Peaking-Free Output-Feedback Adaptive Neural Control Under a Nonseparation Principle

Abstract

High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear properties, high-gain observers are usually subject to peaking responses and noise sensitivity. Existing adaptive neural network (NN) observers cannot effectively relax the limitations of high-gain observers. This paper presents an output-feedback indirect ANC strategy under a nonseparation principle, where a hybrid estimation scheme that integrates an adaptive NN observer with state variable filters is proposed to estimate plant states. By applying a single Lyapunov function candidate to the entire system, it is proved that the closed-loop system achieves practical asymptotic stability under a relatively low observer gain dominated by controller parameters. Our approach can completely avoid peaking responses without control saturation while keeping favourable noise rejection ability. Simulation results have shown effectiveness and superiority of this approach.

Composite learning adaptive backstepping control using neural networks with compact supports

“…1 with x(0) ∈ Ω x 0 and x d (t) under Assumption 2 driven by the control law constituted by (7) to (9) and (17) withŴ i (0) ∈ Ω wi , if there exist constants T ei > T a and i ∈ R + to satisfy the IE conditions Θ i (T ei ) ≥ i I in Definition 1 and the control parameters k ci and i in (8) are chosen to satisfy k c1 , k cn > 1∕2, k ci > 1, i = 2 to n − 1, The following theorem demonstrates the main results of this study.…”

Adaptive fuzzy backstepping output constraint control of flexible manipulator with actuator saturation

“…Observer theory is aimed at providing a real-time estimatê xðtÞ of the state xðtÞ in the above model (2) [34,35]. A straightforward approach to providing a fuzzy adaptive observer for the model (2) as follows:…”

“…. ; 5, and design actual controller v(35) and the adaptation functions h 2 (29), h 4 (32) and h 5(36).…”

Adaptive Fuzzy Synchronization of Fractional-Order Chaotic (Hyperchaotic) Systems with Input Saturation and Unknown Parameters

“…Many scholars have studied the synchronization control problems for fractional-order chaotic systems. So far, there are many control methods for fractional-order nonlinear chaotic systems (such as drive-response method, finite-time synchronization, nonlinear feedback method, adaptive synchronization control method, nonlinear disturbance observer method, nonlinear coupling method, sliding method, PC method, Lyapunov function activated method, 2 Complexity and synchronization control method [27][28][29][30][31][32][33]). It is worth noting that the above literatures which study the problem of fractional-order chaotic systems synchronization have a basic assumption that the controller does not have any restrictions.…”