Robust Lateral Motion Control of Electric Ground Vehicles With Random Network-Induced Delays

Direct Yaw-Moment Control of 4-Wheel-Independent-Drive Electric Vehicle with Network-Induced Delays through Data-reduction Techniques

“…As shown in Figure 1, a two-degree-of-freedom (2-DOF) vehicle model is used in the paper, where CG is the center of gravity; m is the vehicle mass; I z is the vehicle yaw inertia; M z is the yaw moment; F xf and F xr are the longitude tire forces of front and rear wheels, respectively; F yf and F yr are the lateral tire forces of front and rear wheels, respectively; α f and α r are the slip angle of front and rear wheels, respectively. With the 2-DOF vehicle model, the state space formulation of controloriented vehicle lateral dynamics model for DYC of 4WID-EV is expressed as follows [6][7][8]: C r and c f are the cornering stiffness of the rear and front tires, respectively. In vehicle lateral motion control, the desired sideslip angle is generally selected to be zero to ensure vehicle stability, while the desired yaw rate is usually defined to ensure good handling performance.…”

“…In vehicle lateral motion control, the desired sideslip angle is generally selected to be zero to ensure vehicle stability, while the desired yaw rate is usually defined to ensure good handling performance. A widespread expression of the desired yaw rate is described as [6][7][8] In this paper, only the case that the network induced time delays are less than the control cycle period T s , which commonly occurs in most networked control systems, in the network control system (NCS) of DYC for 4WID-EV, the sensor node periodically samples the vehicle states with fixed period T s , the controller node and the actuator node operate in event-driven mode which means a task will be immediately implemented once a message arrives via CAN, and the task implementation time in each node is ignored. Without considering network-induced delays, the NCS of DYC for 4WID-EV runs like an ideal centralized control system with fixed sampling period .…”

Direct Yaw‐Moment Control of All‐Wheel‐Independent‐Drive Electric Vehicles with Network‐Induced Delays through Parameter‐Dependent Fuzzy SMC Approach

“…Equipped by advanced electric motors with more accurate and quicker torque generations than internal combustion engine (ICE) and hydraulic braking systems, AWID-EVs have obvious advantages in terms of direct yaw-moment control (DYC) through flexible differential driving/braking functions over traditional centralized drive vehicles [1,[3][4][5][6]. Plenty of existing studies have focused on the more flexible DYC and its integration control with active steering for AWID-EVs [2,[7][8][9][10][11][12][13]. However, considering the presence of model uncertainties, system parameter variations and external disturbances such as road rough or wind gust, it is one of the most principal issues for AWID-EVs to ensure the robustness of DYC.…”

“…However, in a modern AWID-EV, the control signals from controllers and the measurements from sensors are usually exchanged through an in-vehicle communication network, for example, controller area network (CAN) or FlexRay [2]. In other words, a modern AWID-EV is a networked control system (NCS) rather than a conventional centralized control system [2,7,9,10]. Thus, the network-induced delays cannot be ignored.…”

Co-Design Based Lateral Motion Control of All-Wheel-Independent-Drive Electric Vehicles with Network Congestion

“…With driving motors, each wheel of the AWID-EV can individually generate not only driving torque but also braking torque, which greatly increases the flexibility and possibility of fully utilizing the adhesion of each tire and the efficiency of each motor. Therefore, the AWID-EV has considerable advantages in terms of energy optimization, drivability and driving safety [6,14,15]. congestion problem in the lateral motion control of AWID-EVs has not been addressed in the literature by other researchers.…”