Adaptive NN Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems

Abstract

An adaptive neural network tracking control is studied for a class of multiple-input multiple-output (MIMO) nonlinear systems. The studied systems are in discrete-time form and the discretized dead-zone inputs are considered. In addition, the studied MIMO systems are composed of N subsystems, and each subsystem contains unknown functions and external disturbance. Due to the complicated framework of the discrete-time systems, the existence of the dead zone and the noncausal problem in discrete-time, it brings about difficulties for controlling such a class of systems. To overcome the noncausal problem, by defining the coordinate transformations, the studied systems are transformed into a special form, which is suitable for the backstepping design. The radial basis functions NNs are utilized to approximate the unknown functions of the systems. The adaptation laws and the controllers are designed based on the transformed systems. By using the Lyapunov method, it is proved that the closed-loop system is stable in the sense that the semiglobally uniformly ultimately bounded of all the signals and the tracking errors converge to a bounded compact set. The simulation examples and the comparisons with previous approaches are provided to illustrate the effectiveness of the proposed control algorithm.

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

“…where 20 and 21 are positive design parameters and 2 and Υ 2 are defined in (12). Moreover, the virtual control law is chosen the as same as Section 3; see (15) for the detail.…”

“…In order to enhance system robustness on the uncertain parameters in the presence of external disturbances, sliding mode control [8,9] has been proposed to obtain the desired robotic tracking control performance. Owing to the universal approximation property [10][11][12][13][14][15][16][17], a great number of intelligent control schemes, such as adaptive neural/fuzzy control, have been developed for controlling robotic systems with uncertain nonlinearities [18][19][20][21][22].…”

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

“…In [39], a controller was designed for dual-arm coordination of a humanoid robot based on the adaptive neural control. The tracking performance of the adaptive NN control for a discrete-time system was studied in [40]. In [41], the effectiveness of the NN control was firstly considered in the Prandtl-Ishlinskii (PI) hysteresis system.…”

Neural Network-Based Control of Networked Trilateral Teleoperation With Geometrically Unknown Constraints

“…Specifically, an adaptive neural tracking control was first studied in [44] for a class of multiple-input multiple-output uncertain nonlinear systems with the discretized dead-zone inputs and it is to design a novel intelligent control scheme to avoid the effect of the discretized dead-zone inputs. The networked control systems focusing on modeling, analysis, and synthesis have been investigated in [15], [17], [18], [30], [42], and [43] under constant, random, or time-varying delays.…”