Output Feedback For Nonlinear Systems Research Articles

Adaptive quantized control based on output feedback for nonlinear systems with sensor faults under intermittent DoS attacks

AbstractQuantized signal control has been widely recognized as an effective means for conserving communication resources. However, ensuring stability of quantization‐driven nonlinear systems becomes particularly challenging in the presence of intermittent denial‐of‐service (DoS) attacks and sensor failures. This article explores the stable control problem of quantized nonlinear systems subject to DoS attacks. When an attack exists, all signals are unmeasurable. During the dormant phase of a DoS attack, only the quantized output signal is measurable, while there is a deviation between the received signal and the ideal signal due to sensor failure. To address these challenges, a switch‐type quantized observer is designed in this article to estimate unmeasurable state signals. In addition, we integrate the first‐order dynamic filter into the back‐stepping design process, which not only avoid the obstacles arisen from non‐differentiable output signals, but also simplifies the design process. Furthermore, this article adopts a more flexible sector quantizer and derives a set of adaptive laws to compensate for unknown quantization parameters. It is shown that, by utilizing the estimated signal, the proposed quantized adaptive control is able to effectively fight against DoS attacks and also is fault‐tolerant to sensor failures, guaranteeing semi‐globally uniformly ultimately bounded for all closed‐loop signals. Finally, simulation results validate the effectiveness of the proposed theory.

Output Feedback for Nonlinear Systems With Nonlinear and Discontinuous Inputs

The paper aims to design a high-gain observer for nonlinear systems with nonlinear and discontinuous sensor signal. The ultimate boundedness and exponential stability of the estimation error of high-gain observer are presented. The performance recovery property of output feedback control using high-gain observer is demonstrated. Simulation examples are illustrated to validate the proposed results of this paper.

Predictor-based observer and controller for nonlinear systems with input/output delays

This letter addresses the problem of asymptotic stabilization by output feedback for nonlinear systems with delays in the input and output. Under stabilizability and detectability of the linearized time-delay system, we show that asymptotic stabilization is solvable by predictor-based nonlinear observer and dynamic output compensator. The proof is based on the linearization method, the design of local nonlinear observers and observer-based controllers, as well as the Razumikhin theorem.

High-Gain-Observer-Based Predictive Output Feedback for Nonlinear Systems With Large Input-Delays

Abstract The predictor feedback has been demonstrated to be quite effective in large delay compensation. However, few researches in the field of predictor feedback for large delays focused on output feedback control (OFC). This paper develops the previous work to design high-gain-observer-based predictive output feedback for nonlinear systems with large delays. Two methods are employed for large delay compensation: the backstepping-based partial differential equation (PDE) method and the reduction-based ordinary differential equation (ODE) method. It appears that, for continuous-time control, the first method leads to simpler linear matrix inequality (LMI) conditions and deal with larger delays, whereas the second method is easily exploited for sampled-data implementation under continuous-time measurement. Lyapunov–Krasovskii method is presented to guarantee the exponential stability of the nonlinear systems under predictor-based controllers. Through a simulation example of pendulum, the proposed methods are demonstrated to be efficient when the input delays are too large for the system to be stabilized without a predictor.

Global stabilization via adaptive event-triggered output feedback for nonlinear systems with unknown measurement sensitivity

Global stabilization via adaptive event-triggered output feedback for nonlinear systems with unknown measurement sensitivity

Finite-time adaptive dynamic surface control for output feedback nonlinear systems with unmodeled dynamics and quantized input delays

<p>We delved into a category of output feedback nonlinear systems that are distinguished by unmodeled dynamics, quantized input delays, and dynamic uncertainties. We introduce a novel finite-time adaptive dynamic surface control scheme developed through the construction of a first-order nonlinear filter. This approach integrates Young's inequality with neural network technologies. Then, to address unmodeled dynamics, the scheme incorporates a dynamic signal and utilizes Radial Basis Function (RBF) neural networks to approximate unknown smooth functions. Furthermore, an auxiliary function is devised to mitigate the impact of input quantization delays on the system's performance. The new controller design is both simple and effective, addressing the "hasingularity" problems typically associated with traditional finite-time controls. Theoretical analyses and simulation outcomes confirm the effectiveness of this approach, guaranteeing that all signals in the system are confined within a finite period.</p>

Distributed adaptive leader-following consensus control for a class of non-linear output feedback systems

Abstract This paper deals with the leader-following consensus control for a class of parametric output feedback non-linear multi-agent systems. To design distributed control laws without using agent’s own out information, non-linear functions of agent’s own output are transformed into non-linear functions of relative output information using mean value theorem and variable separation technique. By introducing an input-driven filter and employing adaptive backstepping method, distributed adaptive non-linear control laws are designed using only relative output measurements. The proposed control law is also independent of the Laplacian matrix. Therefore, it can solve the leader-following consensus problem for any directed communication graph that contains a spanning tree with the root node being the leader agent. A numerical example is given to illustrate the effectiveness of the proposed scheme.

Reduced-Order Filters-Based Adaptive Backstepping Control for Perturbed Nonlinear Systems.

In this article, a robust adaptive output-feedback control approach is presented for a class of nonlinear output-feedback systems with parameter uncertainties and time-varying bounded disturbances. A reduced-order filter driven by control input is proposed to reconstruct unmeasured states. The state estimation error is shown to be bounded by dynamic signals driven by system output. The bound estimation technique is employed to estimate the unknown disturbance bound. Based on the backstepping design with three sets of tuning functions, an adaptive output-feedback control scheme with the flat-zone modification is proposed. It is shown that all the signals in the resulting closed-loop adaptive control systems are bounded, and the output tracking error converges to a prespecified small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and validity of the proposed approach.

Adaptive Event-Triggered Output Feedback for Nonlinear Systems With Unknown Polynomial-of-Output Growth Rate

This paper investigates global stabilization via adaptive event-triggered output feedback for a class of uncertain nonlinear systems. Typically, unknown polynomial-function rate is admitted in the unmeasurable-state dependent growth of the systems. This calls for an advanced compensation strategy based on dynamic high gain, which in turn requires more intelligent execution in the event-triggered control architecture. To this end, a novel event-triggering mechanism is designed with two events separately evaluating the behaviors of dynamic gain and the controller signal. Particularly, the event on controller signal is enforced to suspend for a certain time after each execution to guarantee a positive lower bound for the inter-execution intervals. More importantly, the suspension time and the threshold therein are both online adjusted according to dynamic gain (rather than pre-specified), which could become small enough as the dynamic gain increases. This ensures timely execution for the effectiveness of adaptive compensation. Then, with the dynamic gain delicately designed to counteract the influence of the execution error, an event-triggered controller via adaptive output feedback is proposed to make the original system states and observer states converge to zero. Further attempt is performed for more efficient resource saving and disturbance tolerance.

Fault-Tolerant Optimal Control for Discrete-Time Nonlinear System Subjected to Input Saturation: A Dynamic Event-Triggered Approach.

This paper investigates the dynamic event-triggered fault-tolerant optimal control strategy for a class of output feedback nonlinear discrete-time systems subject to actuator faults and input saturations. To save the communication resources between the sensor and the controller, the so-called dynamic event-triggered mechanism is adopted to schedule the measurement signal. A neural network-based observer is first designed to provide both the system states and fault information. Then, with consideration of the actuator saturation phenomenon, the adaptive dynamic programming (ADP) algorithm is designed based on the estimates provided by the observer. To reduce the computational burden, the optimal control strategy is implemented via the single network adaptive critic architecture. The sufficient conditions are provided to guarantee the boundedness of the overall closed-loop systems. Finally, the numerical simulations on a two-link flexible manipulator system are provided to verify the validity of the proposed control strategy.

Adaptive output regulation of a class of nonlinear output feedback systems with unknown high frequency gain

This paper presents an output feedback design approach based on the adaptive control scheme developed for nonlinearly parameterized systems, to achieve global output regulation for a class of nonlinear systems in output feedback form. We solve the output regulation problem without the knowledge of the sign and the value of the high frequency gain a priori. It is not necessary to have both the limiting assumptions that the exogenous signal w and the unknown parameter μ belong to a prior known compact set and the high frequency gain has a determinate lower and upper bounds. The effectiveness of the proposed algorithm is shown with the help of an example.

Adaptive neural network control for nonlinear output-feedback systems under disturbances with unknown bounds

Abstract This paper is concerned with the problem of adaptive backstepping neural network tracking control for a class of output feedback systems with unknown functions under bounded disturbances whose boundaries is unknown. Unknown functions are approximated via online radial basis function (RBF) neural network, high order continuous differentiable functions are introduced into Lyapunov function to realize the estimation of unknown parameters and unknown boundary, and a new dead zone function is designed to replace symbolic function to realize the continuity of virtual control. During the design process, the backstepping design method is applied to deal with the cross terms generated by the tuning function. Barbalat’s lemma proves that all the signals of closed-loop system are bounded and the output tracking error converges to an arbitrarily small neighborhood of the origin. A simulation example are given to illustrate the effectiveness of the control scheme.

Adaptive Quantized Control of Output Feedback Nonlinear Systems With Input Unmodeled Dynamics Based on Backstepping and Small-Gain Method

In this article, an adaptive quantized neural backstepping strategy is investigated for a class of nonlinear systems with input unmodeled dynamics and output constraints based on the small-gain method. A challenge lies in the considered input-quantized actuator possessing both unknown control gain and input unmodeled dynamics, and the application of the small-gain theorem when the system possesses input unmodeled dynamics and the output constraints. By the coordinate transformation of the state variables, the input unmodeled dynamics subsystem is transformed into a suitable form for applying the small-gain theorem. By a logarithmic one to one mapping, the time-varying output constraints are tackled. With these methods, the stability proof based on the small-gain theorem is completed. It is shown that all the signals are bounded, and the output signal is constrained within the preset range.

Cooperative Output Regulation Problem of Nonlinear Multiagent Systems With Proximity Graph via Output Feedback Control.

This article considers the cooperative output regulation problem of nonlinear output feedback systems under the communication network modeled by the proximity graph, which is time varying and state dependent. Under the relaxed assumption that the proximity graph is initially connected, based on an improved potential function, we first propose a distributed connectivity-preserving output feedback control law with a linear internal model and distributed observer, which is robust to uncertain parameter and external disturbances in heterogeneous subsystems with strong nonlinearity. Successively, an adaptive design with parameter update law is derived to further tolerate an uncertain parameter in the exosystem, which generates the leader's trajectory and external disturbances.

Knowledge-based reinforcement learning controller with fuzzy-rule network: experimental validation

A model-free controller for a general class of output feedback nonlinear discrete-time systems is established by action-critic networks and reinforcement learning with human knowledge based on IF–THEN rules. The action network is designed by a single input fuzzy-rules emulated network with the set of IF–THEN rules utilized by the relation between control effort and plant’s output such as IF the output is high THEN the control effort should be reduced. The critic network is constructed by a multi-input FREN (MiFREN) for estimating an unknown long-term cost function. The set of IF–THEN rules for MiFREN is defined by the general knowledge of optimization such that IF the quadratic values of control effort and tracking error are high THEN the cost function should be high. The convergence of tracking error and bounded external signals can be guaranteed by Lyapunov direct method under general assumptions which are reasonable for practical plants. A computer simulation system is firstly provided to demonstrate the design method and the performance of the proposed controller. Furthermore, an experimental system with the prototype of DC-motor current control is conducted to show the effectiveness of the control scheme.

Universal output feedback adaptive control with uncertain nonlinearity and unknown high‐frequency gain sign: A new design

SummaryIn this paper, we propose a new universal output feedback adaptive controller to globally (or semiglobally) stabilize nonlinear output feedback systems, whose nonlinearities are bounded either by known functions with unknown parameters or by completely unknown functions. In addition, no a priori knowledge of the sign of high‐frequency gain is required. The new design focuses on properly arranging the control gains step by step in the filter backstepping design. Instead of Lyapunov‐based argument, an inductive contradiction argument is employed in the proof of stability, which is not common in literature.

Adaptive fuzzy output feedback control for nonlinear systems based on event-triggered mechanism

Approaches of considering event-triggered fuzzy control for nonlinear output feedback systems are restricted to a conservative result that only a tunable unknown tracking error is obtained. This work proposes a new fuzzy control strategy to remove this restriction. Technically, a new idea contributes to the design of the Lyapunov function, which is constructed with a series of smooth functions rather than chosen in a quadratic form as regular. With this idea, a new direct adaptive fuzzy controller is then developed to avoid the possible chattering phenomenon in addition to achieve the asymptotic stability. Besides the chattering-free property, the proposed scheme is performance-oriented in the sense that the relationship between the design parameters and tracking performance is established in an explicit way, which allows the users to make design decisions accordingly. Through Lyapunov analysis, it is proven that besides the system stability, the tracking error will ultimately approach to a user-defined interval. Simulation studies are performed to demonstrate the theoretical results.

Adaptive control of output feedback nonlinear systems with unmodeled dynamics and output constraint

This paper is concerned with the adaptive control problem of a class of output feedback nonlinear systems with unmodeled dynamics and output constraint. Two dynamic surface control design approaches based on integral barrier Lyapunov function are proposed to design controller ensuring both desired tracking performance and constraint satisfaction. The radial basis function neural networks are utilized to approximate unknown nonlinear continuous functions. K-filters and dynamic signal are introduced to estimate the unmeasured states and deal with the dynamic uncertainties, respectively. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded, while the output constraint is never violated. Simulation results demonstrate the effectiveness of the proposed approaches.

Adaptive quantised observer‐based output feedback control for non‐linear systems with input and output quantisation

This study investigates the case of simultaneous input and output quantisation for a class of non-linear output feedback systems subject to uncertain non-linear dynamics and non-zero initial states. An adaptive quantised observer-based output feedback controller is designed to guarantee bounded stability and H∞ performance despite input and output quantisation. In comparison with the existing work, the main contributions of this study are that: (i) an important lemma is proposed to remove the matrix equality constraint used in many existing results, and three new design methods in strict terms of linear matrix inequality techniques are proposed; (ii) this study focuses on eliminating the impact of both input and output quantisation errors. Since the impact of input and output quantisation errors cannot be fully eliminated, the estimation error is proved to be ultimately bounded and to converge to a residual set; and (iii) an adaptive compensation term is constructed to compensate for the time-variant effects caused by non-linear dynamics and uncertain parameter vectors. Finally, two numerical examples are given to show the efficacy and advantages of the proposed methods.

Output regulation of output feedback systems with unknown exosystem and unknown high-frequency gain sign

This paper discusses the global robust output regulation problem for a class of nonlinear output feedback systems. It is assumed that the exosystem and the high-frequency gain sign are unknown and that the unknown parameters can be arbitrarily large. To solve this problem, two major challenges are to be overcome. First, the concurrence of the unknown exosystem and the unknown high-frequency gain sign cannot be handled merely by designing estimators for the two unknown parameters respectively. Second, the conventional extended matching design approach cannot be directly implemented, owing to the arbitrarily large unknown parameters. To cope with these difficulties, a new estimator is developed, and the extended matching design approach is modified to obtain a suitable update law for the estimator. The effectiveness of the proposed adaptive controller is illustrated by an example.