参考轨迹自校正的广义预测算法

为解决广义预测控制的跟踪速度较慢问题,提出了一种广义预测控制参考轨迹自校正策略。首先采用一种假定参考轨迹,然后根据仿真分析和数学推理,推出自校正的参考轨迹广义预测算法,最后通过MATLAB仿真比较两种算法的跟踪速度。结果表明,该算法不仅能够使系统响应沿着事先规划好的期望轨迹前进,并在指定时刻到达设定值。     

四轮转向智能车辆轨迹跟踪及稳定控制研究

为了改善智能车辆轨迹跟踪过程中的行驶稳定性,针对四轮转向车辆提出了一种轨迹跟踪及稳定控制方法。首先建立了车辆三自由度动力学模型,然后应用模型预测控制算法设计轨迹跟踪控制器。考虑了四轮转向车辆的动力学特性和不同路面附着对轮胎侧偏角控制的影响,在跟踪算法中引入零质心侧偏角控制和动态轮胎侧偏角边界控制方法,实现车辆的稳定控制。最后,通过对接路面工况下的仿真,验证了所提出的控制方法能够保证车辆在轨迹跟踪过程中具有良好的稳定性。     

分布式驱动智能汽车轨迹跟踪的协同控制

针对车辆在轨迹跟踪过程中，尤其是高速转向等极限工况下，易出现车辆跟踪精度差和失稳的问题，以分布式驱动智能汽车为研究对象，提出一种考虑横向稳定性的轨迹跟踪协同控制策略。首先，建立车辆纵向、横向以及横摆运动的三自由度动力学模型，设计了基于模型预测控制的主动转向控制器，通过优化求解得到跟踪期望轨迹的最佳前轮转角。然后，采用滑模控制设计横摆力矩控制器，将横摆角速度和质心侧偏角作为联合变量，利用积分二自由度控制模型，计算车辆稳定的等效附加横摆力矩。最后，采用二次规划算法设计最优力矩分配控制器，以满足总的驱动力矩和附加横摆力矩的控制需求。仿真试验结果表明，控制系统在极限高速工况下，能够使车辆精确、稳定的跟踪期望轨迹。     

基于模型预测控制的排爆机器人轨迹跟踪算法研究EI北大核心CSCD

排爆机器人在复杂、危险环境下代替人作业拥有非常重要的地位。排爆机器人对期望轨迹的跟踪是避免发生危险最重要的环节。基于模型预测控制算法提出了排爆机器人末端轨迹跟踪算法。给出了机械臂状态空间的离散模型,已知当前时刻位置状态和下一时刻位置输入状态,预测未来某时域内的输出状态;然后根据给定性能指标和约束条件,求解未来一段时域内的输入序列,对实际输出进行反馈校正;最后,采用实验仿真对比测试,验证了该算法的有效实时性,结果表明,基于模型预测控制的机器人末端轨迹跟踪算法具有良好的动态实时跟踪能力,实现了目标轨迹的有效跟踪。     

基于自适应运动学模型的镜像型迭代学习控制

针对一类具有对称期望轨迹跟踪的工业机器人系统,在运动学模型参数存在偏差的情况下,提出一种基于自适应运动学模型的镜像型迭代学习控制方法.首先,利用期望轨迹的对称特征,将其以中点为界分解成前后两个半轨迹,交替使用两个半轨迹的控制信息,不断优化调整对应半轨迹下次迭代的控制输入,缩短迭代学习周期,实现了具有镜像特征轨迹的迭代学...     

智能电动车横向控制策略研究

针对智能电动车自动横向控制,建立了车辆横向动力学模型,设计了电动车转向控制器的模型预测(MPC)算法。将前轮转角作为控制输入变量,与期望轨迹的横向距离偏差、横摆角偏差及两者的变化率作为状态变量,控制器对车辆未来的状态变量进行预测,输出最优前轮转角,实现智能横向控制。在控制过程中,同时引入期望状态参数和系统松弛因子,优化车辆行驶状态。利用软件进行联合仿真,并进行实车试验。研究结果表明:控制器均能迅速响应,消除偏差,使车辆快速回到期望轨迹,保证车辆稳定平顺地行驶。     

机械臂的改进固定时间滑模控制方法设计

当机械臂末端对给定轨迹进行跟踪控制时,跟踪误差收敛速度容易受初始跟踪误差大小的影响,针对这一问题设计了一种适用于机械臂模型的改进固定时间滑模轨迹跟踪控制策略.在快速终端滑模面的基础上,设计了一种固定时间滑模面,从而使得控制器具有固定时间收敛特性并给与证明;针对滑模控制伴随抖震的特性,对滑模控制器的趋近律进行了抑制抖振的改进,使得趋近律具有一定的自适应性.通过对二自由度机械臂的仿真实验,验证了在系统含有未知扰动的情况下,设计的改进固定时间滑模控制器能够在固定时间内使得机械臂末端轨迹跟踪误差快速收敛,且通过控制器参数的调整能够达到更快的收敛速率.通过仿真对比,验证了论文设计方法的收敛速率要快于快速终端滑模控制方法.     

多变量DMC预测控制在MGT-CCHP系统中应用

MGT-CCHP系统可以减少温室气体排放,具有节能、环保和高安全性等优势,是提供清洁、可靠、高质量、多用途的小型分布式能源的最佳方式之一。将MGT-CCHP系统作为研究对象,通过多变量动态矩阵(DMC)进行预测控制,结合S函数,用Simulink仿真模型,合理选取控制参数,可有效地控制MGT-CCHP模型,较快地跟踪期望值,达到预期的控制效果。     

基于线性时变模型预测控制的自主车辆轨迹跟踪控制器设计与验证

随着自动驾驶技术的快速发展,精确的轨迹跟踪已经成为汽车工业和学术领域公认的实现自主车辆运动控制的核心技术之一。为提高自主车辆轨迹跟踪的实时性与准确性,提出一种应用于自主车辆的线性时变模型预测跟踪控制器（Linear time-varying model predictive controller,LTV-MPC）设计方法。根据运动学原理建立某自主无人小车的二自由度运动学模型,其次,基于该模型构建车辆轨迹跟踪系统的误差模型并利用线性参数化理论对其进行离散化,在模型预测控制框架内将该轨迹跟踪控制器的设计转化为一个线性二次规划最优问题。在一个实际搭建的自主车辆试验平台上对所提出控制器的有效性进行不同预设参考路径轨迹下的实车验证,结果表明,该自主车辆能够对所预设的实际参考道路轨迹进行快速、准确的轨迹跟踪控制,且具有较好的行驶稳定性能。     

移动机器人的离散迭代学习控制

移动机器人是一个集环境感知、动态决策与规划、运动控制与执行等多种功能于一体的综合自动化系统,对移动机器人的运动轨迹跟踪静、动态特性的要求也越来越高。迭代学习控制是一种能够有效地处理重复跟踪控制问题或抑制干扰性问题,提高系统性能的先进控制方法,能在给定的时间范围内实现未知对象实际运行轨迹以高精度跟踪给定期望轨迹,且不依赖精确数学模型。建立了移动机器人的运动模型,将迭代学习控制应用于具有重复运动性质的移动机器人,完成了迭代学习控制结构和控制率设计,通过仿真可见,该系统稳定收敛,能实现移动机器人良好的轨迹跟踪。     

Well-conditioned model predictive control

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.     

Practical motion control with acceleration reference for precision motion—New NCTF control and its application to non-contact mechanism

This paper presents an improved practical controller for enhancing precision motion performance. For practical use, high motion control performance and ease of controller design are desired. A nominal characteristic trajectory following control (NCTF control) has been studied to satisfy the desired response. The NCTF controller consists of a nominal characteristic trajectory (NCT) which is the reference motion of control system and a compensator which makes the motion of the controlled object to follow the NCT. The NCT is easily determined from experimental open-loop time responses of the mechanism. The controller parameters can be also determined easily, without any given model parameters. In the present paper, the Continuous Motion NCTF controller for high continuous motion performance is improved in order to enhance the following characteristic of the object motion on NCT and improve the positioning and tracking accuracies of the system. The improved Continuous Motion NCTF controller (referred to as Acceleration Reference-Continuous Motion NCTF controller (AR-CM NCTF controller)) provides the advantages such as the high overshoot reduction characteristics and the low sensitivity disturbance. The AR-CM NCTF controller includes the structure of the Continuous Motion NCTF controller and acceleration reference for the object motion as the additional controller elements. The design procedure of the AR-CM NCTF controller remains easy and practical. In order to confirm the advantages, the AR-CM NCTF controller was examined in positioning and tracking motion performances using the non-contact mechanism. The experimental results prove that the AR-CM NCTF controller achieves the better positioning and tracking performances than the Continuous Motion NCTF controller.     

Application of infinite model predictive control methodology to other advanced controllers

This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.     

二次调节加载系统的预测控制研究

分析了二次调节加载系统的特性。针对系统的特点，提出一种动态矩阵预测控制（DMC）和PID相结合的方法。通过内环来克服随机干扰，外环采用预测控制来获得优良的跟踪性能和鲁棒性，实现目标和控制的分层，对有关设计参数的选择进行了讨论。仿真试验结果表明：动态矩阵预测控制和PDI相结合的方法有效地克服了系统时变、干扰等未知影响，有较高的控制精度和较强的鲁棒性，可获得满意的控制效果。     

城市轨道列车速度曲线预测函数控制改进算法

为有效解决城市轨道列车速度曲线跟踪控制问题,提出了一种速度曲线预测函数控制改进算法IPFC。所提出的预测函数控制改进算法选用阶跃函数和Morlet小波函数作为基函数,其依据目标速度曲线的变化程度,给出了一种预测函数控制的基函数选择策略,该策略可实现小波与阶跃基函数的切换。除此之外,给出了一种基于系统性能模糊满意度与优化因子的柔化因子非线性自适应在线调整策略,通过调整优化因子能够进一步提升跟踪控制性能。基于大连城市轨道交通12号线中旅顺新港至铁山镇的城市轨道列车速度曲线跟踪控制场景,在硬件在环实验环境下得到的实验结果表明,所提出的IPFC能够显著改善控制系统的控制性能,节能、准时、停车精确、舒适4个性能指标都有明显的改善,特别是准时与精确停车,相比广泛应用于实际的传统改进算法均大幅提升超过了55%。     

Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace

An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.     

液压挖掘机器人轨迹跟踪控制综合策略方案研究

针对液压挖掘机器人工作装置和液压系统高度非线性,单一的控制策略不能实现有效精确控制的特点。提出了挖掘机器人轨迹跟踪控制的综合策略。在传统的轨迹规划器中,加入了轨迹优化模块;在关节伺服控制中,采用了基于规则的智能PID控制策略;在泵控制中,采用了改进的负流量节能控制技术。实验结果证实了这种方案的有效性,可以在很大程度上提高挖掘机器人的轨迹跟踪精度,使操作更加平稳。     

卫星激光通信粗跟踪系统复合控制策略

针对卫星激光通信粗跟踪系统的跟踪稳定精度指标要求高的特点,建立了基于永磁同步电机直接驱动的粗跟踪系统模型并进行了跟踪稳定误差分析。在采用传统PID控制策略存在跟踪误差较大、动态性能较差等不足的基础上,提出了一种基于粗跟踪系统的改进型前馈复合控制策略。从理论上分析了前馈复合控制策略提高了粗跟踪系统的动态性能,为降低跟踪稳定误差提供了改善方案。地面实验验证结果表明,与传统控制策略相比,改进型复合控制策略极大地降低了系统的动态跟踪误差,动态跟踪误差由606μrad降低至13μrad(1δ)。进一步的在轨实验也验证了改进型复合控制策略的合理性和先进性,总体指标满足了卫星激光通信终端的极高精度要求。本文所提出的控制策略对其他高性能跟踪伺服系统设计也具有较大的借鉴意义。     

基于改进遗传算法的电液伺服系统轨迹控制

为了提高挖掘机器人电液伺服系统的轨迹精度,首先,建立挖掘机器人电液伺服系统模型;其次,对遗传算法的种群、适应度函数、交叉概率和变异概率进行改进,设计改进遗传算法的PID控制器,在联合仿真平台上进行了仿真研究,用阶跃和斜坡信号评估控制器性能;最后,搭建挖掘机器人轨迹控制实验平台,采用对挖掘机器人精度要求较高的斜坡作业验证控制器性能。结果表明:相比较于传统的PID控制器和经典遗传算法优化的PID控制器,改进遗传算法优化的PID控制器调整时间短,响应快速,实际动作控制时轨迹跟踪误差最小,可用在挖掘机器人实际轨迹控制中。