Robust synchronisation tracking control of networked Euler–Lagrange systems using reference trajectory estimation based on virtual double-integrators

This paper considers the problem of distributed synchronisation tracking control of multiple Euler–Lagrange systems on a directed graph which contains a spanning tree with the leader node being the root. To design the high performance distributed controllers, a virtual double-integrator is introduced in each agent and is controlled by a virtual distributed linear high-gain synchronisation tracking controller, so that the position and velocity of each agent track those of the reference trajectory with arbitrarily short transient time and small ultimate tracking error. Then taking the double-integrator's position and velocity as the estimates of those of the reference trajectory, in each generalised coordinate of each Euler–Lagrange agent, a local controller with a disturbance observer and a sliding mode control term is designed, to suppress the mutual interactions among the agents and the modelling uncertainties. The boundedness of the overall signals and the synchronisation tracking control performance are analysed, and the conditions for guaranteed control performance are clarified. Simulation examples are provided to demonstrate the performance of the distributed controllers.     

光导AGV智能循迹测控系统的建模与仿真

针对光导式差速驱动型自动导引车的循迹控制系统,构建了一个简化的系统数学模型,并在此基础上进行了控制器的设计,然后通过Matlab/Simulink对系统进行了仿真分析。通过仿真比较了状态反馈控制和模糊控制的控制性能,提出了将这两种控制方式结合的复合控制方法,并得到了较好的仿真结果。该仿真系统模拟的参数接近车体的实际数据,对于实际小车的控制具有一定的参考价值,为今后AGV控制系统的设计提供了依据。     

Nonsmooth multi-objective synthesis with applications

Nonsmooth optimization is used to design feedback controllers subject to closed-loop performance specifications both in time and frequency-domains. In time domain the nonlinear plant is submitted to a set of test input signals and the closed-loop responses so generated are called scenarios. A design technique is proposed which computes a controller with a prescribed structure that satisfies performance specifications for a given set of scenarios in tandem with robustness constraints in the frequency-domain, and is locally optimal among other controllers with these properties.     

The Indirect Shared Steering Control Under Double Loop Structure of Driver and Automation

Due to the critical defects of techniques in fully autonomous vehicles, man-machine cooperative driving is still of great significance in today’s transportation system. Unlike the previous shared control structure, this paper introduces a double loop structure which is applied to indirect shared steering control between driver and automation. In contrast to the tandem indirect shared control, the parallel indirect shared control put the authority allocation system of steering angle into the framework to allocate the corresponding weighting coefficients reasonably and output the final desired steering angle according to the current deviation of vehicle and the accuracy of steering angles. Besides, the active disturbance rejection controller (ADRC) is also added in the frame in order to track the desired steering angle fleetly and accurately as well as restrain the internal and external disturbances effectively which including the steering friction torque, wind speed and ground interference etc. Eventually, we validated the advantages of double loop framework through three sets of double lane change and slalom experiments, respectively. Exactly as we expected, the simulation results show that the double loop structure can effectively reduce the lateral displacement error caused by the driver or the controller, significantly improve the tracking precision and keep great performance in trajectory tracking characteristics when driving errors occur in one of driver and controller.      

牵引车–飞机系统的自适应滑模变结构控制

将自动转向技术应用于牵引车–飞机系统, 并以侧偏位移和相对横摆角作为反馈, 提出一种牵引车四轮主动转向控制策略. 重点考虑牵引车和飞机的侧向和横摆运动, 建立含铰接角在内的牵引车–飞机系统非线性动力学模型. 将牵引车和飞机的轮胎侧偏刚度视为有界的不确定性参数, 将侧向风等因素视为未知的外在扰动, 采用自适应滑模变结构控制方法设计牵引车转向角控制器. 仿真结果表明, 设计出的前、后轮转向控制器能使控制系统同时获得很好的轨迹跟踪性和操纵稳定性, 并且能够有效的克服参数摄动和外界干扰对系统操作性的影响.     

Decentralized adaptive stabilization in the presence of unknown backlash-like hysteresis

Due to the difficulty of handling both hysteresis and interactions between subsystems, there is still no result available on decentralized stabilization of unknown interconnected systems with hysteresis, even though the problem is practical and important. In this paper, we provide solutions to this challenging problem by proposing two new schemes to design decentralized output feedback adaptive controllers using backstepping approach. For each subsystem, a general transfer function with arbitrary relative degree is considered. The interactions between subsystems are allowed to satisfy a nonlinear bound with certain structural conditions. In the first scheme, no knowledge is assumed on the bounds of unknown system parameters. In case that the uncertain parameters are inside known compact sets, we propose an alternative scheme where a projection operation is employed in the adaptive laws. In both schemes, the effects of the hysteresis and the effects due to interactions are taken into consideration in devising local control laws. It is shown that the designed local adaptive controllers can ensure all the signals in the closed-loop system bounded. A root mean square type of bound is obtained for the system states as a function of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters. With Scheme II, the proposed control laws allow arbitrarily strong interactions provided their upper bounds are available. In the absence of hysteresis, perfect stabilization is ensured and the L₂ norm of the system states is also shown to be bounded by a function of design parameters when the second scheme is applied.     

Fastest recovery after feedback disruption

The problem of quickly reducing operating errors during recovery from a feedback disruption is considered. The objective is to design controllers that reduce operating errors as quickly as possible, once feedback has been restored. It is shown that robust optimal feedback controllers that achieve this objective do exist. Furthermore, it is shown that the performance of optimal controllers can be approximated as closely as desired by controllers that generate bang–bang input signals for the controlled system. Controllers that generate bang–bang signals are relatively easy to derive and implement, since bang–bang signals are characterised by a finite list of scalars – their switching times.     

基于驾驶员转向模型的共享控制系统EI北大核心CSCD

针对车辆驾驶对于共享控制系统实用性的需求,提出了基于驾驶员转向模型的共享控制系统.基于驾驶员的视觉预瞄特性与神经肌肉特性建立了驾驶员转向模型,通过遗传算法辨识模型参数并分析其与车速和道路曲率之间的函数关系;采用模糊权重分配策略合理分配驾驶权重;本文利用基于所开发的CarMaker驾驶模拟实验平台,对系统进行在线测试和验证,结果表明该系统不仅能够更好地提升车辆的轨迹跟踪精度和安全性,辅助驾驶员转向,还能够极大地减轻驾驶员负荷.     

Multidisciplinary hybrid hierarchical collaborative optimization of electric wheel vehicle chassis integrated system based on driver’s feel

The optimization design of chassis integrated system mainly involves steering, suspension and brake subsystems, which is essentially a multidisciplinary design optimization. This paper mainly researches the multidisciplinary optimization of the chassis integrated system for the electric wheel vehicle, from the view of ensuring a favorable feel for the driver. The dynamic models of differential steering system, brake system, active suspension system and vehicle are established. Then, taking the coupling relationship of the chassis subsystems into account, this paper proposes an evaluating index of driver’s ride comfort (Drc), which is composed of the steering road feel, brake feel and suspension ride comfort. In order to determine the weight coefficient in the quantization formula of Drc, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to overcome the subjectivity in the selection. Based on these, a multidisciplinary hybrid hierarchical collaborative optimization (HHCO) method is proposed on the basis of the collaborative optimization (CO), which consists of a system level coordinator and a coupling analyzer to solve the problem of poor convergence and the low efficiency of CO method. The optimization results show that the proposed HHCO method has excellent computational efficiency and better convergence compared with the CO method, which can further improve the steering road feel and the drive ride comfort, on the premise of ensuring the brake feel and suspension ride comfort.     

一类非完整约束动力学系统的人工场导向控制

对于一类具有轮式移动机构的非完整动力学系统,本文通过建立人工场的方法来实 现其位姿镇定、轨迹跟踪和路径跟踪等控制问题.人工场用于导向和控制方向角,而通过辅助 的线速度控制以获取最佳收敛路径. 控制器设计中同时兼顾动力学扰动及实际系统速度和输 出力矩的饱和限制,所得控制器对于跟踪问题仅需知道期望位姿,而且结构简单、鲁棒性强、 便于实现.     

基于IMXRT1021嵌入式竞速智能车设计

为了提升智能车的竞速能力,本文从组装、关键器件选型、硬件设计、电路板设计、传感器信号处理、算法和控制等方面,介绍了基于IMXRT1021竞速智能车系统.包括机械部分、硬件部分、PCB设计、传感器信号处理、赛道元素识别算法、控制策略和软件设计架构等内容.本文详细阐述了各个竞赛元素的识别与控制方案,并且通过实验对比智能车的行驶轨迹和绝对速度,分析了不同控制算法对其完赛时间、稳定性等重要技术指标的影响.本文的设计方案具有控制精确、转向灵敏、路径规划能力优秀等优点,对于备战全国大学生智能车竞赛四轮组的选手具有很好的参考价值.     

基于CAN_FD总线的线控制动系统设计

针对于汽车单轮制动执行器故障问题,提出了冗余结构系统设计方案和三轮协同制动力分配策略,其中冗余结构系统设计通过双中央控制器,使制动执行器故障时汽车侧向偏移距离缩短,三轮协同制动力分配策略采用汽车行驶速度和驾驶员制动强度相结合的方式,确保汽车在单轮制动执行器故障时可以安全稳定停车;完成了线控制动系统的软硬件设计,搭建了实物验证平台,该平台共设计了6个控制节点,节点之间通过CAN_FD总线进行通信;实验结果表明,在单轮制动执行器故障时,冗余结构系统设计相比于非冗余结构系统设计在不同制动强度下最大缩短了15.85%的侧向偏移距离,三轮协同制动力分配策略可以确保安全稳定停车。     

汽车转向/防抱死制动系统的无模型协同控制

汽车制动系统和转向系统相互之间存在着复杂的耦合关系,会对汽车行驶安全性和操纵稳定性造成极大的影响。为了动态补偿这种干扰影响,以无模型控制方法设计汽车整车防抱死制动控制器、整车前轮主动转向控制器及转向系统和制动系统的协同控制器,从理论上证明了设计的无模型控制系统的稳定性。最后,在MATLAB/Simulink平台上搭建了车辆模型和控制器,进行汽车转向制动控制的动态性能仿真。仿真结果表明其解决了汽车两个系统的耦合干扰,提高了汽车制动效能和转向稳定性。     

用于汽车耐久性试验的驾驶机器人模糊控制仿真

介绍一种用于汽车耐久性试验驾驶机器人模糊控制方法。驾驶机器人由交流伺服电机、控制器、滚珠丝杠以及机械定位装置组成。阐述了模糊控制的原理、模糊PID控制器的设计规则和设计过程。在MATLAB环境仿真模拟机器人位置伺服系统,表明了模糊PID控制器的有效性。     

MPC-based approach to optimized steering for minimum turning radius and efficient steering of multi-axle crane

In a conventional steering system for a multi-axle crane, the steering angle of each axle is determined according to Ackermann’s steering principle, which minimizes the slip angles of the tires. The role of optimal steering control in improving a driver’s steering efficiency is hardly considered in Ackermann’s principle. To address this problem, this paper proposes a control strategy for determining the optimal steering angles for a multi-axle crane and thereby improving a driver’s steering efficiency by applying the model predictive control (MPC) algorithm and defining a driver’s intentions. A simplified crane model for the steering system was developed using a bicycle model, and a comparative study was carried out via simulation to analyze steering performance for the conventional (Ackermann) and proposed steering control systems for the cases of all-wheel steering and road steering modes. The simulation results show that both the minimum turning radius and the driver’s steering effort are decreased more by the proposed steering control system than by conventional system and that the proposed control strategy therefore yields better steering performance.

     

On the design of approximate non‐linear parametric controllers

This paper focuses on the design of non‐linear parametric controllers, around a nominal input/output trajectory of a discrete‐time non‐linear system. The main result provided herein is a relationship between the tracking performance of the closed‐loop control system in the neighbourhood of a nominal trajectory, and some local features (the first‐order linear approximations about the nominal trajectory) of the non‐linear mappings which characterize the plant and the feedback controller. Such a result can be used to predict the dynamic behaviour of the control system, and to reduce the computational complexity of the optimization task associated with the tuning of the parametric feedback controller. Copyright © 2000 John Wiley & Sons, Ltd.     

Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using Cooperative Game Theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.      

Inverse optimal sliding mode control of spacecraft with coupled translation and attitude dynamics

This paper proposes two robust inverse optimal control schemes for spacecraft with coupled translation and attitude dynamics in the presence of external disturbances. For the first controller, an inverse optimal control law is designed based on Sontag-type formula and the control Lyapunov function. Then a robust inverse optimal position and attitude controller is designed by using a new second-order integral sliding mode control method to combine a sliding mode control with the derived inverse optimal control. The global asymptotic stability of the proposed control law is proved by using the second method of Lyapunov. For the other control law, a nonlinear H_∞ inverse optimal controller for spacecraft position and attitude tracking motion is developed to achieve the design conditions of controller gains that the control law becomes suboptimal H_∞ state feedback control. The ultimate boundedness of system state is proved by using the Lyapunov stability theory. Both developed robust inverse optimal controllers can minimise a performance index and ensure the stability of the closed-loop system and external disturbance attenuation. An example of position and attitude tracking manoeuvres is presented and simulation results are included to show the performance of the proposed controllers.     

基于卷积神经网络的手术机器人控制系统设计

针对传统系统控制精准度低的问题,提出了基于卷积神经网络的手术机器人控制系统设计。根据基于卷积神经网络的手术机器人控制原理,设计控制系统总体结构,选用PCI插槽上直接内插CAN适配卡作为上位机核心组件,采用C++编写的Lib库和DLL库为驱动程序提供适配卡。通过下位机三个节点,处理相关信号,并进行量程转换和越限判断,确保机器人不会失控。选用80C592微控制器设计关节驱动节点结构,以高速工作方式向控制器提供向总线的差动发送和接受能力,避免外界干扰。设计基于视觉的持镜臂,为手术过程提供上下、左右、前后的运动的手术视野。分别采用FN3002力传感器和MPS-M拉线式位移传感器获取相关传感数据,采用卷积神经网络深度学习方法,设计持镜臂运动控制步骤,采用VC++6.0工具,控制软件程序,避免抖动或者误操作主手现象的出现。由实验结果可知,该系统持镜臂轨迹规划与期望轨迹一致,简化了控制系统的复杂性。