Adaptive Dynamic Programming for Stochastic Systems With State and Control Dependent Noise
In this technical note, the adaptive optimal control problem is investigated for a class of continuous-time stochastic systems subject to multiplicative noise. A novel non-model-based optimal control design methodology is employed to iteratively update the control policy on-line by using directly th...
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| Veröffentlicht in: | IEEE transactions on automatic control 2016-12, Vol.61 (12), p.4170-4175 |
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| container_title | IEEE transactions on automatic control |
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| creator | Bian, Tao Jiang, Yu Jiang, Zhong-Ping |
| description | In this technical note, the adaptive optimal control problem is investigated for a class of continuous-time stochastic systems subject to multiplicative noise. A novel non-model-based optimal control design methodology is employed to iteratively update the control policy on-line by using directly the data of the system state and input. Both adaptive dynamic programming (ADP) and robust ADP algorithms are developed, along with rigorous stability and convergence analysis. The effectiveness of the obtained methods is illustrated by an example arising from biological sensorimotor control. |
| doi_str_mv | 10.1109/TAC.2016.2550518 |
| format | Article |
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| ispartof | IEEE transactions on automatic control, 2016-12, Vol.61 (12), p.4170-4175 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive control Adaptive dynamic programming adaptive optimal control Adaptive systems Algorithm design and analysis Algorithms Convergence Dynamic programming Noise control Optimal control Robustness Stability analysis Stochastic processes Stochastic systems |
| title | Adaptive Dynamic Programming for Stochastic Systems With State and Control Dependent Noise |
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