Search

Abstract This study presents a tracking and fault-tolerant controller architecture for uncertain steer-by-wire (SbW) systems using model predictive control in the presence of actuator malfunction and the nonlinear properties of tire lateral stiffness coefficients. By changing the internal model, the model predictive control (MPC) technique was used to achieve optimal tracking performance under the actuator output limitation variation problem and uncertain system parameters. System parameters and state estimates were simultaneously provided by the fault detection and isolation modules to detect actuator failure using the coupling estimation approach. The estimation accuracy was further improved by considering the replacement errors as virtual noise, which was also estimated during the estimation process. Simulation and experimental results demonstrate that the proposed fault-tolerant control technique can identify motor faults and conduct fault-tolerant control based on fault identification, showing good front-wheel steering angle tracking performance under both normal and fault conditions.

Abstract This paper presents an energy-efficient control strategy for electric vehicles (EVs) driven by in-wheel-motors (IWMs) based on discrete adaptive sliding mode control (DASMC). The nonlinear vehicle model, tire model and IWM model are established at first to represent the operation mechanism of the whole system. Based on the modeling, two virtual control variables are used to represent the longitudinal and yaw control efforts to coordinate the vehicle motion control. Then DASMC method is applied to calculate the required total driving torque and yaw moment, which can improve the tracking performance as well as the system robustness. According to the vehicle nonlinear model, the additional yaw moment can be expressed as a function of longitudinal and lateral tire forces. For further control scheme development, a tire force estimator using an unscented Kalman filter is designed to estimate real-time tire forces. On these bases, energy efficient torque allocation method is developed to distribute the total driving torque and differential torque to each IWM, considering the motor energy consumption, the tire slip energy consumption, and the brake energy recovery. Simulation results of the proposed control strategy using the co-platform of Matlab/Simulink and CarSim® demonstrate that it can accomplish vehicle motion control in a coordinated and economic way.

With the increasing prominence and complexity of environmental noise problems, there is an urgent need for novel acoustic materials to meet noise reduction requirements. In this paper, two auxetic microstructures and three typical lattice microstructures are established, and a microscopic-macroscopic acoustic performance study scheme is established through the Johnson-Champoux-Allard-Pride-Lafarge (JCAPL) model. Auxetic-BCC porous materials have lower acoustic resistance and reactance amplitudes than many typical porous materials through experimental verification and comparative analysis of numerical calculations. The porous material's thickness and the backing cavity's thickness have similar effects on sound absorption performance when controlling a single change in structural parameters, and there is an optimum thickness. As acoustic resistance and reactance are dominant at low and high frequencies, respectively, noise reduction is better at low (high) frequencies than at minor (large) porosity, and the porosity of 76.4%–82.0% has the best sound absorption effect. Changes in prism length of materials with high porosity are more sensitive than those with low porosity, so the prism length with porosity of 76.4%, 79.0%, and 82.6% shall be designed to be less than 1.1 mm, 0.9 mm, 0.8 mm, respectively. This study provides theoretical guidance for designing multifunctional porous materials in extreme environments.

In this paper, a path tracking control for active rear steering vehicles considering driver steering characteristics is proposed to help drivers track the desired vehicle trajectory in a tailored way. Firstly, the driver model is established, and based on the driver steering data, the driver parameters are identified by the least square method. Based on driver model parameter identification, driver and vehicle models are formed for the MPC controller design. In MPC controller design, global objective function, considering the task performance, vehicle stability, driver's physical workloads, mental workloads and actuator performance, is designed to optimize the overall performance of the driver-vehicle system. On the constraint, the driver operating limit, actuator physical limit and vehicle stability performance limit are also considered to make the controller feasible to in the real practice. Simulation results under different conditions show that the controller can significantly improve the system performance and reduce the driver's workloads.

In recent years, connected and automated vehicles (CAV) have been a transformative technology that is expected to reduce emissions and change and improve the safety and efficiency of the mobilities [...]

A novel electro-active bushing (EAB) design is proposed based on the unique physical properties of dielectric elastomers. A dielectric elastomer is applied as the tendon layer to a circular two-dimensional double-V auxetic structure. When load and voltage excitation vary, mechanical performance and dimensions of dielectric elastomers change, which empowers EAB to realize an electrostrictive phenomenon and real-time variable stiffness under different voltages. An EAB structure can endure mechanical loads along all directions in a cross-sectional plane, which is more practical than our previous design which can only bear loads along one direction. A theoretical electromechanical model of an EAB unit cell was proposed based on the physics theory of dielectric elastomers. The influences of unit cell parameters on electromechanical responses were researched. Moreover, an EAB prototype was fabricated via the 3D printing method. The experiments using EAB prototype applied 3M VHB 4910 and 3M VHB 9473 acrylic membranes prestretched by 200% and 300%, respectively, were conducted. The results indicated that the electrostrictive displacement increases as the load increases, and the stiffness decreases with the rise in voltage. Greater stiffness is obtained by higher original thickness and prestretch rates, but the electrostrictive displacement and stiffness variation under high excitation was dramatically affected.

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.