| 1. |
- Li, Yuchao, et al.
(författare)
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Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies : Well-Posedness and Convergence
- 2020
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Ingår i: Proceedings of the 2nd Conference on Learning for Dynamics and Control, L4DC 2020. - : ML Research Press. ; , s. 540-549
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Konferensbidrag (refereegranskat)abstract
- dynamic programming models are used to analyze λ-policy iteration with randomization algorithms. Particularly, contractive models with infinite policies are considered and it is shown that well-posedness of the λ-operator plays a central role in the algorithm. The operator is known to be well-posed for problems with finite states, but our analysis shows that it is also well-defined for the contractive models with infinite states studied. Similarly, the algorithm we analyze is known to converge for problems with finite policies, but we identify the conditions required to guarantee convergence with probability one when the policy space is infinite regardless of the number of states. Guided by the analysis, we exemplify a data-driven approximated implementation of the algorithm for estimation of optimal costs of constrained linear and nonlinear control problems. Numerical results indicate potentials of this method in practice.
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| 2. |
- Li, Yuchao, et al.
(författare)
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Linear Time-Varying Model Predictive Control for Automated Vehicles : Feasibility and Stability under Emergency Lane Change
- 2020
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Ingår i: Ifac papersonline. - : Elsevier BV. - 2405-8963. ; , s. 15719-15724
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Konferensbidrag (refereegranskat)abstract
- In this work, we present a novel approach based on linear matrix inequalities to design a linear-time varying model predictive controller for a nonlinear system with guaranteed stability. The proposed method utilizes a multi-model description to model the nonlinear system where the dynamics is represented by a group of linear-time invariant plants, which makes the resulting optimization problem easy to solve in real-time. In addition, we apply the control invariant set designed as the final stage constraint to bound the additive disturbance introduced to the plant by other subsystems interfacing with the controller. We show that the persistent feasibility is ensured with the presence of such constraint on the disturbance of the specified kind. The proposed method is then put into the context of emergency lane change for steering control of automated vehicles and its performance is verified via numerical evaluation.
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| 3. |
- Liu, Hanxiao, et al.
(författare)
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Reinforcement Learning Based Approach for Flip Attack Detection
- 2020
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 3212-3217
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the detection problem of flip attacks to sensor network systems where the attacker flips the distribution of manipulated sensor measurements of a binary state. The detector decides to continue taking observations or to stop based on the sensor measurements, and the goal is to have the flip attack recognized as fast as possible while trying to avoid terminating the measurements when no attack is present. The detection problem can be modeled as a partially observable Markov decision process (POMDP) by assuming an attack probability, with the dynamics of the hidden states of the POMDP characterized by a stochastic shortest path (SSP) problem. The optimal policy of the SSP solely depends on the transition costs and is independent of the assumed attack possibility. By using a fixed-length window and suitable feature function of the measurements, a Markov decision process (MDP) is used to approximate the behavior of the POMDP. The optimal solution of the approximated MDP can then be solved by any standard reinforcement learning methods. Numerical evaluations demonstrates the effectiveness of the method.
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