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Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning

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arXiv 2023年

作者： Wang, Siqi Li, Shaoyuan Yin, Li Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Institute of Systems Engineering Macau University of Science and Technology Taipa China

This paper investigates the problem of designing control policies that satisfy high-level specifications described by signal temporal logic (STL) in unknown, stochastic environments. While many existing works concentrate on optimizing the spatial robustness of a system, our work takes a step further by also considering temporal robustness as a critical metric to quantify the tolerance of time uncertainty in STL. To this end, we formulate two relevant control objectives to enhance the temporal robustness of the synthesized policies. The first objective is to maximize the probability of being temporally robust for a given threshold. The second objective is to maximize the worst-case spatial robustness value within a bounded time shift. We use reinforcement learning to solve both control synthesis problems for unknown systems. Specifically, we approximate both control objectives in a way that enables us to apply the standard Q-learning algorithm. Theoretical bounds in terms of the approximations are also derived. We present case studies to demonstrate the feasibility of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

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arXiv 2024年

作者： Jiang, Mingyang Li, Yueyuan Zhang, Songan Chen, Siyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate occasions, whereas learning-based ones are good at exploration but unstable in converging to feasible solutions. To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios. HOPE guides the exploration of the reinforcement learning agent by applying an action mask mechanism and employs a transformer to integrate the perceived environmental information with the mask. To facilitate the training and evaluation of the proposed planner, we propose a criterion for categorizing the difficulty level of parking scenarios based on space and obstacle distribution. Experimental results demonstrate that our approach outperforms typical rule-based algorithms and traditional reinforcement learning methods, showing higher planning success rates and generalization across various scenarios. We also conduct real-world experiments to verify the practicability of HOPE. The code for our solution is openly available on https://***/jiamiya/HOPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Parking

Distributed Fuzzy Model Predictive control of Nonlinear systems: An Interval Predictor Approach

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Distributed Fuzzy Model Predictive Control of Nonlinear Syst...

Asian control Conference

作者： Hua Zheng Yuanyuan Zou Shaoyuan Li SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya P. R. China Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education Shanghai

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, distributed fuzzy predictive control (DFMPC) for nonlinear systems with a coupled objective function is investigated. Firstly, the original nonlinear subsystem is reformulated as Takagi-Sugeno fuzzy models, thereby enabling the application of certain linear system properties, such as the design of a terminal set and corresponding local controllers within the optimization framework. Subsequently, an interval predictor is devised by taking positive and negative mapping of the fuzzy system matrices. Based on the interval predictor, the DFMPC optimization problem is reconstructed to eliminate non- convexity resulting from membership functions (MFs) and their associated premise variables. This approach allows for solving the DFMPC optimization problem independently of the specific shapes of the MFs and premise variables. Finally, the effectiveness of the proposed Algorithm is verified through its implementation in a two-cart-damp-spring simulation example.

关键词： Linear systems Shape Predictive models Prediction algorithms Linear programming Trajectory Takagi-Sugeno model

Tracking control for High-order Fully Actuated system with Input Constraints by Explicit Reference Governor

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Tracking Control for High-order Fully Actuated System with I...

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Lin Yang Yuanlong Li Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai P. R. China

This paper studies the tracking control problem of high-order fully actuated system subject to input constraints. Since there exist input constraints, an inappropriate reference signal being tracked may cause actuator saturation and further cause system instability. For solving the problem, we firstly design a generalized proportional differential controller to ensure the stability of the closed-loop system without input constraints. Then, an explicit reference governor is constructed to modify the reference signal such that the applied reference signal never exceeds the saturation limits, and the equilibrium of the resulting closed-loop system is globally asymptotically stable. Furthermore, we prove that the applied reference will converge to the original reference as much as possible. Therefore, the system output finally converges to the original reference by tracking the applied reference. A numerical example demonstrates the validity of the proposed method in this paper.

关键词：

BEARING-BASED TARGET ENTRAPPING control OF MULTIPLE UNCERTAIN AGENTS WITH ARBITRARY MANEUVERS

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arXiv 2023年

作者： Su, Haifan Yang, Ziwen Zhu, Shanying Chen, Cailian Yu, Wenbin Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

This paper is concerned with bearing-based cooperative target entrapping control of multiple uncertain agents with arbitrary maneuvers including shape deformation, rotations, scalings, etc. A leader-follower structure is used, where the leaders move with the predesigned trajectories, and the followers are steered by an estimation-based control method, integrating a distance estimator using bearing measurements and a stress matrix-based formation controller. The signum functions are used to compensate for the uncertainties so that the agents’ accelerations can be piecewise continuous and bounded to track the desired dynamics. With proper design of the leaders’ trajectories and a geometric configuration, an affine matrix is determined so that the persistently exciting conditions of the inter-agent relative bearings can be satisfied since the bearing rates are related to different weighted combinations of the affine matrix vectors. The asymptotic convergence of the estimation error and control error is proved using Filipov properties and cascaded system theories. A sufficient condition for inter-agent collision avoidance is also proposed. Finally, simulation results are given to validate the effectiveness of the method in both 2D and 3D cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Acceleration

Improved Adaptive Variable Impedance control for Contact Force Tracking

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Improved Adaptive Variable Impedance Control for Contact For...

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Feili Wei Chaochen Gu Kaijie Wu Hao Cheng Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

The utilization of impedance control is capable of achieving excellent force tracking performance. However, the balance between stability and flexibility is seldom considered in current methods. To address this issue, a novel adaptive variable impedance controller for contact force tracking is proposed in this study. Lyapunov theory is applied to develop an adaptive adjustment law of damping parameter in impedance control to enhance the robot’s adaptability to uncertain environment and dynamic desired force. Additionally, a force compensator is designed based on direct adaptive control to expedite the response of force tracking. What sets this proposed method apart is that it does not require an accurate understanding of environmental parameters. Instead, it dynamically modifies the impedance parameters based on the force error to guarantee the stability and coordination of the system. Simulation results indicate that the proposed controller improves the flexibility and robustness of the force tracking capability of the robot system when compared to existing variable impedance control methods and effectively diminishes force overshoot.

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Integrated Guidance and control Design Subject to Actuator Saturation Based on Fully Actuated system Approach

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Integrated Guidance and Control Design Subject to Actuator S...

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Lin Yang Yuanlong Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Based on fully actuated system approach, this paper proposes a design scheme for integrated guidance and control systems subject to actuator saturation. By elimination elevation-order way, the integrated guidance and control model in the pitch channel is transformed into a high-order fully actuated system with uncertain disturbances and actuator saturation. Firstly, a disturbance observer is constructed to estimate the disturbances generated by model nonlinearities, unknown target maneuvers, variations of aerodynamic parameters, etc. Then, an observer-based fully actuated controller is designed to stabilize the integrated guidance and control system and perform disturbance attenuation. Since the missile’s deflection angle for pitch control is constrained by actuator saturation, an anti-windup compensator is introduced into the designed fully actuated controller to reduce the negative impact of saturation. Moreover, the stability conditions of the closed-loop system are established by the Lyapunov stability theory and the invariant set principle. Finally, a numerical simulation is provided to verify the effectiveness of the proposed design scheme.

关键词： Actuators Missiles control design Estimation Numerical simulation Disturbance observers Stability analysis

K-BMPC: Derivative-based Koopman Bilinear Model Predictive control for Tractor-Trailer Trajectory Tracking with Unknown Parameters

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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC. © 2023, CC BY-NC-SA.

关键词： Model predictive control

Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear systems Against Actuator Faults

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Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

International Symposium on Autonomous systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems Process control Artificial neural networks control systems Approximation error