A new neuroadaptive control architecture for nonlinear uncertain dynamical systems: Beyond σ- and e-modifications

Abstract

Neural networks are a viable pararadigm for adaptive system identification and control. This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system parameters as well as effectively suppress system uncertainty. A linear parameterization of the system uncertainty is considered and state feedback neuroadaptive controllers are developed.

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

“…These include the σ-and e-modification architectures used to keep the system parameter estimates from growing without bound in the face of system uncertainty. In this research [10,13,30], a new neuroadaptive control architecture for nonlinear uncertain dynamical systems is developed. Specifically, the proposed framework involves a new and novel controller architecture involving additional terms, or Q-modification terms, in the update laws that are constructed using a moving time window of the integrated system uncertainty.…”

Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control

“…To address this problem, the authors of [1][2][3][4][5][6][7][8] present modifications to adaptive update laws. In particular, the work in [1][2][3] uses filtered versions of the control input and state; [4][5][6] uses a moving time window of the system uncertainty; and [7,8] uses recorded and instantaneous data concurrently.…”

“…In particular, the work in [1][2][3] uses filtered versions of the control input and state; [4][5][6] uses a moving time window of the system uncertainty; and [7,8] uses recorded and instantaneous data concurrently. In contrast to these approaches, the authors of [9][10][11] present an approach called artificial basis functions that adds modification terms not only to the update law, but also to the adaptive controller and show that the system error can be suppressed during the transient system response.…”

“…In contrast to these approaches, the authors of [9][10][11] present an approach called artificial basis functions that adds modification terms not only to the update law, but also to the adaptive controller and show that the system error can be suppressed during the transient system response. The common denominator of the approaches in [1][2][3][4][5][6][7][8][9][10][11] is that they introduce additional mechanisms to model reference adaptive control laws that capture a form of the system uncertainty in order to suppress its effect.…”

“…Unlike the approaches in [1][2][3][4][5][6][7][8], the proposed framework consists of an architecture involving modification terms in both the adaptive controller and the update law, such that these terms are activated when the system error is nonzero and vanishes as the system reaches its steady state. In addition, this new framework directly allows one to suppress the effect of system uncertainty on the transient system response through a gradient minimization procedure and, hence, leads to improved system performance.…”