This study presents an integrated multi-objective controller for electric vehicles ( EVs) to achieve four main control objectives concurrently, i.e. slip control in traction and braking, lateral stability control, handling improvement, and rollover prevention. The model-based integrated controller provides a centralised structure to improve the overall stability of the vehicle. The proposed controller considers the interactions of different stability objectives and delivers an optimal solution by including all objectives, simultaneously. The main stability issues in vehicle dynamics including slip control in traction and braking, lateral stability, handling improvement, and rollover prevention are integrated and incorporated in this controller. The stability objectives are realised through proper distribution of torques on four independent wheels of EVs. The unified integrated controller is developed using the Model Predictive Control (MPC) approach. Barrier functions are employed to confine the state variables for slip control, lateral stability, and rollover prevention within the handling limits and safe regions of manoeuverability. Simulation results show that the proposed integrated controller can be effectively used for control of different stability and safety issues of EVs in different situations.

Recent advances in active safety systems provide new opportunities to handle rollover problems more efficiently. This paper presents a Model Predictive Control (MPC) strategy for rollover prevention through active front steering. A new Rollover Index (RI) and a specified vehicle model are proposed to realise an effective rollover prevention approach. The new RI is represented by vehicle parameters and state variables so that it can be readily incorporated into the MPC objective function. Using a barrier cost function, the proposed MPC controller limits vehicle maneuvers within a safe region of rollover without sacrificing vehicle maneuverability under normal conditions. The roll dynamics, the tire nonlinearity, the lateral load transfer, and the road bank are considered for vehicle modelling to provide appropriate actions at the threshold of a rollover. An affine tire model is used to handle tire nonlinearity in the MPC structure. A high-fidelity CarSim model for an SUV is employed to evaluate the proposed RI and the MPC controller. Simulation results show the reliability of the proposed RI and the effectiveness of the proposed rollover prevention controller for different rollover situations.

This study introduces a novel control structure for integrated vehicle stability control which can be reconfigured for a wide range of vehicles with different types and combinations of control actuation. The proposed structure can be easily adjusted for a specific vehicle as it enables any actuator to be readily added or removed without reformulating the control problem. Different types and combinations of control actuation can be selected for the control design including differential braking and torque vectoring on all or selected wheels as well as active front or rear steering. To develop this model-based reconfigurable control structure, vehicle modeling is provided in a matrix form and an actuator reconfiguration matrix is defined. The proposed structure realizes integrated lateral stability control and slip control in traction/braking. The Model Predictive Control (MPC) approach is used to develop the controller. By employing the reconfigurable structure, optimal control solutions can be achieved with respect to only available control actuations. Simulation results show that the proposed control structure can facilitate the design of stability control regardless of the actuators configuration.

Small electric vehicles, also known as urban vehicles, have been introduced to alleviate traffic congestion, energy consumption, and pollution due to their smaller sizes, higher maneuverability, and lower fuel consumption. The main objective of this study is to develop a unified control structure that can be reconfigured for stability control of different configurations of urban vehicles with flexible combinations of control actuation. The reconfigurable controller can be readily adjusted for Tadpole and Delta three-wheeled vehicles as well as four-wheeled vehicles without reformulating the problem. Different types and combinations of actuators can be selected for the control design including differential braking, torque vectoring, active front and rear steering, and active camber system. The proposed structure integrates the lateral stability control, the rollover prevention, and the longitudinal slip control in traction/braking. The reconfigurable controller is developed using the model predictive control approach. Simulation results show that the introduced controller can be effectively used for stability control of different configurations of urban vehicles, and facilitates their control design.

In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

This paper proposes an N-link snake robot with parallel elastic actuators (PEA) and a stiffness property that reduces the energy consumption during fast movement. Each PEA couples the actuator output to a spring element, enhancing the periodic gait as demonstrated in previous studies of walking robots. The PEA can mimic each joint of a typical snake robot, which is also controlled by sinusoidal periodic signals. If the actuator output is synchronized with the resonance frequency of the spring, the PEA can greatly reduce the actuator torque and power. Therefore, the present paper proposes PEAs to reduce the energy consumption of N-link snake robots. After analyzing the robot dynamics, we explain how the torque of each joint is minimized. The energy consumption is evaluated by measuring the motor current in experimental tests. During fast movement of the robot, the PEA reduced the current consumption by 72%.