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The latest electric vehicles (EVs) are becoming increasingly complex and resource-intensive owing to their advanced functional capabilities and autonomy. In this study, EVs are considered as cyber-physical systems (CPS), and a novel event-triggered sliding mode controller (ET-SMC) with a hierarchical framework is developed to enhance lateral stability and reduce communication resource consumption in EVs through torque vectoring control (TVC). The high-level controller of the ET-SMC is designed based on a nonlinear vehicle model, and its stability is rigorously proven using the Lyapunov and input-to-state stable (ISS) theorems. Additionally, a traditional periodic sliding mode controller (SMC) is developed as a benchmark controller. The Sine-with-Dwell (SwD) test scenario, an internationally recognized method for verifying lateral stability, is employed to evaluate the control performance using a hardware-in-the-loop (HIL) test bench. The experimental results demonstrate that the ET-SMC can not only maintain similar control performance in enhancing lateral stability but also reduce controller communication resource consumption by approximately 30% compared to the periodic SMC in this typical driving scenario. These results show that the proposed control algorithm is promising for future EVs.

This article focuses on solving the optimal control problem for magnetorheological fluid (MRF)-based semiactive suspension (SAS) systems with input saturation and time-varying delay. A robust switched H$\infty$ method is proposed in this work based on the Takagi–Sugeno (T–S) fuzzy theory. A novel hybrid model that incorporates both the fluid flow mechanism (FFM) and hysteresis phenomenon model (HPM) is adopted to separate the passive and active components of the MRF damper. Under such a framework, the convex and reciprocally convex approaches are used to transform the features of input saturation and time-varying delay into linear matrix inequality conditions. Furthermore, a Lyapunov–Krasovskii function is employed to guarantee the stability of the MRF–SAS system subject to input saturation and time-varying delay. Finally, the effectiveness of the proposed method is validated via a numerical example of an MRF–SAS system. The results show that the proposed controller indicates good dynamic performance for the MRF–SAS system with input saturation and time-varying delay.

Offline reinforcement learning (ORL) has been getting increasing attention in robot learning, benefiting from its ability to avoid hazardous exploration and learn policies directly from precollected samples. Approximate policy iteration (API) is one of the most commonly investigated ORL approaches in robotics, due to its linear representation of policies, which makes it fairly transparent in both theoretical and engineering analysis. One open problem of API is how to design efficient and effective basis functions. The broad learning system (BLS) has been extensively studied in supervised and unsupervised learning in various applications. However, few investigations have been conducted on ORL. In this article, a novel incremental ORL approach with sparse broad critic approximation (BORL) is proposed with the advantages of BLS, which approximates the critic function in a linear manner with randomly projected sparse and compact features and dynamically expands its broad structure. The BORL is the first extension of API with BLS in the field of robotics and ORL. The approximation ability and convergence performance of BORL are also analyzed. Comprehensive simulation studies are then conducted on two benchmarks, and the results demonstrate that the proposed BORL can obtain comparable or better performance than conventional API methods without laborious hyperparameter fine-tuning work. To further demonstrate the effectiveness of BORL in practical robotic applications, a variable force tracking problem in robotic ultrasound scanning (RUSS) is investigated, and a learning-based adaptive impedance control (LAIC) algorithm is proposed based on BORL. The experimental results demonstrate the advantages of LAIC compared with conventional force tracking methods.

The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer’s disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

The electronically controlled semi-active air suspension systems have been widely used to improve the vehicle ride comfort and road holding performance by adjusting the damper stiffness. This work focuses on the design of a damping force controller to enhance the ride comfort, road holding and stability under the presence of unknown air spring pressure. Firstly, an improved skyhook suspension reference model is developed to generate the desired dynamic criteria (i.e. vehicle body acceleration, pitch and roll angles). Secondly, by employing the backstepping technique, a novel damping force controller is proposed to approach the desired dynamic criteria. Thirdly, a parameter estimation method is also designed to estimate the air spring pressure to obtain the air spring force. Comparative studies are carried out among passive suspension systems, and the semi-active air suspension systems with the proposed model reference damping force control, traditional backstepping control and existing sliding mode control. Numerical results show that a significant improvement of ride comfort can be observed with the semi-active air suspension system based on the proposed model reference backstepping controller.

The vehicle lateral stability control is a challenging problem due to the tire nonlinearity and the immeasurable sideslip angle. Thus, an adaptive model predictive control (AMPC) scheme based on interacting multiple model (IMM) vehicle sideslip angle observer is proposed in this paper. First, the observer is composed of the Extended Kalman filter (EKF) and the Unscented Kalman filter (UKF), which improves the real-time performance as well as the observation accuracy. Then, a multi-objective controller based on adaptive vehicle model prediction is designed using model predictive control algorithm. This controller aims to achieve a balance between the actuation and state constraints. The T-S fuzzy algorithm is used to observe the tire cornering stiffness and design an adaptive vehicle model. By utilizing the appropriate objective function and a quadratic programing solver, the controller output is obtained to achieve vehicle stability control. Finally, the effectiveness of the designed AMPC scheme under various working conditions is verified by Carsim-Simulink joint simulation platform and hardware-in-the-loop (HIL) test.