Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints

Abstract

An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

“…In [56], an adaptive BLF controller was devised for PMSM with full-state constraints. An adaptive neural control strategy was designed for multiple input multiple output (MIMO) nonlinear systems with various constraints [57].…”

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

“…Input saturation has an effect on control performance, which has been investigated in the last few decades. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] The Takagi-Sugeno fuzzy modeling approach was utilized to control the nonlinear systems with actuator saturation in the work of Cao and Lin. 37 The stabilization problem was addressed for a class of Hamiltonian systems with state time-delay and input saturation in the work of Sun.…”

Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances

“…Adaptive neural network control was investigated for an uncertain nonlinear system with asymmetric saturation actuators [18], where the established control strategies required the known sign of control 2 Mathematical Problems in Engineering gain and met a specific assumption of uncertain strictfeedback nonlinear system. Although approximation-based adaptive control approaches were proposed for a class of MIMO systems [19,20], the input constraints cannot be compensated when encountering the entirely unknown dynamics model [19]. In [20], the researchers developed an adaptive neural control strategy for a class of affine nonlinear systems.…”

“…Although approximation-based adaptive control approaches were proposed for a class of MIMO systems [19,20], the input constraints cannot be compensated when encountering the entirely unknown dynamics model [19]. In [20], the researchers developed an adaptive neural control strategy for a class of affine nonlinear systems.…”

“…(2) To make the proposed controller more general and practical, the external disturbances, system uncertainties, and the input saturation are considered for the entirely unknown MIMO nonlinear systems in this paper without satisfying the matched parametric uncertainties [16] for a class of affine nonlinear systems [20] or requiring the known sign of the control gain of strict-feedback nonlinear system [18].…”