A new strategy for traction control in turning via engine modeling

The driving stability is affected by driven wheel slip, which can be controlled by the driven wheel torque. In a vehicle powered by an internal combustion engine, the torque can be controlled by an engine management system. The sliding mode algorithm is the mechanism behind the design of the traction control system (TCS). The longitudinal slip is controlled by the position of the throttle valve. The vehicle model used has seven degrees of freedom and a two-state engine model, i.e., the mass of air in the intake manifold and the engine speed. Time-delay transport is considered in the engine model used. A nonlinear tire model for combined slip is used for tire force computation. Due to the nonlinear dynamic of the tire, vehicle, and engine, the control method of sliding mode is used for its robustness. A controller is designed based on the dynamic surface control, for which two first-order surfaces are defined. The effectiveness of the controller is demonstrated with simulation results for different maneuvers. Results show that for different road conditions, the acceleration performance, directional stability, and steerability of a vehicle equipped with TCS is improved. The reason is that the slip is controlled by keeping it in a desired range     

MPC-based yaw stability control in in-wheel-motored EV via active front steering and motor torque distribution

This paper focuses on yaw stability control of in-wheel-motored electric vehicle (EV), and a model predictive controller is designed based on holistic control structure via active front steering and motor torque distribution. By designing a suitable reference model, the controller stabilizes a vehicle along the desired states while rejecting skid and fulfilling its physical constraints, so this is described as a constrained tracking problem. To solve this, the holistic control scheme is built to simplify the hierarchical structure of the controller and directly optimize the control inputs of system. Based on holistic control structure and MPC method, an objective function with constraints is designed over a receding horizon to meet the control requirements. Finally, the proposed nonlinear model predictive controller is evaluated on eight degrees of freedom (8DOF) EV model offline simulation platform. Simulation results of different road maneuver on slippery surfaces show the benefits of the control methodology used.     

An MRAC-based nonlinear speed control of an interior PM synchronous motor with improved maximum torque operation

A model reference adaptive control (MRAC)-based nonlinear speed control strategy of an interior permanent magnet (IPM) synchronous motor with an improved maximum torque operation is presented. In most servo systems, the controller is designed under the assumption that the electrical dynamics are neglected by the field-oriented control. This requires a high-performance inner-loop current control strategy. However, the separate designs for a high-performance current regulator and a robust speed controller need considerable effort. To overcome this limitation, an MRAC-based nonlinear speed control strategy for the IPM synchronous motor is presented, considering the whole nonlinear dynamics. Nonlinear speed control is achieved by an input–output linearization scheme. This scheme, however, gives an unsatisfactory performance under the mismatch of the system parameters and load conditions. For the robust output response, the controller parameters are estimated by an MRAC technique in which the disturbance torque and flux linkage are estimated. The adaptation laws are derived from Lyapunov stability theory. In view of the drive efficiency, the motor has to provide the maximum torque for a given input. To drive the IPM synchronous motor under improved maximum torque operation, the estimated flux linkage is employed for the generation of the d-axis current command. The robustness and output performance of the proposed control scheme are verified through simulation results.     

A novel driver assist stability system for all-wheel-drive electric vehicles

A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.     

基于模型预测控制的电动汽车轮毂电机转矩控制研究

分布式驱动电动汽车（Distributed Drive Electric Vehicles,DDEV）采用内嵌式轮毂电机,使各车轮独立可控,具有调节形式多样化等突出优点.合理的轮毂电机转矩分配是保证DDEV稳定性的关键.本文为提高DDEV稳定性,分析了轮毂电机转矩分配与稳定性的关系,提出一种基于模型预测控制器的DDEV轮毂电机转矩分配控制系统.所提出的控制系统由上层控制器和下层控制器两个主要部分组成.上层控制器设计了基于拉盖尔函数的模型预测控制器,综合分析保证DDEV稳定性所需的轮毂电机转矩约束条件,实现轮毂电机最优转矩分配,提高DDEV稳定性.下层控制器对四个轮毂电机进行实时控制,执行上层控制器设计的最优转矩分配方案.最后在搭建的Matlab/Simulink环境下进行仿真验证.     

Modular integrated longitudinal and lateral vehicle stability control for electric vehicles

In this paper, the problem of integrated longitudinal and lateral vehicle stability control is addressed using a modular optimal control structure. The optimization process of the high level model predictive control (MPC) controller determines required longitudinal force and yaw moment adjustments to minimize the error between vehicle longitudinal and lateral vehicle stability dynamic states with respect to the target courses. The low level controller is designed to optimally regulate torque at each wheel based on the control inputs of the high level controller, and distribute required torque between the wheels via actuation system. The actuation system that is utilized to implement the proposed control structure functions based on all-wheel drive technology that can provide active control of both traction and yaw moment control with differential torque. The multi-layered structure of the control system allows modularity in design. The performance of the control structure is investigated by conducting experimental tests. The experimental tests have been performed on an electric Chevrolet Equinox vehicle equipped with four independent motors. The results show that the integration of the vehicle longitudinal and lateral dynamics preserves vehicle stability in a planar motion and improves the vehicle dynamic response, especially in challenging driving maneuvers.     

Adaptive Vehicle Lateral-Plane Motion Control Using Optimal Tire Friction Forces With Saturation Limits Consideration

This paper presents an adaptive nonlinear control scheme aimed at the improvement of the handling properties of vehicles. The control inputs for steering intervention are the steering angle and wheel torque for each wheel, i.e., two control inputs for each wheel. The control laws are obtained from a nonlinear 7-degree-of-freedom (DOF) vehicle model. A main loop and eight cascade loops are the basic components of the integrated control system. In the main loop, tire friction forces are manipulated with the aim of canceling the nonlinearities in a way that the error dynamics of the feedback linearized system has sufficient degrees of exponential stability; meanwhile, the saturation limits of tires and the bandwidth of the actuators in the inner loops are taken into account. A modified inverse tire model is constructed to transform the desired tire friction forces to the desired wheel slip and sideslip angle. In the next step, these desired values, which are considered as setpoints, are tackled through the use of the inner loops with guaranteed tracking performance. The vehicle mass and mass moment of inertia, as unknown parameters, are estimated through parameter adaptation laws. The stability and error convergence of the integrated control system in the presence of the uncertain parameters, which is a very essential feature for the active safety means, is guaranteed by utilizing a Lyapunov function. Computer simulations, using a nonlinear 14-DOF vehicle model, are provided to demonstrate the desired tracking performance of the proposed control approach.     

Adaptive vehicle traction force control for intelligent vehicle highway systems (IVHSs)

This paper is concerned with robust longitudinal control of vehicles in intelligent vehicle highway systems by adaptive vehicle traction force control. Two different traction force controllers, adaptive fuzzy logic control and adaptive sliding-mode control, are proposed and applied to the fastest stable acceleration/deceleration and robust vehicle platooning problems. The motivation for investigating adaptive techniques arises from the unknown time-varying nature of the tire/road surface interaction that governs vehicle traction. Synchronous application of the engine or brake torques is also proposed for more stable vehicle maneuvers. The lack of controllability during braking (only one net input torque for the two control objectives, i.e., front and rear wheel slips) is partly overcome by applying auxiliary engine torque. Simulations of the two control methods are conducted using a complex nonlinear vehicle model which fully describes the dynamic behavior of the vehicle. Both controllers result in good performance under time-varying operating conditions.     

Optimal robust control of a contactless brake system using an eddy current

As vehicle speed increases, a more powerful brake system is required to ensure vehicle safety and its reliability. A contactless eddy current brake (ECB) is developed to take the superior advantages of fast anti-lock braking to the conventional hydraulic brake systems. Braking torque analysis is performed by using an approximate theoretical model and the model is modified through experiments to have a more reliable result. Designs of an ECB for a scaled model for demonstration and actual vehicle model are performed. Optimal torque control which minimizes a braking distance is achieved by maintaining a desired slip ratio corresponding to the road condition. Optimal controller which is robust to the varying road friction coefficients is designed by using a sliding mode controller. Simulation and experimental results for a scaled model are presented to investigate the performance of a contactless ECB.     

Design of an Optimal Fuzzy Controller for Antilock Braking Systems

Antilock braking systems (ABSs) have been developed to improve vehicle control during sudden braking, especially on slippery road surfaces. The objective of such control is to increase wheel traction force in the desired direction while maintaining adequate vehicle stability and steerability and reducing the vehicle stopping distance. In this paper, an optimized fuzzy controller is proposed for ABSs. The objective function is defined to maintain the wheel slip to a desired level so that maximum wheel traction force and maximum vehicle deceleration are obtained. All the components of a fuzzy system are optimized using genetic algorithms. The error-based global optimization approach is used for fast convergence near the optimum point. Simulation results show fast convergence and good performance of the controller for different road conditions     

Model predictive control of vehicle stability using coordinated active steering and differential brakes

This paper studies model predictive control of lateral stability of vehicles using coordinated active front steering and differential brakes. The controller is designed based on a bicycle model of the vehicle and the moment of the differential brakes is considered as an external torque. The prediction model calculates the prospective values of the vehicle’s yaw rate, lateral velocity, and tire slip angles over the prediction window. The sideslip angle of the vehicle is enforced within a permissible range using soft constraints on the lateral velocity in order to guarantee persistent feasibility. Using computer simulations, the controller is shown to provide proactive control actions to control the vehicle’s sideslip angle. The closed-loop response of the controller is also studied in experimental tests on an instrumented test vehicle. The results show satisfactory performance in various combinations of active front steering and differential brakes. In addition, the computational time of the controller is measured and shown to be safely below the sample time of the controller.     

Torque Feedforward Control Technique for Permanent-Magnet Synchronous Motors

This paper proposes a torque control method for interior permanent-magnet (IPM) motors operating in a wide speed range requiring high torque/power accuracy and a fast dynamic response. Using the fact that the motor parameters are nonlinear and significantly vary with direct and quadrature current operating points, a new optimal operating plane is generated. This operating plane combines the maximum torque per ampere (MTPA) curve, current limit circle, and maximum torque per volt (MTPV) curve, voltage limitations, and torque calculation under the nonlinear parameter variations. As a result, new feedforward tables are generated, which make full use of measured motor parameters. The new torque and flux regulators built around the feedforward tables provide a fast dynamic response and accurate steady-state torque/power production. The proposed controller was implemented and successfully tested on a 105-kW IPM motor electric drive used in a fuel-cell vehicle program.      

Traction Control for Ride-by-Wire Sport Motorcycles: A Second-Order Sliding Mode Approach

This paper addresses the analysis and design of a safety-oriented traction control system for ride-by-wire sport motorcycles based on the Second-Order Sliding Mode methodology. The controller design is based on a nonlinear dynamical model of the rear wheel slip, and the modeling phase is validated against experimental data measured on an instrumented vehicle. To comply with practical applicability constraints, the position of the electronic throttle body is used as control variable and the effect of the actuator dynamics is thoroughly analyzed. After a discussion on the interplay between the controller parameters and the tracking performance, the final design effectiveness is assessed via Mechanical Simulation Corporation BikeSim, a full-fledged commercial multibody motorcycle model.      

An Antilock-Braking Algorithm for an Eddy-Current-Based Brake-By-Wire System

This paper presents a nonlinear sliding-mode-type controller for slip regulation in a braking event for an electromagnetic brake-by-wire (BBW)-system-equipped automobile. The electromagnetic BBW system under consideration consists of a set of eddy current brakes (ECBs) attached to the automobile wheels. The antilock-braking controller modifies the brake torque command generated by a supervisory controller which, in turn, is based on the driver's command sensed via brake pedal sensors. The modified brake torque command is then generated by a closed-loop actuator control algorithm to control the ECB system. It is shown in this paper that the proposed antilock-braking control system is stable in the sense of Lyapunov. Experimental results are presented for a test vehicle equipped with an eddy-current-based BBW system. Experimental results show that the proposed antilock brake control algorithm provides very good slip regulation in a braking event on low friction-coefficient surfaces (wet jennite) when compared with that of a braking event without the proposed antilock-braking control. Experimental results also indicate that the proposed antilock-braking control system provides a smooth stop for the vehicle     

Longitudinal wheel slip control using dynamic neural networks

The control of automotive braking systems performance and a wheel slip is a challenging problem due to nonlinear dynamics of a braking process and a tire–road interaction. When the wheel slip is not between the optimal limits during braking, the desired tire–road friction force cannot be achieved, which influences braking distance, the loss in steerability and maneuverability of the vehicle. In this paper, the new approach, based on dynamic neural networks, has been employed for improving of the longitudinal wheel slip control. This approach is based on dynamic adaptation of the brake actuation pressure, during a braking cycle, according to the identified maximum adhesion coefficient between the wheel and road. The brake actuated pressure was adjusted on the level which provides the optimal longitudinal wheel slip versus the brake actuated pressure selected by a driver, the current vehicle speed, load conditions, the brake interface temperature and the current value of the wheel slip. The dynamic neural network has been used for modeling of a nonlinear functional relationship between the brake actuation pressure and the longitudinal wheel slip during a braking cycle. It provided preconditions for control of the brake actuation pressure based on the wheel slip change.     

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily.     

A Hybrid-Type Variable-Structure Instantaneous Torque Control With a Robust Adaptive Torque Observer for a High-Performance Direct-Drive PMSM

This paper describes a novel instantaneous torque control scheme for a high-performance direct-drive permanent-magnet synchronous motor. The scheme consists of a robust adaptive instantaneous torque observer and a hybrid-type variable-structure instantaneous torque controller. First, to robustly obtain the instantaneous electromagnetic torque information, a robust adaptive torque observer is designed by considering all possible current model uncertainties. The observation gains and uncertainties prediction rules are derived in the sense of Lyapunov theory so that the stability of the proposed estimation scheme is fulfilled. Second, to ensure perfect tracking of the output torque and providing means in eliminating torque ripples, the frequency modes of the disturbances to be eliminated should be included in the stable closed-loop system. To achieve this objective, a hybrid-type variable-structure controller with internal model, for the flux harmonics and system uncertainties, is adopted. The hybrid controller shows better disturbance rejection without control chattering. Comparative evaluation results are presented to demonstrate the validity and effectiveness of the proposed instantaneous torque control scheme.     

电动助力转向系统无刷电机温度研究

电动助力转向系统由控制器ECU根据传感器手力矩、车速等信息确定电机助力,电机与ECU热管理是其成本控制与系统安全的关键。文中提出在控制中区分快速与慢速发热模型,得到了系统温度的增加值,在不同发热均恒条件下与当前温度、输入电流的关系,估算电机温度。仿真与实验证明,通过这种热管理分析与设计方法有效地解决了系统发热问题,成功地实现了系统成本与系统安全的统一。     

Experimental nonlinear torque control of a permanent-magnetsynchronous motor using saliency

In this paper, a new nonlinear control strategy is proposed for a permanent-magnet salient-pole synchronous motor. This control strategy simultaneously achieves accurate torque control and copper losses minimization without recurring to an internal current loop nor to any feedforward compensation. It takes advantage of the rotor saliency by allowing the current (i_d) to have nonzero values. This, in turn, allows us to increase the power factor of the machine and to raise the maximum admissible torque. We apply input-output linearization techniques where the inputs are the stator voltages and the outputs are the torque and a judiciously chosen new output. This new output insures a well-defined relative degree and is linked to the copper losses in such a way that, when forced to zero, it leads to maximum machine efficiency. The performance of our nonlinear controller is demonstrated by a real-time implementation using a digital signal processor (DSP) chip on a permanent-magnet salient-pole synchronous motor with sinusoidal flux distribution. The results are compared to the ones obtained with a scheme which forces the i_d current to zero     

Constrained real-time control of hydromechanical powertrains – methodology and practical application

Axial piston motors and pumps are core components in many hydromechanical systems as for instance the powertrain of wheel loader vehicles. The optimal performance of the individual components as well as the entire powertrain requires a fast and exact control of both the motor’s swivel angle as well as the pressure difference over the pump by means of the currently available information and despite their respective a-priori unknown reference signal generated by the driver with a joystick or gas pedal operation. Therefore, it is essential that the swivel angle and pressure controllers fully exploit the possible dynamics and the constrained resources of the nonlinear swivel angle displacement and the pressure adjustment system, respectively. In this work, a flatness-based feedforward control strategy for the motor’s swivel angle as well as a flatness-based tracking two-degree-of-freedom controller for the pump pressure are proposed. For the realization of the flat feedforward and the tracking controller, a constrained online trajectory generation by means of a nonlinear (switched) state variable filter is designed for each. Therefore, both controllers are capable of considering the relevant state and input constraints in real-time and without relying on an online optimization. The proposed control algorithms are both discussed and their performance is illustrated with measurement data from a wheel loader vehicle.