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Renewable energy penetration increases the power grid's operational uncertainty, threatening the economic effectiveness and reliability of the grid. In this article, we examine how uncertainty affects unit commitment (UC), a classical electricity market procedure. Stochastic programming has helped handle uncertainty for UC and performed well with distribution knowledge, but the lack of such information in practice deteriorates the effectiveness. Such a dilemma becomes more pronounced when dealing with joint chance constraints solely based on samples. To address this issue, we introduce statistical feasibility into UC and develop robust sample-based algorithms employing appropriate uncertainty sets to hedge uncertainty without distribution dependence. We also propose a learn-to-optimize acceleration method to convexify UC. Furthermore, we construct an optimization kernel to boost computational efficiency.

The rapid development of the active front-wheel steering technique makes it possible to pursue precise, fast, and reliable path-tracking control for autonomous vehicles. To mitigate the impact of time-varying model parameters and system disturbances on the control performance, this article proposes a continuous-time solving scheme for the autonomous path- tracking problem, which combines the robust model predictive control (RMPC) technique, non-parallel distributed compensation (non-PDC) method, and polytopic model. First, considering the time-varying longitudinal vehicle velocity, a polytopic model is utilized to construct a vehicle path-tracking system with a simpler structure. Then, a state-feedback controller for the continuous system model is developed based on the non- PDC method. By using the Lyapunov stability proof to transform the optimal control problem into the linear matrix inequalities (LMIs) solving form, an RMPC method is used to ensure path-tracking accuracy. Moreover, a robust compensation control is introduced to improve the anti-disturbance and transient response of the system. The virtual simulation by Carsim/ Simulink and field vehicle tests are conducted to assess the proposed controller. Both the simulation and field vehicle tests prove the effectiveness of the proposed controller in improving the path-tracking performance.

The direct yaw moment control system of distributed drive electric vehicles provides a flexible control scheme to enhance the vehicle stability through four independently driven in-wheel motors. However, the vehicle nonlinearities may lead to the invalidation of control. To this end, a Takagi–Sugeno (T-S) fuzzy-based robust H∞ control method is proposed to ensure the vehicle performance while addressing the nonlinear challenge. First, thanks to the T-S fuzzy modeling technology, the tire nonlinear characteristics are described by fuzzy rules, based on which the vehicle lateral dynamics model is established. Next, the safety region represented by the tire slip angles phase plane is presented to evaluate the vehicle stability performance. Considering the control priority of vehicle handling performance and stability control with different stability margins, a multiobjective optimization function is transformed into a standard robust performance optimization problem with dynamic weight coefficients. A T-S fuzzy-based robust H∞ state feedback controller is then designed to ensure the system stability and H∞ performance. Finally, the hardware-in-the-loop tests are conducted to validate the proposed controller. Comparative results show the effectiveness to improve the vehicle handling performance while ensuring the stability.

Human-machine mutual trust has become one of the important factors restricting steer-by-wire vehicles due to the removal of mechanical connections. To this end, this article proposes a hierarchical shared steering control framework based on the human-machine mutual trust evaluation. The upper level of the framework aims to evaluate the human-machine mutual trust level. The driver's trust level in the machine is evaluated by the human-machine steering difference. And the machine's trust level in the driver is evaluated by driver skills. The lower level is to dynamically optimize the authority allocation considering varying human-machine mutual trust states. The fuzzy method is adopted to calculate the reference value of the human-machine authority based on the mutual trust level. Through minimizing the lateral acceleration and tracking error, the fuzzy rule database for the authority reference level is further tuned offline. Furthermore, to improve the smoothness of authority transfer and path tracking, the model prediction control is used to optimize the human-machine authority levels online. The proposed authority allocation strategy is verified with the driver-in-the-loop test bench. The results show the effectiveness of improving driving performance under different human-machine mutual trust levels.

Independently driven electric vehicles (IDEVs) with typical redundancy characteristics provide more potential to improve global performance. However, the model uncertainties would cause extra energy consumption and even destroy vehicle safety. This study presents a hierarchical control scheme to simultaneously guarantee the vehicle energy conservation and handling stability. In the upper layer, the tire nonlinear characteristics are reconstructed by the polytopic method to facilitate the controller design. Then a robust controller is developed to address the model uncertainties and ensure the vehicle handling stability by integrating the direct yaw moment control (DYC) system and the active front wheel steering (AFS) system. Furthermore, the stability performance of independently driven electric vehicles is analyzed and quantified with different regions in the tire slip angles phase plane. Considering the uncertain longitudinal velocity, the lower layer utilizes a linear time-varying model predictive controller (LTV-MPC) to develop the torque allocation strategy and realize energy conservation. The control authority for stability performance and energy conservation is dynamically adjusted by assessing the vehicle safety state. Hardware-in-the loop (HIL) tests are carried out to verify the effectiveness. The test results demonstrate the proposed method is feasible to balance different control objectives.

With the popularity and increasing demand of new energy automotive, four-wheel independent drive electric vehicles (4WID-EVs) and its distributed control mode is promising to be a better chassis architecture for its flexibility and maneuverability. However, due to the uncertainty of the vehicle internal time-varying parameters and imprecise fault detection method, a novel robust fault estimator-based stochastic model predictive control (SMPC) is proposed in this article to ensure the vehicle lateral motion control and track the desired longitudinal velocity considering the estimation error of motor failure degree. Firstly, a robust H-infinity fault estimator with a linear parameter-varying (LPV) model is established to estimate the real-time fault signal considering the external disturbance and parameter perturbation (i.g. longitudinal and lateral vehicle speed). Then, a SMPC controller framework is developed to alleviate the effect of inevitable evaluated estimation error while the vehicle lateral safety and vehicle speed tracking can be ensured. Finally, the hardware-in-loop (HIL) tests are conducted to prove the feasibility of SMPC controller. The experiment results indicate that the proposed controller framework can effectively preserve vehicle stability and keep vehicle in a healthy state with faster response compared with the traditional fault-tolerant controller.