This paper develops a novel adaptive $H_{\infty }$ control scheme for adaptive cruise control (ACC) system via the data-driven learning. In the proposed technique, a continuous time ACC system is first constructed with unknown system dynamics. To estimate the unknown system dynamics, an adaptive estimator is then formulated utilizing the vectorization and Kronecker's products operations, enabling the reconstruction of the unknown system dynamics through the detectable input/output information. An adaptive law is utilized to ensure the convergence of the estimated parameters. Moreover, a data-driven learning technique is employed to resolve the constructed Riccati equation in an online manner. To accomplish this, the Riccati equation is reformulated via the Kronecker's products by using another adaptive law, whose convergence can be also effectively guaranteed. Unlike existing neural network-based approximate dynamic programming (ADP) algorithms, the data-driven learning scheme proposed in this paper does not involve neural network approximation errors, so the solution is relatively more accurate. Finally, simulation and experimental verification results are provided to verify the effectiveness of the presented $H_{\infty }$ control and data-driven learning algorithm.

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the Kalman filter, especially the standard unscented Kalman filter (UKF) that has been widely used in vehicle state estimation because of its superiority in dealing with nonlinear filtering problems. However, although the UKF assumes that the noise statistics of the system are known, due to the complex and changeable operating conditions, sensor aging and other factors, these noises vary. In order to realize high-precision vehicle state estimation, a noise-adaptive UKF algorithm is proposed in this article. The maximum a posteriori (MAP) algorithm is used to dynamically update the noise of the vehicle system, and it is embedded into the update step of the UKF to form an adaptive unscented Kalman filter (AUKF). The system will dynamically update the noise when noise statistics are unknown and prevent filter divergence by adjusting the mean and covariance of the estimated noise to improve accuracy. On this basis, the proposed method is verified by the joint simulation of CarSim and Matlab/Simulink, confirming that the AUKF performs better than the standard UKF in estimation accuracy and stability under different degrees of noise disturbance, and the estimation accuracy for the yaw rate, side slip angle and longitudinal velocity is improved by 20.08%, 40.98% and 89.91%, respectively.

Aiming at the impact of heat generation and temperature rise on the driving performance of a permanent magnet (PM) motor, taking the PM in-wheel motor (IWM) for electric vehicles as an object, research is conducted into the temperature distribution of the electromagnetic–thermal effect and cooling structure optimization. Firstly, the electromagnetic–thermal coupling model considering electromagnetic harmonics is established using the subdomain model and Bertotti’s iron loss separation theory. Combined with the finite element (FE) simulation model established by Ansoft Maxwell software platform, the winding copper loss, stator core loss and PM eddy current loss under the action of complex magnetic flux are analyzed, and the transient temperature distribution of each component is obtained through coupling. Secondarily, the influence of the waterway structure parameters on the heat dissipation effect of the PM-IWM is analyzed by the thermal-fluid coupled relationship. On the basis, the optimization design of waterway structure parameters is carried out to improve the heat dissipation effect of the cooling system based on the proposed chaotic mapping ant colony algorithm with metropolis criterion. The comparison before and after optimization shows that the temperature of key components is significantly improved, the average convection heat transfer coefficient (CHTC) is increased by 23.57%, the peak temperature of stator is reduced from 95.47 °C to 82.73 °C, and the peak temperature of PM is decreased by 14.26%, thus the demagnetization risk in the PM is improved comprehensively. The research results can provide some theoretical and technical support for the structural optimization of water-cooled dissipation in the PM motor.

Although optimal control with full state-feedback has been well studied, online solving output-feedback optimal control problem is difficult, in particular for learning online Nash equilibrium solution of the continuous-time (CT) two-player zero-sum differential games. For this purpose, we propose an adaptive learning algorithm to address this trick problem. A modified game algebraic Riccati equation (MGARE) is derived by tailoring its state-feedback control counterpart. An adaptive online learning method is proposed to approximate the solution to the MGARE through online data, where two operations (i.e., vectorization and Kronecker’s product) can be adopted to reconstruct the MGARE. Only system output information is needed to implement developed learning algorithm. Simulation results are carried out to exemplify the proposed control and learning method.

Conventional ACC method has great fluctuation and deviation when solving speed and distance control problems. Thus, this paper develops a reinforcement learning (RL) based robust optimal prescribed performance controller for ACC systems. To this end, we first construct a continuous time ACC system with unknown system dynamics (e.g., target vehicle acceleration, sensor and actuator attacks, etc). To estimate the unknown system dynamics, an unknown system dynamic estimator (USDE) is designed, where the unknown system dynamic can be accurately estimated by using the input-output information, this is helpful for controller design. Then, a RL based optimal control method is developed, where the prescribed performance function (PPF) is applied, the system states can be effectively defined within a certain range. To realize the online solution for optimal control, we design a new adaptive law based on the adaptive dynamic programming (ADP) framework to online learn the critic neural network (NN) weights, because of the strong convergence, the proposed learning algorithm can be effectively applied in practical industrial systems. Finally, the efficacy of the proposed control technique is tested through simulations and experiments.