On on-line sampled-data optimal learning for dynamic systems with uncertainties

In this study, a novel on-line optimal learning control is proposed to achieve the optimal performance for dynamic systems with modeling uncertainties, measurement noise and iteration-varying initial conditions. By introducing a nominal model and a sampled-data controller, it is possible to find the optimal solution iteratively of an optimization problem using gradient descent method. A feedback controller is introduced along the finite-time domain to ensure that the difference between the output of the nominal model and that of the actual plant can be made arbitrarily small. This feedback thus can be used to handle various uncertainties in the plant model, while the feedforward learning controller is used to ensure the convergence of the plant output to the optimal solution. Hence, by tuning sampling period and feedback gain matrix, it is possible to ensure that the output of plant converges semi-globally practically to the optimal solution. Simulation results illustrate the effectiveness of the proposed method.