Observer based robust neuro-adaptive control of non-square MIMO nonlinear systems with unknown dynamics

Abstract

This paper addresses a robust adaptive control problem of non-square nonlinear systems with unmeasurable states. The systems are assumed to be multi-input/multi-output subject to dynamical uncertainties and external disturbances. The approach is studied for two cases, i.e., underactuated and over-actuated nonlinear systems. The new observer does not need to satisfy the SPR conditions. Moreover, a constant full-rank matrix with an adaptive gain is used to approximate the unknown gain matrix. Therefore, the proposed controller's structure simplifies its implementation. The unknown nonlinearity is estimated neural networks. Stability of the closed-loop system is proved using Lyapunov analysis. The feasibility of the proposed approach is validated by simulation examples.

A Novel Output-Feedback Controller for Non-Square MIMO Nonautonomous Nonlinear Systems With Unstructured Uncertainties Using Higher-Order Switching Differentiator

“…However, stability analysis has been performed only for the over-actuated case. Similarly, [36] discusses a NN controller for non-square nonlinear systems. Nonetheless, the proposed approach has certain limitations, such as an increased complexity in the control structure due to the employment of NNs, and the restriction that the controlled system must conform to an affine form.…”

“…) square systemIn this case, that is, p = q, the square matrix G in(36) is directly determined as(37) similar to the case of underand over-actuated system. Therefore, the inverse matrix of G evidently exists, which guarantees the existence of M in(34).VOLUME 11, 2023 …”