robust exponential concurrent learning adaptive control for systems preceded by dead-zone input nonlinearity

A concurrent learning (cl) adaptive control is proposed for a class of nonlinear systems in the presence of dead-zone input nonlinearity to guarantee the exponential convergence of the tracking and the parameter estimation errors. the proposed method enriches and encompasses the conventional filtering-based cl by proposing robust and optimal terms. the optimal term is designed by solving a suitable quadratic programming optimization problem based on control lyapunov function theory which also meets the need for prescribed control bounds. a suitable robust term is proposed to tackle the presence of the dead-zone input nonlinearity. recent methods of adaptive cl tune the control parameters using trial and error, which is a tedious task. in this paper, by some analysis and proposing two nonlinear optimization problems, the values of the control parameters are derived. the nonlinear optimization problems are solved using the time-varying iteration particle swarm optimization algorithm. the simulation results indicate the effectiveness of the proposed method.