基于MPC555高性能32位单片机的混合动力汽车工况控制研究

介绍了基于NPC555的混合动力汽车动力总成控制系统的组成，阐述了其底层驱动模块、状态转换判断模块、电机驱动模块、内燃机单独驱动模块、内燃机与电机联和驱动模块、串联驱动模块、制动能量回收模块等的功能。试验表明，该控制系统可以实现混合动力汽车运行工况控制。     

Vehicle yaw stability control using active limited-slip differential via model predictive control methods

In this paper, the problem of vehicle yaw control using an active limited-slip differential (ALSD) applied on the rear axle is addressed. The controller objective is to minimise yaw-rate and body slip-angle errors, with respect to target values. A novel model predictive controller is designed, using a linear parameter-varying (LPV) vehicle model, which takes into account the ALSD dynamics and its constraints. The controller is simulated using a 10DOF Matlab/Simulink simulation model and a CarSim model. These simulations exemplify the controller yaw-rate and slip-angle tracking performances, under challenging manoeuvres and road conditions. The model predictive controller performances surpass those of a reference sliding mode controller, and can narrow the loss of performances due to the ALSD's inability to transfer torque regardless of driving conditions.     

基于多点约束法的鼓式制动器有限元分析

为研究径向浮动蹄式制动器的性能特点,利用接触与多点约束相结合的方法,建立了结构有限元模型.经对比发现,该模型较传统模型能更准确的反映摩擦片的受力状态,且制动力矩仿真结果与试验数据有较好的一致性.利用该模型对制动器制动性能和摩擦片应力分布情况进行的仿真计算结果表明,径向浮动蹄式制动器有利于改善车辆制动跑偏现象和领蹄的受力状况,提高车辆的行车安全性和领蹄摩擦片的耐磨性:从蹄的受力均匀性有待进一步提高.     

基于MPC的液压混合动力车辆能量管理策略研究

针对双轴并联式液压混合动力车辆(PHHV),以蓄能器荷电状态(SOC)和发动机瞬时燃油质量流量m8f为输入量,发动机需求功率比例φ为输出量,以油耗最小为目标函数设计了模型预测控制器(MPC)进行PHHV的能量管理。基于MATLAB/Simulink平台搭建了包括需求功率计算、发动机、蓄能器和泵/马达等主要部件的PHHV车辆模型并进行MPC能量管理。研究结果表明,在美国道路城市循环工况(UDDS)下,MPC管理下的PHHV能充分发挥混合动力的特点,合理调节分配发动机和液压单元的需求功率,降低行驶过程的总油耗。     

多约束模型预测控制在AUV深度控制中的应用

针对AUV深度控制,提出深度偏差转换为指令纵倾的算法,基于拉盖尔网络逼近的模型预测控制理论,设计了舵角舵速硬约束和纵倾软约束条件下的纵倾运动模型预测控制器。仿真结果表明,该控制方法具有良好的动态控制性能,并可快速处理多种约束条件的二次规划问题。     

Wheel slip control with torque blending using linear and nonlinear model predictive control

Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.     

A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators

The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200?Hz control frequency.     

基于模型预测控制的半挂汽车主动防侧倾控制方法

提出了一种半挂汽车主动防侧倾控制方法。搭建了一个七自由度动力学模型和一个三自由度参考模型;用无迹Kalman滤波的方法,来估计车辆的横向载荷转移率;确定优化目标,运用模型预测控制(MPC)理论进行最优化求解,得到各车轴的主动防侧倾力矩;在Simulink/Trucksim联合仿真环境中,进行仿真对比与分析。结果表明:在本文的MPC控制器和PID控制器作用下,半挂汽车各状态量皆收敛,且横向载荷转移率保持在0.7以内;相比于PID控制,MPC控制所需的防侧倾力矩更小更均衡,各状态量变化也更加平稳。因此,本MPC控制器在提升半挂汽车侧倾稳定性的同时具有较好的鲁棒性。     

重载列车运行控制的研究

介绍重载列车运行控制方面的研究进展.按不同的控制目标、研究方法和控制器设计方法进行分类阐述.     

Multi-scale perimeter control approach in a connected-vehicle environment

This paper proposes a novel approach to integrate optimal control of perimeter intersections (i.e. to minimize local delay) into the perimeter control scheme (i.e. to optimize traffic performance at the network level). This is a complex control problem rarely explored in the literature. In particular, modeling the interaction between the network level control and the local level control has not been fully considered. Utilizing the Macroscopic Fundamental Diagram (MFD) as the traffic performance indicator, we formulate a dynamic system model, and design a Model Predictive Control (MPC) based controller coupling two competing control objectives and optimizing the performance at the local and the network level as a whole. To solve this highly non-linear optimization problem, we employ an approximation framework, enabling the optimal solution of this large-scale problem to be feasible and efficient. Numerical analysis shows that by applying the proposed controller, the protected network can operate around the desired state as expressed by the MFD, while the total delay at the perimeter is minimized as well. Moreover, the paper sheds light on the robustness of the proposed controller. This multi-scale hybrid controller is further extended to a stochastic MPC scheme, where connected vehicles (CV) serve as the only data source. Hence, low penetration rates of CVs lead to strong noises in the controller. This is a first attempt to develop a network-level traffic control methodology by using the emerging CV technology. We consider the stochasticity in traffic state estimation and the shape of the MFD. Simulation analysis demonstrates the robustness of the proposed stochastic controller, showing that efficient controllers can indeed be designed with this newly-spread vehicle technology even in the absence of other data collection schemes (e.g. loop detectors).