Rongrong Yu

A parallel parking strategy of the uncertain autonomous vehicle is presented. In the actual driving environment, due to the influence of unpredictable factors such as uneven road surface and climate change, there is uncertainty in the process of parallel parking of the autonomous vehicle. The uncertainty which may change with time is bounded, yet the bound is unknown. In order to achieve accurate parallel parking and collision avoidance of the autonomous vehicle, the uncertainty analysis is added into the autonomous vehicle system. Firstly, the dynamic model of the uncertain autonomous vehicle is determined based on the hierarchical constraint approach, and the analytical form of the dynamic model is obtained. Then, a self-regulating leakage type adaptive robust control based on the Udwadia-Kalaba method is proposed. Finally, the applicability and superiority of the parking strategy are demonstrated by comparing with other control methods.

In order to achieve the stability and safety of the autonomous vehicle while reducing the control cost, a driving strategy for uncertain autonomous vehicle is presented. There are many uncertainties in the autonomous vehicle driving, and the boundary of the uncertainty maybe unknown. To solve this problem, a gated leakage type adaptive robust control based on the tracking deviation is developed. The salient feature of the control lies in the novel leakage mechanism designs. The leakage mechanism is designed to provide gated value for the leakage to prevent excessive control effort. The control system ensures the performance of the autonomous vehicle in terms of lateral and yaw displacement, which in turn prevents the vehicle from sideslip even in uncertainty. Compared with the constant leakage type adaptive robust control and Linear Quadratic Regulator (LQR) control, the effectiveness and superiority of the proposed control method are verified

Under the theoretical support of fuzzy sets theory and confidence index, the proposed approach involves the development of a high‐order robust control method that incorporates optimization techniques to address uncertain systems. First, the utilization of fuzzy sets theory is employed to establish a fuzzy dynamical model. Second, the design of a high‐order robust controller is performed. Third, the design parameters are optimized based on confidence index, and the values are found that minimize the control cost. The simulation results show that this high‐order robust control has significant advantages in dealing with uncertainty, as well as minimizing the control cost. It can be ensured the uniform boundedness and uniform ultimate boundedness. Thus, this proposed method offers the advantage of achieving both robustness and optimality simultaneously.

In order to improve the control performance of the Cyber-Physical Systems (CPSs), an integrated modelling-control-design trio framework is established in this paper. In the modelling part, CPS has two components, cyber component and physical component, so the system may be subject to (possibly fast) time-varying cyber interference and (possibly fast) time-varying physical uncertainty. A dynamic model encompassing these two phases of the CPS is established. In the control part, a novel control design is proposed based on the dynamic model. The problem of constraint-following for CPS operating under cyber interference and physical uncertainty is considered. In the design part, the choice of control parameters is investigated. This procedure consists of two stages. The first stage is to design a control scheme based on feasible control design parameters, so that it can guarantee the performance in the case of both cyber interference and physical uncertainty. The second stage is to seek the optimal design among the feasible control design parameters, which is resolved by an intelligent multi-agent game-theoretic approach. We invoke both Nash game and Stackelberg strategy to choose the optimal parameters. Interestingly, the optimal parameters obtained from different game settings are the same. This shows the conception of optimality we established spans in a broader context. The robustness and superiority of the system performance are demonstrated in the intelligent transportation system.