Robust gain-scheduled output feedback yaw stability control for in-wheel-motor-driven electric vehicles with external yaw-moment

This paper presents a robust gain-scheduled output feedback yaw stability H_∞ controller design to improve vehicle yaw stability and handling performance for in-wheel-motor-driven electric vehicles. The main control objective is to track the desired yaw references by managing the external yaw moment. Since vehicle lateral states are difficult to obtain, the state feedback controller normally requires vehicle full-state feedback is a critical challenge for vehicle lateral dynamics control. To deal with the challenge, the robust gain-scheduled output feedback controller design only uses measurements from standard sensors in modern cars as feedback signals. Meanwhile, parameter uncertainties in vehicle lateral dynamics such as tire cornering stiffness and vehicle inertial parameters are considered and handled via the norm-bounded uncertainty, and linear parameter-varying polytope vehicle model with finite vertices is established through reducing conservative. The resulting robust gain-scheduled output feedback yaw stability controller is finally designed, and solved in term of a set of linear matrix inequalities. Simulations for single lane and double lane change maneuvers are implemented to verify the effectiveness of developed approach with a high-fidelity, CarSim^®, full-vehicle model. It is confirmed from the results that the proposed controller can effectively preserve vehicle yaw stability and lateral handling performance.     

A shared steering controller design based on steer-by-wire system considering human-machine goal consistency

Shared control structure is beneficial to steering controller design of intelligence vehicles, and human-machine goal consistency is a key prerequisite for shared control. However, the goal consistency is usually given and cannot be changed, and the steering controller in low goal consistency, which directly affect the vehicle performance in case of emergency, has not been sufficiently investigated. This paper proposes a shared steering controller for path-following task based on Nash game strategy and steer-by-wire system considering different human-machine goal consistency. The driver-automation interactive path-following task is modeled by non-cooperative MPC, and authority weight of lateral displacement is used to balance the control objectives of the driver and automation. Human-machine goal consistency is determined by the driver and the automation controller steering angle. Aimed at different goal consistencies, a continuous authority weight adjustment algorithm is designed to ensure correct path following. This is especially true in low consistency in this study, when four driving modes are given to meet the different demand for control power. Simulations and hard-in-loop tests are conducted to verify the proposed control algorithm and the results show that it can perform the path-following task irrespective of human-machine goal consistency.     

Decoupling and robust control of velocity-varying four-wheel steering vehicles with uncertainties via solving Attenuating Diagonal Decoupling problem

This paper is devoted to solve the combined problem of input–output decoupling and robust control of the four-wheel steering vehicles. A more practical three-degree-of-freedom systems covering longitudinal, lateral and yaw motions are used to improve the safety and steerability while uncertainties and external disturbances are considered. A novel decoupling conception Attenuating Diagonal Decoupling and a new index Coupling Attenuation Index are introduced and the system is divided up into two systems with a special structure. The first system is caused by uncertainties and disturbances and the second system is a certain system coupling with the first one. A control scheme composed of a coupling attention controller and a decoupling controller are explored. The influences of the uncertainties and disturbances on the output are attenuated under the coupling index by the coupling attention controller designed for the first system while the input–output decoupling is achieved by employing the decoupling controller designed for the second system. Furthermore, we prove in theory that the input–output decoupling and robust control are both established for the closed-loop system of the control scheme and the primordial vehicle system. Besides these works, a switching law is introduced such that the above excellent performances are realizable in four-wheel steering vehicles with conventional steering interfaces. Simulations show that even with a large velocity varying range, the decoupling and robust performances are guaranteed simultaneously, i.e. the handling stability and steerability are improved.     

Robust stabilization of balanced and splay formations with heterogeneous controller gains

This paper analyses collective motion of multi-vehicle systems in balanced or splay formation when the vehicles are equipped with heterogeneous controller gains. Balancing refers to a situation in which the positional centroid of the vehicles is stationary. The splay formation is a special case of balancing in which the vehicles are spatially distributed with equal angular separation between them. The paper proposes strategies to achieve such balanced and splay formations about a desired centroid location while allowing the vehicles to move either along straight line paths or on individual circular orbits. Feedback control laws that can tolerate heterogeneity in the controller gains, which may be caused by imperfect implementation, are derived and analyzed. It is shown that drastic failures leading to controller gains becoming zero for almost half of the vehicles in the group can be tolerated and balanced formation can still be achieved. On the other hand, splay formation can still be achieved if the controller gain is zero for at most one vehicle. Simulation examples are given to illustrate the theoretical findings.     

Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles

This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on an electric assist control strategy (EACS), the fitness function is defined so as to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. In addition, in order to reduce the number of the decision variables, a new approach is used for the battery control parameters. Finally, the optimization process is performed over three different driving cycles including ECE-EUDC, FTP and TEH-CAR. The results from the computer simulation show the effectiveness of the approach and reduction in FC and emissions while ensuring that the vehicle performance is not sacrificed.     

Interval fuzzy robust non-fragile finite frequency control for active suspension of in-wheel motor driven electric vehicles with time delay

This paper proposes a fuzzy non-fragile finite frequency H_∞ control algorithm for the active suspension system (ASS) of the electric vehicles driven by in-wheel motors with an advanced dynamic vibration absorber (DVA). Firstly, an interval type-2 Takagi-Sugeno (T-S) fuzzy model is established to formulate the nonlinear time-delay ASS with the uncertainties of sprung mass, unsprung mass, suspension stiffness, and tire stiffness. Secondly, a differential evolution (DE) algorithm is adopted to optimize the parameters of vehicle suspension and DVA. Thirdly, a non-fragile finite frequency H_∞ control controller is developed under the consideration of controller perturbation and input delay to improve the comprehensive performance of the chassis under the finite frequency external disturbances. Finally, simulation tests are carried out to verify the effectiveness of the proposed controller.     

Robust H∞ dynamic output-feedback control for four-wheel independently actuated electric ground vehicles through integrated AFS/DYC

This paper simultaneously addresses the parameter/state uncertainties, external disturbances, input saturations, and actuator faults in the handling and stability control for four-wheel independently actuated (FWIA) electric ground vehicles (EGVs). Considering the high cost of the available sensors for vehicle lateral velocity measurement, a robust H_∞ dynamic output-feedback controller is designed to control the vehicle motion without using the lateral velocity information. The investigated parameter/state uncertainties include the tire cornering stiffness, vehicle mass, and vehicle longitudinal velocity. The unmodeled terms in the vehicle lateral dynamics model are dealt as the external disturbances. Faults of the active steering system and in-wheel motors can cause dangerous consequences for driving, and are considered in the control design. Input saturation issues for the tire forces can deteriorate the control effects, and are handled by the proposed strategy. Integrated control with active front steering (AFS) and direct yaw moment (DYC) is adopted to control the vehicle yaw rate and sideslip angle simultaneously. Simulation results based on a high-fidelity and full-car model via CarSim-Simulink show the effectiveness of the proposed control approach.     

A yaw stability-guaranteed hierarchical coordination control strategy for four-wheel drive electric vehicles using an unscented Kalman filter

A novel hierarchical coordination control strategy (HCCS) is offered to guarantee the stability of four-wheel drive electric vehicles (4WD-EVs) combining the Unscented Kalman filter (UKF). First, a dynamics model of the 4WD-EVs is established, and the UKF-based estimator of sideslip angle and yaw rate is constructed concurrently. Second, the equivalent cornering stiffness coefficients are jointly estimated to consider the impact of vehicle uncertainty parameters on the estimator design. Afterwards, a HCCS with two-level controller is presented. The sideslip angle and yaw rate are controlled by an adaptive backstepping-based yaw moment controller, and the computational burden is relieved by an improved adaptive neural dynamic surface control technology in the upper-level controller. Simultaneously, the optimal torque distribution controller of hub motors is developed to optimize the adhesion utilization ratio of tire in the lower-level controller. Finally, the proposed HCCS has shown effective improvement in the trajectory tracking capability and yaw stability of the 4WD-EVs under various maneuver conditions compared with the traditional Luenberger observer-based and the federal-cubature Kalman filter-based hierarchical controller.     

The path following of intelligent unmanned vehicle scheme based on adaptive sliding mode-model predictive control

An adaptive sliding mode-model predictive control for the path following of intelligent unmanned vehicle is given in this paper. On account of excellent performances of the sliding mode structure, this algorithm can not only effectively estimate the uncertainty of the vehicle system to further improve the following accuracy, but minish the amount of calculation in comparision with model predictive control. Then, the following accuracy between the real system and the theoretical model can be compensated by the fractional order coefficient of controller. Therefore, an adaptive fractional order sliding mode-fractional order model predictive control is designed to follow the path of the intelligent unmanned vehicle. Meanwhile, the robust stability and control accuracy of the associated control algorithm are proved. Finally, different paths are designed to verify the theoretical analysis of the control performance in the controllers.     

Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer

Over the last decade, considerable interest has been shown from industry, government and academia to the design of Vertical Take-Off and Landing (VTOL) autonomous aerial vehicles. This paper uses the recently developed sliding mode control driven by sliding mode disturbance observer (SMC-SMDO) approach to design a robust flight controller for a small quadrotor vehicle. This technique allows for a continuous control robust to external disturbance and model uncertainties to be computed without the use of high control gain or extensive computational power. The robustness of the control to unknown external disturbances also leads to a reduction of the design cost as less pre-flight analyses are required. The multiple-loop, multiple time-scale SMC-SMDO flight controller is designed to provide robust position and attitude control of the vehicle while relying only on knowledge of the limits of the disturbances. Extensive simulations of a 6 DOF computer model demonstrate the robustness of the control when faced with external disturbances (including wind, collision and actuator failure) as well as model uncertainties.     

Robust active suppression for body-freedom flutter of a flying-wing unmanned aerial vehicle

Flying-wing unmanned aerial vehicles have received extensive attention over the past decade because of their excellent aerodynamic and stealth performance. However, the aeroelastic interaction problems among unsteady aerodynamics, flight dynamics, and structural dynamics, such as the body-freedom flutter, are still open. This paper presents the study of a robust control scheme for active body-freedom flutter suppression of a flexible flying-wing unmanned aerial vehicle. The control objective is to expand the boundary of body-freedom flutter and to enhance the control robustness to external unknown disturbance simultaneously. The paper begins with the modeling procedure of a parameter-varying aeroservoelastic plant for the design of control law. Then, it presents how to synthesize a robust controller so as to suppress the flutter instability for a wide flight range of dynamic pressures. Afterwards, the paper shows how to analyze the flutter stability of the closed-loop system and the robustness of the controller, respectively. The numerical results demonstrate that the proposed robust controller can not only expand the flutter boundary of the unmanned aerial vehicle by 30%, but also exhibit the strong robustness to external disturbance.     

Nonlinear H∞ robust control applied to F-16 aircraft with mass uncertainty using control surface inverse algorithm

In this paper, an analytic solution of nonlinear H_∞ robust controller is first proposed and used in a complete six degree-of-freedom nonlinear equations of motion of flight vehicle system with mass and moment inertia uncertainties. A special Lyapunov function with mass and moment inertia uncertainties is considered to solve the associated Hamilton-Jacobi partial differential inequality (HJPDI). The HJPDI is solved analytically, resulting in a nonlinear H_∞ robust controller with simple proportional feedback structure. Next, the control surface inverse algorithm (CSIA) is introduced to determine the angles of control surface deflection from the nonlinear H_∞ control command. The ranges of prefilter and loss ratio that guarantee stability and robustness of nonlinear H_∞ flight control system implemented by CSIA are derived. Real aerodynamic data, engine data and actuator system of F-16 aircraft are carried out in numerical simulations to verify the proposed scheme. The results show that the responses still keep good convergence for large initial perturbation and the robust stability with mass and moment inertia uncertainties in the permissible ranges of the prefilter and loss ratio for which this design guarantees stability give same conclusion.     

Dynamic predictor-based adaptive cruise control

We study the stabilization problem of a platoon of Adaptive Cruise Control (ACC) vehicles in the presence of input-delay. We use a dynamic predictor for input-delay compensation, a filtered version of the standard finite spectrum assignment method that overcomes robustness issues, in particular those raised by the approximation of distributed time-delay terms. Each vehicle must achieve the velocity of the preceding vehicle while ensuring a safe inter-vehicular distance established by a time headway-based spacing policy. To this end, a proportional-integral type controller combined with a dynamic predictor is added to each vehicle in the platoon that guarantees stability and zero steady-state error. String stability property of the closed-loop system, i.e., the platoon’s ability to attenuate fluctuations arising in the motion of the leading vehicle, is analyzed using a frequency domain framework. The effectiveness of the proposed control scheme is illustrated with simulation results of a platoon of five vehicles.     

基于多传感器的智能路灯控制器设计

针对现有路灯控制器在节能和故障检测方面的不足,本文提出一种智能路灯控制器设计。该控制器综合利用多传感器信息实现夜深无人时段的人员和车辆检测,在无人车经过时关闭或降低路灯亮度,减少不必要的电力消耗。并实现路灯工作状况的自检,通过电力载波通信实现控制器与路灯管控中心的数据通信,实现远程故障监测。     

Integrated tire slip energy dissipation and lateral stability control of distributed drive electric vehicle with mechanical elastic wheel

Stability and energy consumption have always been important issues in electric vehicle research. Excessive slip energy not only aggravates tire wear, but also consumes energy of electric vehicle. In order to ensure the lateral stability and to reduce the slip energy dissipation of the distributed drive electric vehicle (DDEV) equipped with Mechanical Elastic Wheel (MEW), an integrated framework considering both tire slip energy dissipation and lateral stability control is proposed. The SESC (Slip Energy and Stability Control) is a hierarchical control framework for DDEV with MEW. A PID speed tracking controller and an (Integral Terminal Slide Mode) ITSM controller are designed at the upper-level controller. The ITSM controller can improve the lateral stability of the vehicle by obtaining the desired yaw moment. Speed tracking controller can stabilize the speed of the vehicle and obtain the desired longitudinal force. At the lower-level controller, the brush model of the MEW is proposed to express tire slip energy. In order to reduce the error of the vehicle dynamics and the slip energy dissipation, a mixed objective function including a holistic corner controller (HCC) and a minimum tire slip energy characterization is proposed. The proposed control framework is verified by Carsim and Matlab/Simulink under emergency simulation conditions. The simulation results show that the SESC based method can improve the lateral stability of DDEV with MEW effectively, and has better performance compared with fuzzyPID+AD based method. Meanwhile, the SESC achieves less slip energy than conventional torque distribution method.     

高速公路BP神经网络限速控制

通过对高速公路在运行中遇到的主要问题的阐述,分析了可变限速控制在高速监控系统中的作用。针对高速公路可变限速控制是一个非线性时变系统,难于用数学模型进行建摸这一特点,提出了BP神经网络控制方法。阐述了BP神经网络的学习算法,根据高速公路主线上车辆数目以及路面状况、气象条件等信息,设计了高速公路可变速度标志神经网络控制器,并对控制器进行了仿真研究。仿真表明:网络学习速度快,自适应强,泛化能力好,结果精度高,对交通流限速控制中具有一定的推广应用价值。     

Trajectory tracking of an autonomous vehicle using immersion and invariance control

An Immersion and Invariance [I & I] controller is designed to control the nonlinear lateral vehicle’s motion, using the steering angle as the only input. Similar to most of the lateral vehicle’s dynamics control law, the cornering stiffness parameters are involved in our proposed controller. Because of the tight relation between tire/road properties and the cornering stiffness parameters, they are not available from the outputs of the sensors and therefore, should be estimated for utilizing in the control law. An online data-driven identification is employed for estimating the cornering stiffness parameters. In addition, a robust model-based fault detection and approximation method in the presence of uncertainties via neural networks is presented. The performance of the obtained control law is investigated via simulation tests in different situations and in the presence of the disturbance. Moreover, some validation tests are performed using the CarSim software to show the effectiveness of our algorithm.     

电动汽车用永磁同步电机控制系统的研究

本文设计了一套以TI公司的TMS320F2812芯片为控制核心的电动汽车用永磁同步电机(PMSM)控制系统.控制器通过人机接口、串口通讯和网络通讯三种方式进行参数设置和数据交换,采用矢量控制算法,研究了id=0控制策略.实验表明,该控制系统具有较好的动、静态性能,调节速度快,完全满足规定的性能指标.     

Driver and automation cooperation approach for share steering control system

The interferences and drivers' maloperations are important factors affecting vehicle driving safety. This paper investigates the problem of authority allocation to weaken the impact of interferences and drivers’ maloperations on the shared steering control system. Based on the parallel framework of the shared steering control system, an extended framework including the upper level and the lower lever is proposed. The lower lever is used to realize the shared steering control, which includes the driver model, trajectory tracking controller and vehicle model. To improve the robustness of the system, the uncertainty of vehicle dynamics parameters is considered in the trajectory tracking controller, including tire cornering stiffness and longitudinal velocity. The upper level is used to calculate the authority level of the driver and controller required by the lower lever, which consists of an authority dynamic allocation model and an authority allocation decision strategy. The role of the authority dynamic allocation model is to calculate the reference allocation level of the driver and controller dynamically. When the driver's operation and vehicle working states are trustworthy, the reference allocation levels of the driver and controller will be followed. Conversely, a decision result will be gained by the authority allocation decision strategy to replace the reference allocation levels, and the sum of the authority levels of the driver and the automation will not be fixed as 1. The simulation results show that the proposed approach can effectively improve vehicle driving safety, anti-interference and reliability, and can effectively reduce the impact of crosswind and driver's maloperation on vehicle safety, and alleviate the driver's operation load.     

An adaptive compensator for a vehicle driven by DC motors

A vehicle system driven by two independent DC motors is presented here, one of which is used for the right wheel and the other is used for the left wheel. An adaptive compensator using Takagi-Sugeno fuzzy systems is proposed to control the vehicle system. The compensator includes an adaptive model identifier and adaptive controller. An online method is used to adjust the parameters of the identifier model to match the behavior model of the vehicle system. Then, the parameters of the identifier model are employed in a standard parallel-distributed compensator to provide asymptotically stable equilibrium for the closed-loop vehicle drive system, in which the velocity and direction angle of the vehicle are controlled. Results demonstrate that the proposed controller structure is robust to load changes and follows different trajectories very well.