Model-Free Adaptive Control of Nonlinear Systems with Input Backlash and Constraints

For nonlinear Backlash systems with input constraints that are difficult to model accurately, a model-free adaptive control algorithm (MFAC)based on data-driven technology is proposed. The criterion function of MFAC algorithm is applied to set up a quadratic programming problem with input constraints, and the optimal control input signal is obtained by solving the quadratic programming problem. The tracking differentiator is used to process the measured output signal to reject the adverse effect of random noise on the solution of the optimal input signal and the control stationarity. The stability of the control algorithm is also analyzed and proved. Finally, the superiority of the proposed control algorithm is verified by example test.