Reinforcement Learning based Output-Feedback Controller for Complex Nonlinear Discrete-time Systems

Shih, Peter; Jagannathan, S.

doi:10.1109/isic.2007.4450920

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Therefore many researchers were also motivated to design output feedback learning controllers that does not require link velocity measurements. To name a few, in [8] and [9] neural network based reinforcement-learning controllers were presented for different classes of nonlinear discrete-time systems. In [10], a learning controller for a class of single-input, singleoutput, minimum phase, nonlinear, time-invariant systems with unknown output-dependent nonlinearities, unknown parameters and known relative degree ρ is considered.…”

Section: Introductionmentioning

confidence: 99%

Lyapunov-based output feedback learning control of robot manipulators

Dogan

Tatlıcıoğlu

Zergeroğlu

et al. 2015

2015 American Control Conference (ACC)

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Abstract-This paper address the output feedback learning tracking control problem for robot manipulators with repetitive desired joint level trajectories. Specifically, an observer-based output feedback learning controller for periodic trajectories with known period have been proposed. The proposed learning controller guarantees semi-global asymptotic tracking despite the existence of parametric uncertainties associated with the robot dynamics and lack of velocity measurements. A learningbased feedforward term in conjunction with a novel observer formulation is designed to obtain the aforementioned result. The stability of the controller-observer couple is guaranteed via Lyapunov based arguments. Numerical studies performed on a two link robot manipulator are also presented to demonstrate the viability of the proposed method.

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Section: Introductionmentioning

confidence: 99%