Distributed adaptive output-feedback tracking control of non-affine multi-agent systems with prescribed performance

This paper focuses on the problem of adaptive output feedback tracking control for a class of non-strict-feedback nonlinear systems with unknown control coefficients. By employing a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then, after the introduction of a simple input-driven observer, an adaptive output feedback controller which contains only one parameter is developed for such systems by using backstepping technique and variable separation method. The designed controller ensures that all the signals in the closed-loop systems are Semi-globally Uniformly Ultimately Bounded (SGUUB).

This paper investigates adaptive output feedback control problem for switched uncertain nonlinear systems with input quantization, unmeasured system states and state constraints. Firstly, fuzzy logic systems are introduced to identify system uncertainties, then the fuzzy based observer is constructed to estimate unavailable states. Secondly, combing the backstepping technique and the barrier Lyapunov function method, an adaptive fuzzy output feedback control law is designed, which guarantees that all signals in the closed-loop system are bounded, the system output tracks the reference signal, and system states satisfy their corresponding constraint conditions. Finally, simulation results further show the good performances of the proposed control scheme.

This paper is concerned with the adaptive output-feedback tracking control problem for switched stochastic nonlinear time-delay systems under arbitrary switching in the presence of input and output quantization. Combining common Lyapunov- Krasoviskii functional approach, neural network(NN) approximation-based method and backstepping technique, a novel observer-based adaptive output-feedback controller is presented to guarantee that all signals of closed-loop systems are 4-moment (or 2-moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error can converge to a small neighborhood of the origin. Finally, a simulation example is given to verify the effectiveness of the proposed methodology.

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

Adaptive event-triggered tracking control for strict-feedback nonlinear ODE cascaded n+1 coupled hyperbolic PDE systems.

In this paper, an adaptive tracking control approach is developed for a class of rössler chaotic systems with control input saturation by using dynamic surface control technique. Firstly, the nonlinear dynamical models of rössler chaotic systems with control input saturation are given. Then, an adaptive tracking controller is designed in the framework of dynamic surface control, which guaranteed that all the signals of the closed-loop control systems are ultimately uniformly bounded. A compensation control input term is introduced in the design of adaptive tracking controller in order to solve the control input saturation problem. Further, the stability of the closed-loop control systems is analyzed based on the Lyapunov stability theory. Finally, simulation results are given to illustrate the feasibility of the proposed approach.

Switched-observer-based adaptive DSC design of nonstrict-feedback switched stochastic nonlinear time-delay systems

Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems

In this paper, an novel adaptive tracking control is developed for a class of completely non-affine pure-feedback nonlinear systems using radial basis function neural networks (RBFNNs). Combining the dynamic surface control (DSC) technique and backstepping method, the explosion of complexity in the traditional backstepping design is avoided. Using mean value theorem and Young's inequality, only one learning parameter need to be tuned online in the whole controller design, and the computational burden is effectively alleviated. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results verify the effectiveness of the proposed approach.

This paper is concerned with the problem of adaptive neural output-feedback control for a class of nonlinear strict-feedback systems. In this research, radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions and a state observer is constructed to estimate the immeasurable state variables. An adaptive neural output feedback control scheme is proposed via backstepping technique. The proposed design method can guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system, and ensures the observer errors converge to a small neighborhood. At last, a simulation example is included to show the effectiveness of the proposed method.

Based on the backstepping technique, an adaptive neural network (NN) based output feedback controller is proposed to achieve a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict-feedback form. The neural networks are utilized to approximate unknown functions in the systems. The adaptive output feedback controller needs only to adjust less adaptive parameters, therefore it is clear that the proposed approach can reduce on-line computation burden. It is proven that all the signals in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. A simulation example is used to verify the effectiveness of the proposed approach.

In this paper, the output feedback adaptive multi-dimensional Taylor network (MTN) tracking control for a class of nonlinear systems with unmeasurable states is investigated. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states, and then an adaptive MTN-based output-feedback control approach is developed via backstepping technique. Secondly, in view of the simple structure of MTN, the controller based on MTN has the advantages of simple structure and fast calculation speed. Thirdly, in order to avoid the “differential explosion” problem inherited from the backstepping design, dynamic surface control (DSC) technique is introduced in the process of controller design. The results demonstrate that this scheme guarantees the stability and tracking performance of the closed-loop system. Finally, simulation examples are given to reveal the viability of the proposed method.

Fully distributed dynamic event-triggered reliable COR of nonlinear MASs with communication link faults and actuator faults for directed graphs

In this article, the adaptive consensus tracking control is developed for uncertain multiagent systems with time-varying state delay in the case that leader's state is accessible at sampling instants. By proposing a distributed sampled observer with hybrid form, adaptive tracking controller with the complementary term is designed for first-order multiagent systems, and then is extended to high-order multiagent systems with the aid of dynamic surface control. Through the complementary term, the effects of parameter estimation error as well as dynamical terms with time-varying delays are eliminated and thus less conservative condition on time delays is required. It is proved that, under criteria in terms of linear matrix inequalities (LMIs), tracking error and estimation error exponentially converge to zero for first-order systems, and to a sufficiently small neighborhood of zero for high-order systems.

This study presents the state-feedback and output-feedback adaptive shared trajectory tracking control laws for nonlinear Euler–Lagrange systems subject to parametric uncertainties and output constraints framed within linear inequalities. The logarithm-driven coordinate transformation is used to ensure that system outputs are consistently bounded by defined regions, while model-based adaptive laws are used in the machine controller to estimate and cancel parametric uncertainties and the human controller can be given arbitrarily. The stability of the whole controlled system is proved by Lyapunov stability theory, and simulation examples are used to illustrate the performance of the proposed shared control laws.