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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.

By possessing a good capacity for energy absorption and a lightweight structure, the negative Poisson's ratio (NPR) structure has very fine prospects for application in vehicle engineering. By combining the traditional side door impact beam and a NPR structure, a novel side door NPR impact beam is first proposed in this work to improve the side impact crashworthiness in automobiles. The performance of the side door NPR impact beam is first studied in detail by comparison with a traditional side door impact beam and aluminum-foam-filled impact beam. To make further improvement on the performance of the side door NPR impact beam, the multi-objective design optimization while considering reliability is also investigated in this work. The parametric model of the NPR structure is established to improve modeling efficiency when the shape and topology are changed. A Latin hypercube sampling technique, orthogonal design, and a response surface model are then combined to construct the surrogate models. A radial-based importance sampling technique (RBIS) and multi-objective particle swarm optimization algorithm (MOPSO) are applied in the inner and outer loop respectively to find the optimal multi-objective reliability solutions. The results indicate that the side impact crashworthiness is improved remarkably by the side door NPR impact beam and the structure is further improved by the multi-objective reliability optimization. The studies in this work also serve as a good example for other improvements in automobile performance. [ABSTRACT FROM AUTHOR]

The precise estimation of the battery's state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation-adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge. [ABSTRACT FROM AUTHOR]

The resistance of cancer to chemotherapeutic agents is a major obstacle during chemotherapy. Clinical multidrug resistance (MDR) is commonly mediated by membrane drug efflux pumps, including ATP-binding cassette subfamily B member 1, also termed P-glycoprotein (P-gp). P-gp is a membrane transporter encoded by the MDR1 gene. The current study aimed to investigate the impact of psoralen on the expression and function of P-gp. The 10% inhibitory concentration (IC10) of psoralen, and its capacity to reduce MDR in adriamycin (ADR)-resistant MCF-7/ADR cells were determined using MTT assay. The ability of psoralen to modulate the transport activity of P-gp in MCF-7/ADR cells was evaluated by measuring the accumulation and efflux of rhodamine 123 (Rh 123) and adriamycin with flow cytometry. The present study evaluated the mRNA level of MDR1 in MCF-7 and MCF-7/ADR cells treated with psoralen using reverse transcription-quantitative polymerase chain reaction. The protein expression level of P-gp was examined by western blot analysis. The current study demonstrated that the IC10 of psoralen in MCF-7/ADR cells was 8 µg/ml. At 8 µg/ml, psoralen reduced MDR and the sensitivity of the MCF-7/ADR cells to ADR compared with untreated cells. Additionally, psoralen significantly increased the intracellular accumulation of ADR and Rh 123. However, the IC10 of psoralen did not affect the protein expression levels of P-gp or mRNA levels of MDR1 (P>0.05). Psoralen reduces MDR by inhibiting the efflux function of P-gp, which may be important for increasing the efficiency of chemotherapy and improving the clinical protocols aiming to reverse P-gp-mediated MDR. [ABSTRACT FROM AUTHOR]

Dielectric elastomer is one of the most concerned type of electroactive polymer. When a membrane of dielectric elastomer is subject to a voltage on both sides, the membrane reduces thickness and expands area, and vice versa. Due to this unique behavior, dielectric elastomer can be applied as active structure, actuator, sensor, energy harvesting component, etc. However, the mechanical performance of active structure is limited by the low Young’s modulus of dielectric elastomer. Inspired by the human’s bone–muscle system, a novel active structure that applying dielectric elastomer on the double-V type Negative Poisson’s ratio (NPR) structure was proposed in this paper. When voltage and load vary, mechanical performance and dimensions of proposed Electroactive NPR (ENPR) structure change. The electro-mechanical model of ENPR structure was established from the perspective of Helmholtz free energy. And the theoretical model was verified by the experiment of an ENPR structure prototype. Furthermore, the influences of dimensions of dielectric elastomer and structural parameters on the electro-mechanical performance were researched. The results indicated that all variables influenced the stiffness of ENPR structure. Width number of cells and number of layers had limited effects on the electroactive response, but tuning of others variables which can improve stiffness will weaken the electroactive response. Through periodic replication of ENPR cells, the electro-mechanical property can be superimposed to satisfy higher bearing requirements and larger size demands. ENPR structure can realize integration, electronization, informatization and intellectualization of elastic component, sensor, actuator, and energy harvesting system theoretically, which is significant and can be expected to be widely applied on mechanical systems. [ABSTRACT FROM AUTHOR]