A neuroadaptive control architecture for nonlinear uncertain dynamical systems with input actuator constraints

Abstract

This paper presents a new model reference neuroadaptive control architecture for multivariable nonlinear uncertain dynamical systems with input actuator constraints. In particular, we consider both linear and nonlinear in the parameters neural network approximations to design neuroadaptive controllers for stabilization and command following in the presence of actuator dynamics that can effectively account for actuator amplitude and rate saturation constraints. An illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

Complexity, Robustness, and Network Thermodynamics in Large-Scale and Multiagent Systems: A Hybrid Control Approach

“…In this research [36], we develop a new model reference adaptive control architecture for nonlinear uncertain dynamical systems with input actuator and time delay constraints. In particular, we consider both linear and nonlinear in the parameters neural network approximations to design neuroadaptive controllers for stabilization and command following in the presence of actuator dynamics that can effectively account for actuator amplitude and rate saturation constraints.…”