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To ensure trajectory tracking accuracy and driving safety, this paper presents a real time nonlinear model predictive control (NMPC) method for autonomous guided vehicles (AGVs) to avoid dangerous load transfer effect in carrying heavy equipment. The AGV with a four-wheel-steering four-wheel-driving (4WS4WD) structure is controlled with a steer and velocity input for each wheel. The tracking problem is implemented by the standard NMPC framework and the referenced trajectory is planned by the Reeds-Shepp algorithm. A Large load creates a considerable load transfer effect which is dangerous for the wheels; hence, the controller limits the lateral acceleration to reduce load transfer. To realize online numerical computation for NMPC, the continuation/generalized minimal residual (C/GMRES) method is applied. A semismooth transformation based on the Chen-Harker-Kanzow-Smale (CHKS) function is introduced to handle inequality constraints since C/GMRES only solves standard equality forms. Trajectory tracking simulations are carried out in contrast to the quadratic programming method without lateral load transfer optimization, and practical experiments on an AGV carrying 13 tons of steel are also performed. The results indicate the accuracy, efficiency, safety and applicability of the proposed method on heavy-duty AGVs. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive sliding mode (ASM) controller for improving vehicle maneuverability and lateral stability of steer-by-wire (SBW) vehicles. The proposed stability control algorithm is composed of the following three remarkable characteristics: 1) As the vehicle sideslip angle which is very useful in vehicle stability control is often unavailable or unmeasurable for practical applications, a sliding mode-based state observer is proposed to online estimate the actual sideslip angle via the yaw rate and the lateral acceleration measurements. 2) ASM lateral stability controller, as an upper level controller, is designed to calculate the corrected steering angle for driving both the yaw rate and the sideslip angle to achieve their desired values in the presence of modelling uncertainties and external disturbances. Moreover, an adaptive law is incorporated in the control law to estimate the switching gain such that the complex uncertainty bound information can be avoided. 3) The desired steering angle is realized by a lower steering controller via ASM for an SBW system. The hardware-in-the-loop simulation results demonstrate the excellent stability control performance of the proposed control for different steering maneuvers. [ABSTRACT FROM AUTHOR]

To improve the maneuverability and stability of a vehicle and fully leverage the advantages of torque vectoring technology in vehicle dynamics control, a finite-time yaw rate and sideslip angle tracking controller is proposed by combining a second-order sliding mode (SOSM) controller with the backstepping method in this paper. However, existing research indicates that first-order sliding mode (FOSM) control suffers from the chattering problem, while the traditional SOSM controller requires knowing the bound of the uncertain term in advance to obtain the switching gain, which is difficult in practice. To address these problems, this paper proposes an adaptive second-order sliding mode (ASOSM) controller based on the backstepping method by adding the high-frequency switching term to the first derivative of the sliding mode variable, which implies that the actual control can be acquired after an integration process. The switching gain in the ASOSM controller is obtained by an adaptive algorithm without knowing any information of the uncertainty. The proposed algorithm is compared with FOSM and SOSM in different scenarios to demonstrate its applicability and robustness. Simulation results show that the bandwidth of the vehicle transient response can be improved by 21%. In addition, ASOSM and SOSM controllers are insensitive to vehicle mass and tire type, implying their robustness to such disturbances. Furthermore, ASOSM requires less control action because of the adaptive law when it performs similarly with SOSM and FOSM. [ABSTRACT FROM AUTHOR]

In this article, a robust control scheme for an in-wheel-motor-drive electric vehicle (IWMD EV) is put forward to enhance vehicle lateral stability considering network-induced time delays. A robust sliding mode controller (RSMC) is devised, and the derived control law is partitioned into two portions, i.e., the continuous and discontinuous parts. A Linear-Quadratic-Regulator (LQR) problem with network-induced time delays is formulated with the objectives of minimizing the reference states tracking errors and reducing the control efforts. Then, it is transformed into an iterative solution derivation of a two-point boundary value problem without delays, and the derived solution is obtained and constitutes the continuous part of the control law. Meanwhile, the global sliding mode theory is applied to deriving the discontinuous part of the control law, which has robustness to vehicle parameters variation and modeling uncertainties. The proposed control scheme exhibits better performance in dealing with network-induced time delays compared with the original optimal LQR controller in simulation and Hardware-in-the-Loop (HIL) tests. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system improves the handiness of steering by torque difference between two-side wheels. However, the DDAS and the vehicle stability control (VSC) will interfere with each other due to the same actuators. This paper proposes a hierarchical coordination control approach for the DDAS and VSC. The system is divided into four layers: the parameter estimation layer, the control region division layer, the coordination decision layer, and the control allocation layer. In the control region division layer, the stable area is firstly determined on the $\beta - \dot{\beta }$ phase portrait, and the coordinated control region is obtained by applying the simulated annealing algorithm inwardly and outwardly to the boundary of the stable area. In the coordination decision layer, the direct control strategy of steering hand wheel torque is used to establish the controller of the DDAS based on the fuzzy adaptive PID algorithm. At the same time, the controller of yaw moment is established based on the sliding mode control theory. In the stable region, the DDAS works separately. The VSC works separately in the unstable region. In the coordination region, the Sigmoid function is used to adjust the weight coefficient of the DDAS dynamically. Besides, the control allocation layer includes torque distribution controller and vehicle slip ratio controller. Finally, the vehicle model is established in CarSim, the control strategy, and the wheel motor model are established in Simulink. The joint simulation results of several typical conditions show that the designed control strategy can improve the handling stability of the vehicle effectively while DDAS working even in severe driving condition. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control strategy to improve the stability performance for four-wheel independent drive electric vehicles in critical cornering by introducing the supervision mechanism for yaw moment control and slip ratio regulation simultaneously. The proposed control strategy is a three-level structure: the first level is composed of a yaw moment controller and a longitudinal velocity controller; the second level is made up of a tyre force distributor; and the third level consists of a slip ratio regulator. To begin with, the supervision mechanism is developed to supervise vehicle driving states and determine whether the yaw moment controller should be activated. After that, the tyre force distributor is presented to assign the resultant force/moment generated by the first level to the four independent wheels, in which average distribution, dynamic load distribution, and optimal distribution schemes are separately discussed for comparison. Also, the slip ratio regulator is designed to regulate the slip ratio within the threshold. In the end, simulations performed on a nonlinear vehicle model with eight degree of freedom validated the effectiveness of the proposed control strategy and approaches. [ABSTRACT FROM AUTHOR]

This paper presents a coupling control strategy of force and displacement for an electric differential power steering (EDPS) system to improve steering maneuverability and handling stability of electric vehicles with motorized wheels. In order to investigate mutual influences between vehicles and drivers, models of the EDPS system, the vehicle, the tire as well as the actuate motor are introduced. Then, by analyzing key factors that affect the interaction between vehicles and drivers, the optimum hand wheel torque of the EDPS system is developed and actualized by torque difference between two front wheels based on the ${\text{H}}_{2}{\text{/ H}}_{{{\infty }}}$ control method. In addition, a yaw rate control method is schemed to diminish the yaw moment interference to vehicles caused by the torque difference between two front wheels, and μ-synthesis theory is applied here to improve the yaw rate tracking performance as well as attenuate influences of system uncertainties and disturbances. Finally, simulations by MATLAB/Simulink and hardware-in-the-loop experiments based on dSPACE are conducted and effectiveness of the proposed control strategy is demonstrated by simulation and experiment results and numerical analyses. [ABSTRACT FROM AUTHOR]

An integrated chassis control framework that consists of a basic chassis controller and a torque vectoring controller is designed for a three-axle electric bus with distributed motor-driven and active rear steering subsystems. In the basic chassis controller, the active speed limiting control is integrated for antisideslip and antirollover purposes, and the interaxle torque distribution ratio is optimized for energy economy. Meanwhile, the active rear steering control is designed for the tire-wear coordinating purpose. In the torque vectoring controller, the model-based motion control algorithm based on the holistic cornering control method is designed, by which a torque increment is generated at each wheel to change the plane motion states of the vehicle. To solve the optimal torque increment vector, a real-time constrained quadratic programming problem is formulated. The constraints related to the wheel torque limits, the tire friction limits, and the anti-wheel-slip requirements are constructed and converted as the upper and lower bounds of the increments of the longitudinal tire forces. To verify the performance of the control framework, a Trucksim-Simulink cooperative test platform is established. The test results show satisfactory performances on energy economy, anti-wheel-slip, and the safety and stability of the motion control. [ABSTRACT FROM PUBLISHER]