Huynh et al., 2014 - Google Patents
A martingale approach and time-consistent sampling-based algorithms for risk management in stochastic optimal controlHuynh et al., 2014
View PDF- Document ID
- 10689417612536467175
- Author
- Huynh V
- Kogan L
- Frazzoli E
- Publication year
- Publication venue
- 53rd IEEE Conference on Decision and Control
External Links
Snippet
In this paper, we consider a class of stochastic optimal control problems with risk constraints that are expressed as bounded probabilities of failure for particular initial states. We present here a martingale approach that diffuses a risk constraint into a martingale to construct time …
- 238000005070 sampling 0 title abstract description 19
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0285—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks and fuzzy logic
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wachi et al. | Safe reinforcement learning in constrained markov decision processes | |
US10423129B2 (en) | Controlling dynamical systems with bounded probability of failure | |
Dutta et al. | Reachability analysis for neural feedback systems using regressive polynomial rule inference | |
Wabersich et al. | Linear model predictive safety certification for learning-based control | |
Everitt et al. | Reinforcement learning with a corrupted reward channel | |
Wabersich et al. | Safe exploration of nonlinear dynamical systems: A predictive safety filter for reinforcement learning | |
Golowich et al. | Planning in observable pomdps in quasipolynomial time | |
Bonzanini et al. | Safe learning-based model predictive control under state-and input-dependent uncertainty using scenario trees | |
Chaudhari et al. | Sampling-based algorithms for continuous-time POMDPs | |
Jackson et al. | Safety verification of unknown dynamical systems via gaussian process regression | |
Lorenzen et al. | An improved constraint-tightening approach for stochastic MPC | |
Hakobyan et al. | Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach | |
da Silva et al. | Active perception and control from temporal logic specifications | |
Ahmadi et al. | Verification of uncertain POMDPs using barrier certificates | |
Lechner et al. | Infinite time horizon safety of bayesian neural networks | |
von Rohr et al. | Probabilistic robust linear quadratic regulators with Gaussian processes | |
Rafieisakhaei et al. | Feedback motion planning under non-gaussian uncertainty and non-convex state constraints | |
Sadraddini et al. | Formal methods for adaptive control of dynamical systems | |
Shirai et al. | Chance-constrained optimization in contact-rich systems for robust manipulation | |
Huynh et al. | A martingale approach and time-consistent sampling-based algorithms for risk management in stochastic optimal control | |
Bao et al. | Learning-based adaptive-scenario-tree model predictive control with probabilistic safety guarantees using bayesian neural networks | |
Zhai et al. | Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function--Part I: Theoretical Framework and Off-line Study | |
Jeddi et al. | Lyapunov-based uncertainty-aware safe reinforcement learning | |
Wang et al. | Reinforcement learning with temporal logic constraints for partially-observable markov decision processes | |
Min et al. | Constructing confidence sets after lasso selection by randomized estimator augmentation |