Optimal Control of Uncertain Switched Systems Based on Model Reference Adaptive Control
نویسندگان
چکیده
Switched systems are an important subclass of hybrid systems that consists of subsystems with continuous dynamics and a rule to regulate the switching behavior between them. Switched systems appear in a wide range of applications, such as intelligent transportation systems and smart energy systems. Due to the desire to drive switched systems toward optimal behavior, e.g. maximization of traffic flow in traffic networks, minimization of energy consumption in energy systems, optimal control of switched systems has attracted a lot of attention. Solution of the HamiltonJacobi-Bellman (HJB) equation via Dynamic Programming has been proposed to address optimal control for switched systems. This, however, gives rise to the ‘curse of the dimensionality’. Recently, adaptive dynamic programming (ADP) has been adopted to solve optimal control problems for switched systems. ADP algorithms are capable of avoiding the ‘curse of the dimensionality’ and approximating the optimal control law via recursive relationships and soft computation technologies, like neural networks. Lu and Ferrari [1] train neural networks to approximate the optimal value function in a forward fashion. The weights are updated online by recursive relationships involving the objective function and its gradient with respect to the switching modes. Heydari and Balakrishnan [2] adopt ADP to obtain the optimal switching sequence and switching instants. A batch training algorithm is applied to update weights of the neural networks in a backward fashion.
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