Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints

Abstract

This paper addresses the consensus problem of nonlinear multiagent system with state constraints. A novel γ-type barrier Lyapunov function is adopted to handle with the bounded constraints. The iterative learning control strategy is introduced to estimate the unknown parameter and basic control signal. Five control schemes are designed, in turn, to address the consensus problem comprehensively from both theoretical and practical viewpoints. These schemes include the original adaptive scheme, projection-based scheme, smooth function-based scheme and its alternative, and dead-zone-like scheme. The consensus convergence and constraints guarantee are strictly proved for each control scheme by using the barrier composite energy function approach. Illustrative simulations verify the theoretical analysis.

A Novel Adaptive Iterative Learning Control Approach for Nonlinear Output‐Constrained Systems With Input Quantization

“…Since 𝐸 𝑘 , 𝐸 1 and 𝜌 are the bounded and non-negative, the necessary conditions for (83) can be obtained according to the limit theorem as follows, lim . Furthermore, it follows from (20) that…”

The filtering‐based maximum likelihood iterative estimation algorithms for a special class of nonlinear systems with autoregressive moving average noise using the hierarchical identification principle

“…, (21)-(22), (25)-(27), (28)-(38) and(43)-(46) construct the F-ML-HGI identification algorithm for the bilinear systems. The identification steps of the F-ML-HGI algorithm tocomputêk(t) = [̂T k (t),ê T k (t),f T k (t)]T are listed as follows.1.…”

“…For example, Ding proposed a hierarchical gradient-based iterative algorithm and a hierarchical least squares-based iterative algorithm for the multi-input-output-error autoregressive (AR) systems. 25 The iterative methods are often used in designing the controller 26,27 and finding the solutions of matrix equations or the roots of nonlinear equations. 28 For example, Zhang et al established an implicit iterative algorithm for solving a class of Lyapunov matrix equations.…”

“…5. Computê1 ,k (t),û 1 ,k (t), andv ,k (t) using (25)- (27), and form̂s ,k (t) and̂n ,k (t) using (30) and (31). 6.…”

Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

“…In [32][33][34], the γ-type BLF and state transformation technique using mapping functions have been proposed for MASs to handle the state constraints problem. But all of these methods have the limitations that the initial tracking condition is required to be known and satisfied constraints, and the state constraints must be imposed from the beginning of system operation, which reduce the applicability.…”