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Successive approximate design of the optimal tracking controller for linear systems with time-delay is considered. A functional-based transformation is introduced, which transforms the system with control delay into a system without control delay formally. By applying the successive approximation approach of differential equations, the two-point boundary value (TPBV) problem with both time-delay and...
This paper considers the optimal tracking control (OTC) problem for bilinear systems affected by sinusoidal disturbances, and gives a successive approximation approach to solve analogous problems for general bilinear systems. For a given bilinear OTC problem, a sequence of linear problems is constructed, and their solutions are shown to converge to the desired solution. By iteratively solving the...
This note presents a feedforward and feedback optimal control (FFOC) law for a class of nonlinear time-delay systems affected by external disturbances. A sequence of inhomogeneous linear non-delay state and adjoint vector equations are constructed to approximate to the original two-point boundary value (TPBV) problems from the original optimal control problem. The convergence of the sequences is proved...
An approximate optimal tracking control (OTC) algorithm for sinusoidal disturbances rejections of nonlinear systems is developed in this paper. In the proposed control method, the nonlinear two-point boundary value (TPBV) problem, which is derived from the original OTC problem, is rewritten into a new form through a transformation and is transformed into a sequence of linear TPBV problems by using...
The optimal tracking control problem for nonlinear interconnected large-scale systems with time-delays is considered. By using a successive approximation approach (SAA), a high order, coupled, nonlinear two-point boundary value (TPBV) problem with both time-delay and time-advance terms, which is derived from the necessary condition of the original optimal tracking control problem, is transformed into...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and in this instance, RL techniques require the use of function approximators for learning value functions and policies. Often, local linear models have been preferred over distributed nonlinear models for function approximation in RL. We suggest that one reason for the difficulties encountered when using...
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