A Novel Approach Based on Terminal Sliding Mode Control for Optimization of Nonlinear Systems

Abstract

It is difficult for many discrete nonlinear systems to quickly meet control requirements and establish accurate dynamic models. This study presents a control strategy that combines model-free adaptive control and discrete terminal sliding mode control to solve these problems. A novel discrete terminal sliding surface is designed in this strategy. It not only avoids the establishment of system models but also achieves quick response. Firstly, a perturbation observer is established to estimate perturbation of the system and time-varying parameter pseudo-gradient is estimated from input and output data of the system. Secondly, the controller combining the partial form dynamic linearization model free adaptive control and discrete terminal sliding mode control is designed. The stability and convergence of the proposed strategy are proved in theory. Thirdly, the proposed strategy is compared with PID technique and the method of combining model free adaptive control(MFAC) with discrete sliding mode control in chemical system and water tank system respectively. Simulation examples demonstrate that the proposed strategy is superior to others in terms of fast response. This study provides a reference for solving the control problem of discrete nonlinear systems. INDEX TERMS Dynamic models of nonlinear systems, perturbation observer, data-driven control, discrete terminal sliding mode control.

Three Tank System Sensors and Actuators Faults Detection Employing Unscented Kalman Filter

“…Some of the main parameters of this system such as the viscosity coefficients are uncertain due to the change in the liquid characteristics, aging effects, other environmental reasons as corrosion, scaling, and changing operating conditions. Some techniques have been previously proposed such as neural, fuzzy [13], [14], non-linear observers [15], the generalized likelihood ratio-based system [16], and the multiple model-based approaches [17]. Nevertheless, these approaches are unable to estimate the fault if the noise in the system is higher than the fault magnitude.…”