithm.

Membership-function-dependent memory controller design for interval type-2 fuzzy systems under fading channels

This paper studies the problem of membership-function-dependent memory output feedback (MOF) controller design for interval type-2 (IT2) fuzzy systems under fading channels. A time-varying stochastic process is introduced to model the unpredictable channel fading phenomenon. Taking the impact of channel fading into consideration, a non-parallel distribution compensation (non-PDC) strategy is employed to construct a class of memory output feedback fuzzy controllers. By fully exploiting the membership functions, the membership-function-dependent performance analysis and controller synthesis results have been obtained, such that the closed-loop system can be guaranteed with mean-square exponential stability and performance γ. Finally, simulation studies have been presented to demonstrate the effectiveness of the proposed approach.

Observer-based Takagi–Sugeno fuzzy sampled-data dynamic positioning controller design for unmanned surface vehicle with external disturbances and actuator faults

This article investigates the problem of fuzzy sampled-data dynamic positioning output feedback controller design for unmanned surface vehicle with actuator faults and external disturbances. First, the Takagi–Sugeno fuzzy system is established by considering unknown disturbances and actuator faults. Then, a disturbance observer and an extended state observer are proposed to estimate external disturbances, unmeasured states and actuator faults, respectively. Third, based on the outputs of observers, a fuzzy sampled-data dynamic positioning controller is presented to ensure the stability of the closed-loop system. By means of linear matrix inequality technique, the stability conditions are established via the Lyapunov function method. Finally, the simulation result is shown to validate the advantages of the proposed control scheme.

Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

Quantized Output Feedback Control of AFS for Electric Vehicles With Transmission Delay and Data Dropouts

This article proposes a robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> output feedback control strategy to improve vehicle lateral stability through active front wheel steering with quantization error, transmission delay and data dropouts. Since vehicle lateral dynamics presents inherently uncertain challenges, the polytopic technique is used and the variation of tire cornering stiffness is compensated via norm-bounded uncertainty. Meanwhile, considering that the measurement outputs are inevitably suffer from network-induced constrains, i.e. the quantization error, transmission delay and data dropouts, which degrade the stability and handling performance of vehicle, a robust gain-scheduling <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> output feedback controller design strategy is proposed and the stability conditions are presented in the form of linear matrix inequalities that are derived from Lyapunov asymptotic stability and quadratic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> performance. Finally, two simulation cases are carried out with MATLAB/Simulink-Carsim to validate the effectiveness of the proposed method.

Prescribed Performance Fuzzy Adaptive Output Feedback Control for Nonlinear MIMO Systems in a Finite Time

This article studies the fuzzy adaptive output feedback control design problem for nonstrict feedback multi-input–multi-output nonlinear systems with full-states prescribed performance in finite time. Fuzzy logic systems are introduced to solve the problem of unknown nonlinear dynamics. And on this basis, a fuzzy-based state observer is designed to observe the unmeasurable state. Further, by combining the adaptive back-stepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which not only solves the computational complexity explosion problem inherent in the back-stepping control algorithm, but also improves the control performance, in contrast to the traditional DSC methods with linear filters. Besides, to further improve the tracking performance under the structure of full-states prescribed performance, a new Lyapunov function is designed considering the transform error constraint. Based on the Lyapunov theory, the stability of the closed-loop system is analyzed to ensure that all signals of the closed-loop system are semiglobal practical finite-time stability. Finally, to elaborate on the feasibility and effectiveness of the proposed control method, a simulation example is provided.

Guaranteed cost robust output feedback control design for fractional-order uncertain neutral delay systems

This paper investigates the stabilization of uncertain neutral-type delay fractional systems. Both static and dynamic output feedback control design methods are proposed. A guaranteed cost-based feedback control design is considered and closed-loop robust stability is investigated using the Lyapunov theorem. Asymptotic stability conditions of the closed-loop system is described in terms of linear matrix inequalities (LMIs). A simulation study, encompassing a numerical example of a fractional order system and a two stage chemical reactor, is considered to assess the performance and validity of the proposed control design.

Adaptive output-feedback control design for maximum power point tracking of uncertain photovoltaic systems

The problem of maximum power point tracking (MPPT) control of uncertain photovoltaic (PV) systems is addressed in this paper, on the basis of an adaptive Kalman-like observer. The system is composed of a photovoltaic generator (PVG) supplying power to a DC centrifugal pump driven by a DC–DC boost converter. The PVG is connected to the converter by a long PV cable. Indeed, many PV power plant situations require that the PV panels be installed at a great distance from the converter for several reasons including the security and the property of the site and its exposure to good daily solar irradiance. This obviously leads to the difficulty of PVG output current and voltage measurement using ordinary sensors. Such quantities measurement being in fact necessary for MPPT algorithms and controllers design. Furthermore, the specific parameters of the long PV cable used, namely its resistance and inductance, could have a significant effect on the MPPT control efficiency if instead of PVG delivered voltage and current measurements, only those accessible at the converter side of the cable are used in MPPT controllers design. Therefore, this work aims at overcoming the two aforementioned issues by proposing an adaptive output-feedback control-based MPPT for PV systems. An adaptive Kalman-like observer providing online estimates of inaccessible state variables as well as of PV cable unknown parameters is firstly designed. Then, a backstepping control law is synthesized to meet the MPPT objective. The convergence of both adaptive observer and closed-loop system control has been established using Lyapunov approach and the effectiveness of the developed adaptive MPPT controller in achieving an accurate and robust MPPT control towards uncertainties of the PV cable parameters, has been validated through numerical simulations.

Nonlinear boundary output feedback stabilization of reaction–diffusion equations

This paper studies the design of a finite-dimensional output feedback controller for the stabilization of a reaction-diffusion equation in the presence of a sector nonlinearity in the boundary input. Due to the input nonlinearity, classical approaches relying on the transfer of the control from the boundary into the domain with explicit occurrence of the time-derivative of the control cannot be applied. In this context, we first demonstrate using Lyapunov direct method how a finite-dimensional observer-based controller can be designed, without using the time derivative of the boundary input as an auxiliary command, in order to achieve the boundary stabilization of general 1-D reaction-diffusion equations with Robin boundary conditions and a measurement selected as a Dirichlet trace. We extend this approach to the case of a control applying at the boundary through a sector nonlinearity. We show from the derived stability conditions the existence of a size of the sector (in which the nonlinearity is confined) so that the stability of the closed-loop system is achieved when selecting the dimension of the observer to be large enough.

Offline output feedback robust anti-windup MPC-LPV using relaxed LMI optimization

This paper addresses a new technique of constrained output feedback robust model predictive control (RMPC) with anti-windup (AW) synthesis adopting linear parameter varying state-space (LPV-SS) systems via relaxed linear matrix inequalities (LMI) procedure. We proposed an output feedback control design with a conservatism reduction, including a robust AW-LPV synthesis to solve the robust stability under saturated signal control mode. Lyapunov stability functions through the LPV emphasis are considered. To evaluate the efficiency of the proposed method, we presented a time response analysis, closed-loop z-plane, worst-case upper bound, and we compared the performance of the proposed method with some MPC benchmarks using a numerical example.

An improved output feedback controller design for linear discrete‐time systems using a matrix decomposition method

SummaryThis paper presents the design of output feedback controllers for discrete‐time (DT) linear systems. New sufficient LMI conditions are derived for designing static and controllers using decomposition of an auxiliary matrix. The decomposition facilitates linearization of nonlinear term of reduced size to obtain linear matrix inequality criteria. This leads to less conservative results as shown in the numerical examples. In addition, the proposed static output feedback criteria is also used for designing dynamic output feedback controllers for DT systems. Furthermore, a comparative study is also made for the proposed design method with the results existing in the literature. Finally, a DT static output feedback controller is designed for a quarter‐car suspension system. Simulation results are provided to show the efficacy of the proposed design method.

Stabilization of rational nonlinear discrete-time systems by state feedback and static output feedback

The design of State Feedback (SF) and Static Output Feedback (SOF) controllers for nonlinear discrete-time systems subject to time-varying parameters is discussed in the context of Difference-Algebraic Representations (DAR) and parameter-dependent Lyapunov functions applied to obtain convex conditions in the form of Linear Matrix Inequalities (LMI). The proposed conditions guarantee the system robust stabilization and provide an estimate of the Domain-of-Attraction (DoA). Firstly, a novel strategy for gain-scheduled SF control is proposed incorporating information on the system’s nonlinearities to compute the control action. Secondly, a new gain-scheduled SOF control design solution is derived, without structural constraints imposed on the output matrix and without making use of iterative algorithms, unlike most approaches in the current literature. Finally, numerical examples illustrate the proposed methodology’s potential.

Quantized-communication-based neural network control for formation tracking of networked multiple unmanned surface vehicles without velocity information

This paper proposes a quantized communication-based output-feedback control strategy for formation tracking of networked unmanned surface vehicles (USVs) with uncertainty. Under limited network communication, it is assumed that each USV measures only the position and orientation information. In particular, this information is quantized and transmitted to USVs connected to a band-limited directed network. The primary contributions of this study are to derive distributed learning laws for neural networks using discontinuous signals and to analyze the stability of the neural network-based output-feedback control system designed in a quantized communication environment. A neural network-based local observer is developed to estimate the velocity information of each USV with model uncertainty and external disturbance. Then, a neural network-based output-feedback control design strategy using distributed and quantized postures is presented to accomplish the desired formation of networked USVs with uncertainty and underactuation. The distributed learning laws of neural networks are derived using neighbors’ quantized signals. The auxiliary signal and approach angle are employed to solve the underactuation and stability analysis problems. Despite the discontinuity of quantized signals, it is proven that all errors in the closed-loop system are bounded and can be made arbitrarily small. Finally, simulation results are given to verify the theoretical results of the proposed control system.

Pole assignment and distributed output feedback control via graph-based decomposition

ABSTRACT A new distributed observer-based output feedback control design with an arbitrary control structure is proposed. Assuming that the linear time-invariant (LTI) system does not have any fixed-modes with respect to the control structure, the pole placement at the desired locations becomes theoretically feasible. In this paper, the structural output feedback controller design is transformed into equivalent problems based on a graph-theoretic approach. Two augmented structures are established and formulated as the solution of a set of binary linear programs (BLP). The first one facilitates the incorporation of the maximum number of edges in the principal bipartite graph and the second one results in the least order of dynamic control for the principal block. Next, the distributed design problem is segmented into two stages: (i) design of a set of static control loops based on convex optimisation, to maximise the controllability/observability radius and (ii) design of the control loops corresponding to the principal bipartite graph based on the reduced-order observer-based control. The performance of the proposed approach is investigated in terms of the pole-placement accuracy, control effort, robustness to perturbations in system dynamics, and effect of controller order reduction.

ℓ₂-ℓ∞ Control of Discrete-Time State-Delay Interval Type-2 Fuzzy Systems via Dynamic Output Feedback.

This article investigates the design of the l2-l∞ dynamic output-feedback (DOF) controller for interval type-2 (IT2) T-S fuzzy systems with state delay. For nonlinear systems, the IT2 fuzzy model is an efficient modeling method which can better express uncertainties than the (type-1) fuzzy model. In addition, state delay is also a general factor that affects system performance. After analyzing the stability of the system, based on convex linearization and the projection theorem, this article proposes a delay-dependent output-feedback controller design method. The IT2 membership functions (MFs) of the fuzzy controller are chosen to be different from those of the model so as to increase the freedom of controller selection. A membership-function-dependent (MFD) method based on the staircase MFs is applied to relax the stability analysis results. Finally, a numerical simulation example is given to illustrate the effectiveness of the results.

Membership-Function-Dependent Design of $L_1$-Gain Output-Feedback Controller for Stabilization of Positive Polynomial Fuzzy Systems

This article presents the <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula>-gain polynomial fuzzy output-feedback controller design and the stability analysis using the sum-of-squares (SOS) approach for positive polynomial fuzzy-model-based (PPFMB) control systems. The polynomials, positivity, and optimal <inline-formula><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> performance make some existing convex methods for general systems inapplicable. To overcome this problem, first, an augmented system of the PPFMB control system is constructed; then, by introducing some constraint conditions and mathematical techniques, nonconvex stability and positivity conditions are skillfully transformed into convex ones simultaneously. In addition, to control the systems flexibly and lower the implementation cost, the imperfect premise matching concept is taken into account for controller design. Besides, the high-degree-polynomial approximation method is adopted to conduct stability and positivity analysis by incorporating the information of membership functions and the boundary information of the state variables. On the basis of the Lyapunov stability theory, the relaxed stability and positivity conditions in terms of the SOS form are obtained. Finally, two simulation examples are presented to verify the feasibility of the theoretical results.

Fuzzy dynamic output feedback control for nonlinear networked multirate sampled-data systems: An integral inequality method

This paper is devoted to fuzzy dynamic output feedback control for nonlinear networked multirate sampled-data systems based on T-S fuzzy systems. Using asynchronous multirate sampled-data, a multirate fuzzy controller is designed. The resultant closed-loop system is modeled as a perturbed system with two perturbed terms which include multirate sampling and the asynchronous membership functions between the controlled plant and the controller. A novel refined integral inequality is developed, and it improves Jensen's inequality and extends Wirtinger's inequalities by considering the sawtooth structure of time-varying delays. By applying the inequality to the perturbed system and constructing a new storage function, a stability criterion and two fuzzy dynamic output feedback controller design methods are presented for the nonlinear networked multirate sampled-data systems. Finally, three illustrative examples show the effectiveness of the proposed method.