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Ying Shuai Quan,Jin Sung Kim,Chung Choo Chung 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
In this paper, we propose a Robust Model Predictive Control combined with Control Barrier Function (RMPC-CBF) for a nonholonomic robot with obstacle avoidance subject to additive input disturbances. Both Input-to-State Stability (ISS) and Input-to-State Safety (ISSf) are provided to theoretically guarantee the system’s stability and safety. CBF-based safety conditions are formulated as constraints inside a robust MPC strategy. Robust satisfaction of the constraints is ensured by tightening the state constraint set. With admissible disturbances under a certain bound, ISS and robust recursive feasibility are guaranteed by computing the terminal region and state constraint set. For obstacle avoidance, Input-to-State Safe Control Barrier Function (ISSf-CBF) is chosen to provide robust set safety for the dynamic systems under input disturbances, which always guarantees that states stay inside or close to the safe set. With the proposed method, the future state prediction is taken into consideration and optimal performance is accomplished via MPC, and the system’s safety is ensured via CBF. Numerical simulation results confirm the effectiveness and validity of the proposed approach.
Recurrent Neural Network-Based Model Predictive Control for Waypoint Tracking
Ying Shuai Quan,Woo Young Choi,Seung-Hi Lee,Chung Choo Chung 한국자동차공학회 2019 한국자동차공학회 부문종합 학술대회 Vol.2019 No.5
This paper presents an recurrent neural network-based model predictive control for an autonomous driving vehicle. Model predictive control is effective in vehicle lateral control but too computationally expensive to be applied in real-time control. To resolve this problem, we propose a recurrent neural network-based approximate model predictive control. The offline-trained neural network exhibits the ability to model the waypoint tracking system and provided the closed-loop performance. The performance of the approximate recurrent neural network-model predictive control (RNN-MPC) is validated by computational experiments of waypoints tracking control scheme.
Ying Shuai Quan,Jin Sung Kim,정정주 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.7
This paper presents a robust controller using a Linear Parameter Varying (LPV) model of a lane-keeping system with parameter reduction. Both varying vehicle speed and roll motion on a curved road influence the lateral vehicle model’s parameters, such as tire cornering stiffness. Thus, we use the LPV technique to take the parameter variations into account in vehicle dynamics. However, multiple varying parameters lead to a high number of scheduling variables and cause massive computational complexity. In this paper, to reduce the computational complexity, Principal Component Analysis (PCA)-based parameter reduction is performed to obtain a reduced model with a tighter convex set. We designed the LPV robust feedback controller using the reduced model solving a set of Linear Matrix Inequality (LMI). The effectiveness of the proposed system is validated with full vehicle dynamics from CarSim on an interchange road. From the simulation, we confirmed that the proposed method largely reduces the lateral offset error, compared with other controllers based on a Linear Time-Invariant (LTI) system.
Q learning-based Autonomous Valet Parking System
Ying Shuai Quan(전영수),Dae Jung Kim(김대정),Seung-Hi Lee,Chung Choo Chung 한국자동차공학회 2020 한국자동차공학회 부문종합 학술대회 Vol.2020 No.7
In this paper, we propose a brand new vehicle lateral motion control algorithm for autonomous parking system utilizing Q-learning algorithm. Normally for optimal vehicle control, linearization is introduced to deal with the nonlinearity of vehicle dynamics, which reduces the optimality of the derived control laws. To solve the problem, a Q-learning based vehicle parking control algorithm is proposed. A path planning method is introduced to the design of the state vector in the Q-learning algorithm for vehicle lateral control. Feasibility of the proposed algorithm is validated by computational simulation results showing satisfactory performances on the test scenario.
Robust MPC-CBF를 이용한 논홀로노믹 로봇의 장애물 회피
전영수(Ying Shuai Quan),홍정훈(Jeong Hun Hong),김진성(Jin Sung Kim),정정주(Chung Choo Chung) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.10
본 논문에서는 additive input disturbance가 존재하는 상황에서 논홀로노믹 로봇의 장애물 회피를 위한 Robust Model Predictive Control combined with Control Barrier Function (RMPC-CBF)를 제안한다. CBF는 로봇의 safety 조건을 만족하기 위해 사용되고, 이를 위해 Robust MPC의 constraint로 반영된다. 제안하는 방법은 CBF의 safety 조건을 만족하는 RMPC를 통해 예측된 모델의 상태가 최적화 된다. 또한, 본 논문에서는 제안하는 알고리즘의 stability를 보장하기 위해 Input-to-State Stability(ISS)의 이론을 통해 증명한다. 알고리즘의 유효성을 검증하기 위해 MATLAB을 이용하여 시뮬레이션을 수행하였고, 이를 통해 제안하는 알고리즘의 장애물 회피를 확인하였다.
Jin Sung Kim,Ying Shuai Quan,Chung Choo chung 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
This paper proposes the data-driven modeling and control method with the Koopman operator for the lane-keeping system. The vehicle can be modeled as a linear motion model but has underlying complicated nonlinear behavior. Thus, there exists a need to model the full vehicle dynamics effectively. To this end, we use the Koopman operator to express the full vehicle nonlinear dynamics as a linear structure. However, it is not practical to use the Koopman operator directly because it lies in infinite-dimensional space. Hence, we apply the extended dynamic mode decomposition to approximate the Koopman operator as a finite-dimensional linear operator. We conduct a comparative study between the linear model-based optimal control and the Koopman operator-based optimal control. As a result, it is observed that the proposed method reduces the system state by 20% compared to the linear model-based controller.