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In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods...
To solve the learning control problem of a bioreactor system, a novel framework of heuristic dynamic programming (HDP) with sparse kernel machines is presented, which integrates kernel methods into critic learning of HDP. As a class of adaptive critic designs (ACDs), HDP has been used to realize online learning control of dynamical systems, where neural networks are commonly employed to approximate...
An algorithmic method is proposed to design stabilizing control laws for a class of nonlinear systems that comprises single-input feedback-linearizable systems and a particular set of single-input non feedback-linearizable systems. The method proceeds iteratively and consists of two stages; it converts the system into cascade form and reduces the dimension at every step by creating quotient manifold...
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