Kernel-Based Approximate Dynamic Programming for Real-Time Online Learning Control: An Experimental Study

It is shown that both online learning control schemes are effective for approximating near-optimal control policies of nonlinear dynamical systems with model uncertainties and it is demonstrated that KDHP can achieve better performance than conventional DHP, which uses multilayer perceptron neural networks.