Class Of Uncertain Strict-feedback Systems Research Articles

Event-triggered adaptive control for nonlinear systems using time-receding horizons without initial dependence

This paper investigates the full-state constraint event-triggered adaptive control for a class of uncertain strict-feedback systems. The lack of information on the coupling dynamics of virtual variables in backstepping increases the complexity of feedback design. Given this, the requirements of shaping system performance constraints, eliminating initial dependence, and reducing data transfer costs together give rise to an interesting and challenging problem. Constructing the time-receding horizon (TRH) and stitching it with the quadratic Lyapunov function (QLF) is the key to constrained tracking. Specifying TRHs as a set of smooth bounds with fixed-time convergence and forcing the system to stabilize within the constrained region before the prescribed settling time provide a sufficient condition for practical finite-time stability (PFS). For relaxing the initial dependence, a tuning function is designed to match the performance constraints under arbitrary system initial conditions. A dual-channel event-triggered mechanism (ETM) is developed to automatically adjust the controller and estimator data flow updates with less transmission burden. By combining a specific inequality with backstepping, uncertainties are overcome without the “complexity explosion” in recursion steps. Finally, simulations demonstrate the effectiveness of the proposed method.

Dynamic Event-Triggered Control for a Class of Uncertain Strict-Feedback Systems via an Improved Adaptive Neural Networks Backstepping Approach

Dynamic Event-Triggered Control for a Class of Uncertain Strict-Feedback Systems via an Improved Adaptive Neural Networks Backstepping Approach

Adaptive Fuzzy Control With Global Stability Guarantees for Unknown Strict-Feedback Systems Using Novel Integral Barrier Lyapunov Functions

In this article, the adaptive fuzzy tracking control problem for a class of uncertain strict-feedback systems with unknown nonlinearities is investigated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">particular emphasis on global stability</i> . The proposed control scheme is designed by integrating the barrier Lyapunov functions (BLFs) with the techniques of fuzzy approximation and backstepping. The novel integral BLFs (iBLFs) are introduced to overcome the design difficulties induced by the virtual control coefficients and determine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> the compact set for guaranteeing the validity of fuzzy approximation. Compared with existing approximation-based control results, the developed controller not only guarantees global stability without requiring prior information of system nonlinearities and assumptions on the time derivatives of virtual control coefficients, but also prevents the “explosion of complexity” issue without attaching additional filters. The simulation results further confirm the effectiveness of the theoretical findings.

Finite-Time Robust Intelligent Control of Strict-Feedback Nonlinear Systems With Flight Dynamics Application.

The tracking control is investigated for a class of uncertain strict-feedback systems with robust design and learning systems. Using the switching mechanism, the states will be driven back by the robust design when they run out of the region of adaptive control. The adaptive design is working to achieve precise adaptation and higher tracking precision in the neural working domain, while the finite-time robust design is developed to make the system stable outside. To achieve good tracking performance, the novel prediction error-based adaptive law is constructed by considering the estimation performance. Furthermore, the output constraint is achieved by imbedding the barrier Lyapunov function-based design. The finite-time convergence and the uniformly ultimate boundedness of the system signal can be guaranteed. Simulation studies show that the proposed approach presents robustness and adaptation to system uncertainty.

Fuzzy-approximation-based global adaptive control for uncertain strict-feedback systems with a priori known tracking accuracy

In this study, we propose a novel adaptive backstepping fuzzy control scheme for a class of uncertain strict-feedback systems where the tracking accuracy is known a priori, and we also introduce a multiswitching-based adaptive fuzzy controller. Compared with the existing method for adaptive fuzzy control, the advantages of the proposed scheme are as follows. First, the controller guarantees that all the closed-loop signals are globally uniformly ultimately bounded, which differs from most existing adaptive fuzzy control approaches where the semi-global boundedness of the closed-loop signals is ensured under a harsh assumption on the approximation domain of the fuzzy logic system. Second, our controller ensures that the tracking error converges to an accuracy that is given a priori for the uncertain strict-feedback system, which cannot be achieved using existing adaptive fuzzy control methods. Third, based on some nonnegative functions, we analyze the convergence of the tracking error using Barbalat's Lemma. Fourth, the main technical novelty is the construction of three new nth-order continuously differentiable switching functions, which are used to design the desired controller. Finally, three simulation examples are provided that illustrate the effectiveness of the proposed control strategy.

Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known &lt;italic&gt;a Priori&lt;/italic&gt;

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.