Adaptive Critic Design Using Policy Iteration Technique for LTI Systems: A Comprehensive Performance Analysis

Prasad, Lal Bahadur; Tyagi, Barjeev; Gupta, Hari Om

doi:10.1007/s40313-015-0167-5

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Remark 7: In the previous works [14], [17], [18], [21], [23], [27], unknown dynamics is considered but external disturbances are ignored. The exploited scheme can deal with completely unknown dynamic systems subject to external disturbances.…”

Section: This Is the End Of The Proofmentioning

confidence: 99%

“…an actor-critic controller, consisting of a neural actor network and a critic network, is addressed for a high-order strictfeedback affine system. For a class of linear time-invariant dynamic systems of affine formulations, an adaptive actorcritic-based optimal controller is designed using policy iteration technique [14]. Though this method [14] doesn't rely on complete knowledge of system dynamics, external disturbances are ignored.…”

Section: Introductionmentioning

confidence: 99%

“…For a class of linear time-invariant dynamic systems of affine formulations, an adaptive actorcritic-based optimal controller is designed using policy iteration technique [14]. Though this method [14] doesn't rely on complete knowledge of system dynamics, external disturbances are ignored. Furthermore, by considering system uncertainties and external disturbances, an adaptive critic design (ACD) strategy is proposed for parabolic partial differential equation systems [15].…”

Section: Introductionmentioning

confidence: 99%

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Actor-Critic Reinforcement Learning Control of Non-Strict Feedback Nonaffine Dynamic Systems

2019

IEEE Access

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The most focuses of the existing actor-critic reinforcement learning control (ARLC) are on dealing with continuous affine systems or discrete nonaffine systems. In this paper, I propose a new ARLC method for continuous nonaffine dynamic systems subject to unknown dynamics and external disturbances. A new input-to-state stable system is developed to establish an augmented dynamic system, from which I further get a strict-feedback affine model that is convenient for control designing based on a model transformation approach. The Nussbaum function is connected with a fuzzy approximation to devise an actor network whose tracking performance is further enhanced via strengthening signals generated by a fuzzy critic network. The stability of the closed-loop control system is guaranteed by the Lyapunov synthesis. Finally, the comparison simulation results are presented to verify the design.INDEX TERMS Reinforcement learning control, continuous nonaffine systems, actor network, fuzzy critic network.

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