Adaptive Neural Control of Nonlinear MIMO Systems With Time-Varying Output Constraints

Abstract

In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control input's bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

“…(2) In construct to previous output-constraint neural/ fuzzy back-stepping control approach [15,25,38,40,41], the proposed control scheme does not need numerous NNs/FLS to construct virtual and practical control law in each step, only one neural network including one adaptive laws is required to approximate the lumped unknown function, thus deriving a low-computational control scheme.…”

“…Assumption 4 (see [15]). The output bounds ( ) and ( ) are smooth functions and their derivatives from 1 to th are all available.…”

“…Therefore, the output constraints given by (5) are now transformed into tracking error constraints shown as (15). To establish the relationship between 1 ( ) and 1 ( ),…”

“…However, the works described in [4,5,[12][13][14] only focus on the stabilization of interested systems, and the output-constraint problems are rarely considered. Meanwhile, it must be pointed out that violation of the output constraint may lower the system performance or even lead to systems instability [15,16].…”

Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance

“…One salient feature of the proposed control is that both the transient and steady-state suspension response can be strictly guaranteed. This is achieved by incorporating a prescribed performance function (PPF) [23][24][25][26] into adaptive control. An augmented NN is then used to compensate for the unknown dynamics.…”