Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking

A multi-constrained model predictive control (MPC) algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computation, an active steering linear error model is applied in the MPC controller. Then, a control increment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed controller at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.     

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume (VAV) air conditioning system is with strong coupling and large time delay, for which model predictive control (MPC) is normally used to pursue performance improvement. Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response, a novel tuning method based on machine learning and improved particle swarm optimization (PSO) is proposed. In this method, the relationship between MPC controller parameters and time domain performance indices is established via machine learning. Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices. In addition, the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method. Finally, the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.     

传输时延和数据包丢失的网络化系统预测控制

针对具有传输时延和数据包丢失的网络化系统研究了有约束模型预测控制问题。考虑网络控制系统中同时存在时延和丢包的情况,给定时延和丢包的上界值,用预测控制器产生的对应时刻的控制输入补偿网络时延和数据包丢失,通过建立时延状态矩阵将系统模型进行分类并视为切换系统,并通过构造切换Lyapunov函数证明了切换系统的稳定性。为了在提高系统性能的基础上减少计算量,利用部分滚动优化方法进行控制器设计,给出了性能指标的上界和系统渐进稳定的充分条件,通过在线求解LMI问题得到状态反馈控制律。仿真研究验证了该方法的有效性。     

汽车自适应巡航跟车多目标鲁棒控制算法设计

为满足自适应巡航系统跟车模式下的舒适性需求并且兼顾车辆安全性,基于模型预测控制原理提出一种多目标鲁棒跟车控制算法.建立考虑前车加速度干扰的自适应巡航系统车间纵向运动学模型,该模型可全面反映系统的动态演化规律,提高模型的精度和可靠性;针对自适应巡航系统需求进行目标分析,设计一种考虑舒适性和安全性的多目标模型预测控制算法;针对模型预测控制算法鲁棒性较差的问题,引入修正项反馈,提高控制系统鲁棒性,采用向量约束管理法解决模型预测控制算法硬约束造成的控制系统无优化解问题.仿真结果表明:该算法使跟车时车辆加速度及冲击度保持在舒适性范围,同时车间距始终大于最小安全距离,兼顾了舒适性和安全性要求,实现了自适应巡航控制系统跟车目的.     

基于模型预测控制的汽车底盘集成控制

为了实现汽车底盘的集成控制,提出了模型预测控制(MPC)方法。在紧急避障操纵时,利用四轮差动制动和主动前轮转向集成控制的方式实现对汽车理想操纵特性的跟随。上层集成控制器通过模型预测控制方法决策下层子控制器所需的附加横摆力矩和前轮附加转角。建立了七自由度非线性车辆模型作为预测模型,并通过实车试验对其精度进行了验证。在Carsim中的虚拟试验对模型预测控制方法的控制效果进行了验证。     

Implementation of MPC-Based Trajectory Tracking Considering Different Fidelity Vehicle Models

In order to investigate how model fidelity in the formulation of model predictive control(MPC) algorithm affects the path tracking performance, a bicycle model and an 8 degrees of freedom(DOF) vehicle model, as well as a 14-DOF vehicle model were employed to implement the MPC-based path tracking controller considering the constraints of input limit and output admissibility by using a lower fidelity vehicle model to control a higher fidelity vehicle model. In the MPC controller, the nonlinear vehicle model was linearized and discretized for state prediction and vehicle heading angle, lateral position and longitudinal position were chosen as objectives in the cost function. The wheel step steering and sine wave steering responses between the developed vehicle models and the Carsim model were compared for validation before implementing the model predictive path tracking control. The simulation results of trajectory tracking considering an 8-shaped curved reference path were presented and compared when the prediction model and the plant were changed. The results show that the trajectory tracking errors are small and the tracking performances of the proposed controller considering different complexity vehicle models are good in the curved road environment. Additionally, the MPC-based controller formulated with a high-fidelity model performs better than that with a low-fidelity model in the trajectory tracking.     

基于串级MPC和EPS的集成驾驶员转向车道线保持

针对电动助力转向( EPS)作为转向执行机构的车道线保持的控制系统设计及保留驾驶员对车辆操控问题,提出基于串级模型预测控制( MPC)和EPS集成驾驶员转向的车道线保持系统. 在车道线识别视觉系统空间,建立车道线保持状态空间模型,设计基于MPC的车道线保持控制器( LMPC) . 建立EPS状态空间模型,设计基于MPC的EPS车辆前轮转角控制器( EMPC) . LMPC与EMPC经逆转向机构模型组成串级控制结构. 分析驾驶员转向对车道线保持控制的影响,进而通过保留驾驶员对车辆控制来提高处理紧急事件的能力. 仿真结果表明:在不同车速和不同曲率道路下,该控制策略均能快速消除横向位置偏差和航向角偏差,保证车辆沿着车道线行驶,具有较好的适应性和鲁棒性. 驾驶员转向可以改善车道线保持和提高车辆主动安全性.     

行星式混合动力客车的模型预测动态协调控制

行星式混合动力客车在驱动模式切换时会产生较大冲击度,以往基于PID的控制器在模式切换过程中无法有效保证车辆驾驶平顺性.基于此问题,利用模型预测控制(Model Predictive Control,MPC)可以在线滚动优化获得最优控制序列的特点,提出了一种基于MPC的动态协调控制方法,实现发动机的启动控制.依据整车动力学方程和实车历史数据,在Matlab/Simulink平台中搭建基于数据驱动的发动机模型和车辆闭环仿真模型,并将发动机启动过程视为受约束的多目标优化问题,根据系统状态空间方程和优化问题设计基于数据驱动的模型预测控制器,在纯电动模式向混合动力模式切换过程中,与传统基于PID的控制方法以及被动切换展开对比.仿真结果表明,在保证车辆动力性的前提下,相比于PID控制方法和被动切换,在模式切换过程中,基于MPC的动态协调控制方法不仅可以实现发动机的正常启动,还能大幅度降低峰值冲击度,同时使车辆良好地跟随目标车速.本文提出的模型预测控制器可以降低整车冲击度,保证车辆模式切换时的平顺性.     

事件触发机制下的工业过程多速率模型预测控制方法

随着工业互联网的发展,为避免网络拥塞,控制系统正在由时间触发向事件触发控制方向发展。本文针对一类多速率工业过程,将模型预测控制与提升技术和事件触发技术相结合,提出了事件触发机制下的多速率模型预测控制方法。该方法首先采用提升技术解决多速率问题,进而采用模型预测控制(Model Predictive Control, MPC)方法设计控制器以跟踪设定值;在此基础上,设计保证系统稳定的事件触发机制,使控制器仅在违反触发机制时更新。通过对典型工业磨矿过程仿真实验来验证本文所提方法的有效性,结果表明所提方法能够有效减少信道资源的占用和计算负载,为工业互联网环境下工业过程控制器设计提供了新的方法。     

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance. Aiming at this for hybrid electric vehicles (HEV) during the car-following (CF) and lane-change (LC) process while accelerating, a hierarchical control strategy for vehicle stability control is proposed. This new approach is different from the conventional hierarchical control. On the basis of model predictive control (MPC) theory, a two-layer MPC controller is designed at the top level of the control structure. The upper layer is a linear time-varying MPC (LTV-MPC), while the lower layer is a hybrid MPC (HMPC). For the LTV-MPC controller, a control-oriented linear discrete model for HEV is established, which integrates the dynamic model with three degrees of freedom (DOF) and the car-following model. The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic (MLD) model of the HEV powertrain. As for the bottom level, a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established. In addition, the system stability is proven. A deep fusion of vehicle dynamics control and energy management is achieved. Compared with LC-ACC control and conventional ACC control, the simulation and the hardware-in-the-loop (HIL) test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions. Additionally, the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.

     

椭圆轨道编队构型保持控制方法研究

针对椭圆参考轨道的编队构型保持控制系统,以对地等张角绕飞编队为背景设计了相对轨道模型预测控制算法.首先,基于哈密尔顿-雅克比方程,推导了一组包含项摄动的相对运动模型.在此模型的基础上,结合具体的对地等张角编队任务,提出了一种构型保持的在线滚动优化的闭环预测控制算法,并将模型预测优化问题转换为在线求解二次规划问题,该算法能够较好地控制存在状态约束、控制约束等各种约束控制问题.最后,通过数值仿真验证该算法的有效性.     

Industrial applications of advanced control techniques

This paper discusses two industrial control applications using advanced control techniques. They are the optimal-tuning nonlinear PID control of hydraulic systems and the neural predictive control of combustor acoustic of gas turbines. For hydraulic control systems, an optimal PID controller with inverse of dead zone is introduced to overcome the dead zone and is designed to satisfy desired time-domain performance requirements. Using the adaptive model, an optimal-tuning PID control scheme is proposed to provide optimal PID parameters even in the case where the system dynamics is time variant. For combustor acoustic control of gas turbines, a neural predictive control strategy is presented, which consists of three parts: an output model, output predictor and feedback controller. The output model of the combustor acoustic is established using neural networks to predict the output and overcome the time delay of the system, which is often very large, compared with the sampling period. The output-feedback c     

Robustly stable model predictive control based on parallel support vector machines with linear kernel

Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.     

孤岛运行光储微电网控制策略研究

光储微电网孤岛运行时存在电能质量差、系统稳定性差等问题。系统的控制策略多为基于PI控制的双闭环控制算法,导致动态响应速度慢。针对这一问题,提出了一种考虑新能源波动的多步预测有限集模型预测控制（FCS-MPC）策略。首先,对DC-DC变换器采用稳压控制,为逆变器提供稳定的直流电压提高系统的稳定性;然后,对光伏逆变器采用恒功率控制策略维持稳定的功率输出,对储能逆变器采用基于多步长改进模型预测控制（MPC）的下垂控制,以实现对参考电压的快速跟踪及负荷功率的合理分配,而且多步长改进MPC可降低传统MPC的预测误差,提高系统的稳定性;最后,利用MATLAB搭建仿真模型,仿真结果验证了所提控制策略的有效性。     

基于ARM的嵌入式自适应温度控制系统设计

针对传统电阻炉温控系统受时滞长、惯性大和非线性等因素影响而普遍存在超调量大和调节时间长等问题,设计了一种基于ARM的嵌入式自适应温度控制系统.采用基于神经网络的PID自适应控制器对温度进行准确控制,提出了模糊Smith预估补偿控制方法来消除纯滞后系统的超调并提高稳定性,并将该方法与传统PID和模糊PID的控制方法进行了比较.结果表明,该系统能够实现电阻炉温度的快速和准确控制.相对于其他两种方法,该方法在系统超调和调节时间方面有极大提高,增强了系统的鲁棒性,具有较好的工程应用前景.     

长时滞网络控制系统的Smith预估模糊PI控制器的设计

随着控制系统规模的不断扩大,控制网络的应用越来越广泛,由此产生了网络控制系统.在网络控制系统中由于网络带宽的限制,不可避免的使系统中存在网络所诱导的时延,它是导致系统性能下降甚至不稳定的一个潜在因素.针对网络所诱导的长时延,将Smith预估器应用到网络控制系统中,并通过与模糊PI控制方法相结合,构成Smith预估模糊PI控制器,设计出的控制器使闭环系统具有较好的性能和稳定性,并对于模型失配的情况具有较高的鲁棒性.仿真结果表明了所提出方法的有效性.     

多变量对象的预测PI控制及其工业应用

针对强耦合、大时滞多变量系统．首先将系统进行动态解耦,消除系统之间的耦合,然后将解耦对象简化为互相独立的一阶滞后对象,最后运用单变量预测PI控制算法设计这些一阶滞后对象的控制器．这种控制器结构简单,可调参数少,参数调节方便、直观．仿真和实际应用表明：此类预测PI控制器具有良好的控制性能和鲁棒稳定性能．     

长时延网络控制系统的H_2/H_∞混合控制

提出了具有长时延的网络控制系统的一种新模型,利用信息接收缓冲区将不确定的长时延转变成在几个确定时延间进行转换,从而将延迟扩大化的程度减小。根据离散马尔可夫跳跃线性系统理论,提出了网络控制系统的H2/H∞混合控制问题,得到通过求解线性矩阵不等式来获得鲁棒均方稳定的跳跃反馈控制器的设计方法,设计出的控制器计算简单且易实现。将结论应用到三容系统中,仿真结果表明了此方法的有效性和优越性。     

Real-time routing control design for traffic networks with multi-route choices

This work considers those road networks in which there are multi-route choices for bifurcation-destination(or origin-destination) pairs, and designs a real-time variable message sign(VMS)-based routing control strategy in the model predictive control(MPC) framework. The VMS route recommendation provided by the traffic management authority is directly considered as the control variable, and the routing control model is established, in which a multi-dimensional control vector is introduced to describe the influence of route recommendations on flow distribution. In the MPC framework, a system optimum routing strategy with the constraints regarding drivers' acceptability with recommended routes is designed, which can not only meet the traffic management authority's control requirement but also improve drivers' satisfaction with the route guidance system. The simulation carried out shows that the proposed routing control can effectively mitigate traffic congestion, reduces followers' time delay, and improves drivers' satisfaction with routing control in road networks.     

船舶航向反步自适应控制

针对反步控制算法控制参数多、难整定的问题,经过理论分析提出基于期望指标的二阶系统参数整定方法.将系统变换为二阶系统形式,在选取闭环系统的自然频率、阻尼、闭环系统调节时间与超调量后,确定系统闭环极点,由极点整定了两个关键控制参数,积分增益取值参考PID控制器中积分时间参数的方法整定.在海浪干扰和船舶模型参数出现很大摄动的情况下,对所设计控制器进行了仿真试验,结果表明,所设计的控制器具有很好的鲁棒性,提出的整定控制器参数方法是可行的.