Adaptive predictive control with neural prediction for a class of nonlinear systems with time-delay

This work presents an adaptive predictive control with neural prediction for a class of single-input single- output nonlinear systems with known time-delay. The well-known linear dynamic modeling approach together with the neural modeling method is employed to approximate these nonlinear systems. The predictive control law with integral action is derived based on the minimization of a generalized predictive performance criterion. A real-time adaptive control algorithm, including a recursive least-squares estimator and a proposed neural predictor, is successfully applied to achieve the control performance specifications. Stability and properties of the closed-loop control systems are investigated as well Simulation results reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative time-delay nonlinear systems. Experimental results for a variable-frequency oil-cooling control process are performed which have shown effectiveness of the proposed method under the conditions of set-points and load changes.