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.     

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.     

Nonlinear MPC-based slip control for electric vehicles with vehicle safety constraints

This paper presents a new slip control system for electric vehicles (EVs) equipped with four in-wheel motors, based on nonlinear model predictive control (nonlinear MPC) scheme. In order to ensure vehicle safety, wheel slip stable zone is considered as time-domain constraints of the nonlinear MPC. Besides, the motor output torque is limited by the motor maximum torque, which varies with motor angular velocity and battery voltage, so the motor maximum output torque limitation is considered as system time-varying constraints. The control objectives include: vehicle safety, good longitudinal acceleration and braking performance, preservation of driver comfort and lower power consumption. This paper utilizes nonlinear MPC to solve this complex optimization control problem subject to the constraints, and the vehicle safety objective is achieved by wheel slip stable zone constraints, the other objectives are realized by adding additional cost functions. In addition, a penalty on the slack variables is also added to ensure that the state constraints (wheel slip) do not cause infeasible problems. The effectiveness of the proposed controller is verified in the off-line co-simulation environment of AMESim and Simulink, and a rapid control prototyping platform based on Field programmable gate array (FPGA) and dSPACE is completed to evaluate the real time functionality and computational performance of the nonlinear MPC controller.     

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.     

Integrated model predictive control and velocity estimation of electric vehicles

In this paper, an integrated estimation and control system is developed for the stability and traction control of electric vehicles. A model predictive control technique is used to track the desired vehicle yaw rate while maintaining small lateral velocity and tire slip ratios. This paper proposes a new method to control the lateral stability of the vehicle. In this method, the lateral vehicle velocity is controlled indirectly by adjusting the reference yaw rate. This reduces the size of the prediction model and its computational complexity. The controller requires the vehicle’s lateral and longitudinal velocities as well as its tire forces for stability and traction control. This paper also proposes a novel velocity estimation scheme that uses the combined vehicle kinematics and tire model. The developed Kalman-based estimator provides velocities and lateral forces at each corner that are robust to changes in the road condition. The combined model-based and kinematic-based estimation structure mitigates some common problems of the widely used kinematic-based estimators such as the spikes and drifting issues. The stability of the proposed time-varying estimator is also investigated. The designed control and estimation scheme are experimentally validated on various driveline configurations and proven to provide reliable results.     

Minimum-time thermal dose control of thermal therapies

The problem of controlling noninvasive thermal therapies is formulated as the problem of directly controlling thermal dose of the target. To limit the damage to the surrounding normal tissue, the constraints on the peak allowable temperatures in the selected spacial locations are imposed. The developed controller has a cascade structure with a linear, constrained, model predictive temperature controller in the secondary loop. The temperature controller manipulates the intensity of the ultrasound transducer with saturation constraints, which noninvasively heats the spatially distributed target. The main nonlinear thermal dose controller dynamically generates the reference temperature trajectories for the temperature controller. The thermal dose controller is designed to force the treatment progression at either the actuation or temperature constraints, which is required to minimize the treatment time. The developed controller is applicable to high and low-intensity treatments, such as thermal ablation and thermoradiotherapy. The developed approach is tested using computer simulations for a one-dimensional model of a tumor with constraints on the maximum allowable temperature in the normal tissue and a constrained power output of the ultrasound transducer. The simulation results demonstrate that the proposed approach is effective at delivering the desired thermal dose in a near minimum time without violating constraints on the maximum allowable temperature in healthy tissue, despite significant plant-model mismatch introduced during numerical simulation. The results of in vitro and in vivo validation are reported elsewhere.     

Predictive Control of Constrained Linear Systems with Multiple Missing Measurements

This paper investigates the problem of predictive control for constrained control systems, in which the measurement signal may be multiply missing. An augmented stochastic model is firstly introduced to describe and compensate these missing measurements. By means of an infinite horizon quadratic performance objective, a state-feedback predictive control law involving missing probability is designed by minimizing the upper bound on performance objective at each sampling instant. It is shown that the on-line optimization problem subject to input and state constraints can effectively be solved in terms of linear matrix inequalities. The designed predictive controller can achieve the desired control performance and also guarantee the closed-loop stability in mean square sense. Finally, an example is given to illustrate the proposed results.     

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.     

高空作业车智能控制器的设计与开发

为了改善高空作业车的作业性能,设计了一款高空作业车智能控制器,解决了完全依靠操作人员的视觉反馈以及用机械调速阀对机构进行控制,精确性能差的问题,提高了操作的安全智能性、可靠性。在介绍了高空作业车控制系统组成基础上,首先给出了智能控制器的整体框架结构,再对控制器的软硬件设计实现进行了阐述。在实际车辆测试中,工作稳定,性能达到了预期设计效果。     

Evaluation of soft switching for EV and HEV motor drives

Soft switching has the potential of reducing switch stresses and of lowering the switching losses as compared to hard switching. To understand the effectiveness of the soft-switching technique, when applied to electric vehicle (EV) and hybrid electric vehicle (HEV) systems, it may be necessary to first evaluate their system requirements and performance. This evaluation process would require knowledge of the vehicle dynamics. The vehicle load requires a special torque-speed profile from the drivetrain for minimum power ratings to meet the vehicle's operational constraints, such as initial acceleration and gradability. The selection of motor and its control for EV and HEV applications are dictated mainly by this special torque-speed requirement. As a consequence, this requirement will have a strong influence on the converter operation. This paper makes an attempt to evaluate EV and HEV running in both standard Federal Test Procedure 1975 city driving and highway driving cycles. A simplified analysis is carried out for several of the most commonly used electric motors operating on the optimal torque-speed profile. Special attention is given to the converter conduction and switching losses, by analyzing the switching losses, and by assuming that an ideal soft-switching scheme will have zero switching losses, one can evaluate the improvement in the system efficiency if a soft-switching control is used. The relative significance of soft switching for EV and HEV systems is then established     

Multiobjective control of a vehicle with triple trailers

We consider the backing-up control of a vehicle with triple trailers using a model-based fuzzy-control methodology. First, the vehicle model is represented by a Takagi-Sugeno fuzzy model. Then, we employ the so-called "parallel distributed compensation" design to arrive at a controller that guarantees the stability of the closed-loop system consisted of the fuzzy model and controller. The control-design problem is cast in terms of linear matrix inequalities (LMIs). In addition to stability, the control performance considerations such as decay rate, constraints on input and output, and disturbance rejection are incorporated in the LMI conditions. In application to the vehicle with triple trailers setup, we utilize these LMI conditions to explicitly avoid the saturation of the steering angle and the jackknife phenomenon in the control design. Both simulation and experimental results are presented. Our results demonstrate that the fuzzy controller effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the saturation of the actuator and "jackknife" phenomenon.     

A fault-tolerant control architecture for induction motor drives in automotive applications

This paper describes a fault-tolerant control system for a high-performance induction motor drive that propels an electrical vehicle (EV) or hybrid electric vehicle (HEV). In the proposed control scheme, the developed system takes into account the controller transition smoothness in the event of sensor failure. Moreover, due to the EV or HEV requirements for sensorless operations, a practical sensorless control scheme is developed and used within the proposed fault-tolerant control system. This requires the presence of an adaptive flux observer. The speed estimator is based on the approximation of the magnetic characteristic slope of the induction motor to the mutual inductance value. Simulation results, in terms of speed and torque responses, show the effectiveness of the proposed approach.     

基于模糊预测控制的船舶动力定位系统的设计

在系统分析和研究船舶动力定位系统的基础上,提出一种基于模糊预测控制的船舶动力定位的方法,设计了模糊预测控制器。首先,在预测控制部分,探讨预测模型和反馈校正的设计;在模糊控制部分,研究隶属函数和模糊规则的具体制定。最后采用工程数据,对该算法和模糊预测控制器进行了仿真验证和性能评判,结果表明,所设计的控制器能对船舶进行有效的定位。     

Active queue management algorithm based on data-driven predictive control

Model predictive control (MPC) is a popular strategy for active queue management (AQM) that is able to incorporate physical and user defined constraints. However, the current MPC methods rely on explicit fluid model of TCP behavior with input time delay. In this paper, we propose a novel AQM algorithm based on data-driven predictive control, called Data-AQM. For Internet system with large delay, complex change and bad disturbance, data-driven predictive controller can be obtained directly based on the input–output data alone and does not require any explicit model of the system. According to the input–output data, the future queue length in data buffer, which is the basis of optimizing drop probability, is predicted. Furthermore, considering system constraints, the control requirement is converted to the optimal control objective, then the drop probability is obtained by solving the optimal problem online. Finally, the performances of Data-AQM are evaluated through a series of simulations.     

自适应串级自抗扰弹性飞翼无人机姿态控制

文中针对飞翼无人机因其宽泛的飞行包线和特殊的布局带来的飞行控制技术难点,给出了一种自适应串级自抗扰飞翼无人机宽包线控制算法。首先,推导了适用于该算法的弹性飞翼无人机的非线性数学模型;其次,分别设计了弹性飞翼无人机的内环和外环自抗扰姿态控制器。自适应自抗扰控制器利用扩张状态观测器进行估计并动态反馈补偿,再利用NLSEF 抑制补偿残差;不需要无人机精确的模型参数,也无需精确的气动参数及摄动界限。仿真分析显示所设计的自适应自抗扰控制器较好地解决了弹性飞翼无人机从低空低速到高空高速的鲁棒控制,能够克服干扰及气动模态参数大范围摄动的影响。     

一类基于自适应反馈控制的车辆编队研究

文中研究了基于生物刺激神经动力学方法的车辆编队控制问题。以编队车辆的运动学模型为基础,在跟随领航者体系下建立车辆编队系统动态模型。根据李雅普诺夫稳定性理论,设计出新的自适应反馈跟踪控制器,将车辆跟随问题转化为系统误差的控制问题,并运用仿真实验验证控制器的有效性。与现有的反馈跟踪控制器进行对比后可以看到,文中所设计的控制器在稳定性和收敛速度上有了进一步的提高。     

Robust Constrained Model Predictive Control Based on Parameter-Dependent Lyapunov Functions

The problem of robust constrained model predictive control (MPC) of systems with polytopic uncertainties is considered in this paper. New sufficient conditions for the existence of parameter-dependent Lyapunov functions are proposed in terms of linear matrix inequalities (LMIs), which will reduce the conservativeness resulting from using a single Lyapunov function. At each sampling instant, the corresponding parameter-dependent Lyapunov function is an upper bound for a worst-case objective function, which can be minimized using the LMI convex optimization approach. Based on the solution of optimization at each sampling instant, the corresponding state feedback controller is designed, which can guarantee that the resulting closed-loop system is robustly asymptotically stable. In addition, the feedback controller will meet the specifications for systems with input or output constraints, for all admissible time-varying parameter uncertainties. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques.     

Lateral stabilization of a four wheel independent drive electric vehicle on slippery roads

In this paper, a new controller is proposed for lateral stabilization of four wheel independent drive electric vehicles without mechanical differential. The proposed controller has three levels including high, medium and low control levels. Desired vehicle dynamics such as reference longitudinal speed and reference yaw rate are determined by higher level of controller. Moreover, using a neural network observer and a fuzzy logic controller, a novel reference longitudinal speed generator system is presented. This system guarantees the vehicle’s stable motion on the slippery roads. In this paper, a new sliding mode controller is proposed and its stability is proved by Lyapunov stability theorem. This sliding mode control structure is faster, more accurate, more robust, and with smaller chattering than classic sliding mode controller. Based on the proposed sliding mode controller, the medium control level is designed to determine the desired traction force and yaw moment. Therefore, suitable wheel forces are calculated. Finally, the effectiveness of the introduced controller is investigated through conducted simulations in CARSIM and MATLAB software environments.     

Embedded explicit model predictive vibration control

This article presents an embedded active vibration suppression system featuring real-time explicit model predictive control (EMPC) that is implemented on a microcontroller unit (MCU). The EMPC controller minimizes the tip deflection of an aluminum cantilever beam driven by piezoceramic actuators, gaining its feedback from direct position measurements. The output and input performance of the EMPC method is compared to an analogously tuned positive position feedback (PPF) controller. An extensive analysis is provided on the cycle timing and memory needs of the explicit predictive vibration control scheme. The results demonstrate that the EMPC controller may achieve the same vibration suppression results compared to PPF with less input effort, while inherently respecting process constraints. Furthermore, we show that EMPC task execution timing is comparable in the random access memory (RAM) and read only memory (ROM) alternatives, suggesting that numerous current microcontrollers are suitable for EMPC-based active vibration control, in case the prediction model is kept simple.     

四旋翼飞行器逃逸算法的实现

提出了四旋翼飞行器的逃逸行为算法。逃逸行为算法可以降低无人直升机与动态障碍物碰撞的可能性。通过在算法中实现群粒子的相对定位给出了四转子的移动和约束模型。为四转子的实际模型设计了控制器,该控制器根据规定的轨迹计算出每个螺旋桨转子的速度。通过仿真和对真实的派诺特AR．Drone飞行器的测试验证了该算法的功能。