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In order to overcome numerical stability problems that inherently occur in the recursive least-squares (RLS)-based adaptive dynamic programming paradigms for online optimal control design, a novel method to promote improvements in the state-value function approximations for online algorithms of the discrete linear quadratic regulator (DLQR) control system design is proposed. The algorithms resulting...
The proposed methodology is based on development of online algorithms for approximate solutions of the Hamilton- Jacobi-Bellman (HJB) equation through a familiy of non-squares approximators for critic adaptive solution of the Discrete Algebraic Riccati Equation (DARE), associated with the problem of Discrete Linear Quadratic Regulator (DLQR) . The proposed method is evaluated in a multivariable dynamic...
This paper is concerned with the development of online algorithms for approximate solutions of the Hamilton-Jacobi-Bellman (HJB) equation. In the discrete linear quadratic regulator (DLQR) control system design, the HJB equation is the discrete algebraic Riccati (DARE) equation. Due to the problem of dimensionality curse, this equation is approximated via heuristic dynamic programming (HDP). The proposed...
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