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The paper derives an iterative solution algorithm for H∞ control design that is based on policy iteration.
And this algorithm is an optimal control algorithm based on policy iteration (PI), and it can obtain the suitable approximations of cost function and the optimized control policy.
In this paper we discuss an online algorithm based on policy iteration for learning the continuous-time (CT) optimal control solution with infinite horizon cost for nonlinear systems with known dynamics.
Since the proposed method is based on VI learning mechanism, it does not require the admissible control for the implementation, and thus satisfies a more general initial condition than the works based on policy iteration (PI).
The idea is to show the feasibility of introducing the VI learning mechanism to solve for the constrained optimal control problem from a theoretical point of view, and thus the initial admissible control can be avoided compared with most existing works based on policy iteration (PI).
Rollout algorithm [15], as an approximate dynamic programming methodology based on policy iteration ideas, has been successfully applied to various domains such as combinatorial optimization [18] and stochastic scheduling [19].
Similar(54)
Politics has to be based on policy.
Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics.
The proposed flight controller is obtained by solving the Hamilton-Jacobi-Isaacs (HJI) equations based on an policy iteration (PI) approach.
For making ambulance redeployment decisions in a dynamic setting under uncertainty an ADP approach based on a policy iteration algorithm has been developed by Maxwell et al. (2009, 2010).
Furthermore, a least-squares based policy iteration algorithm is employed to train the supplementary controller with convergence and stability guarantee.
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