Adaptive NN Controller of Nonlinear State-Dependent Constrained Systems With Unknown Control Direction

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 1; pp. 913 - 922
• Main Authors: Li, Dapeng, Han, Hong-Gui, Qiao, Jun-Fei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Adaptive neural control; Adaptive systems; Artificial neural networks; Boundaries; Closed loops; Constraints; Control methods; Control systems; Control systems design; Controllers; Neural networks; nonlinear constrained systems; Nonlinear control; nonlinear state-dependent mappings; Nonlinear systems; Nussbaum gain technique; Parameter identification; Radial basis function; System dynamics; Tracking errors; Transient analysis; Transient performance
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3177839

Various constraints commonly exist in most physical systems; however, traditional constraint control methods consider the constraint boundaries only relying on constant or time variable, which greatly restricts applying constraint control to practical systems. To avoid such conservatism, this study develops a new adaptive neural controller for the nonlinear strict-feedback systems subject to state-dependent constraint boundaries. The nonlinear state-dependent mapping is employed in each step of backstepping procedure, and the prescribed transient performance on tracking error and the constraints on system states are ensured without repeatedly verifying the feasibility conditions on virtual controllers. The radial basis function neural network (NN) with less parameters approach is introduced as an identifier to estimate the unknown system dynamics and reduce computation burden. For removing the effect of unknown control direction, the Nussbaum gain technique is integrated into controller design. Based on the Lyapunov analysis, the developed control strategy can ensure that all the closed-loop signals are bounded, and the constraints on full system states and tracking error are achieved. The simulation examples are used to illustrate the effectiveness of the developed control strategy.