基于遗传算法的P—F—PI控制器人手臂定位控制中的应用

提出用遗传算法来优化控制参数的Ｐ－Ｆ－ＰＩ（比例－模糊－比例积分）控制器,控制机器人手臂定位系统。Ｐ－Ｆ－ＰＩ控制器是在大偏差时用比例控制,在中偏差时用Ｆ控制,接近稳态时用ＰＩ控制,而这三个控制器的切换参数以及Ｆ控制器中的修正系统用遗传算法来优化。实验证明,该控制方法能满足机器人手臂定位控制动态和静态要示求。     

机器人柔性手臂混合ITAE最佳鲁棒控制研究

机器人柔性手臂动力学模型的复杂性及客观系统中的不确定因素,使传统的控制系统很难达到预定的控制要求,寻求鲁棒性强的控制策略势在必行。针对模型参数及扰动的不确定性,进行混合ITAE最佳控制、H∞PID鲁棒控制策略研究,同时利用遗传算法(GA)的隐含并行性和全局搜索特点整定控制器的控制参数以达到混合ITAE、H∞优化性能,并用MATLAB软件进行数值仿真,结果表明这种控制设计方法适用于柔性机器人手臂的控制。     

基于GA的模糊控制器在弯辊控制中的应用

UC轧机中间辊弯辊控制回路的数学模型具有很强的时变性和不确定性,为实现其精确控制,设计了一种基于遗传算法的模糊控制器并将其应用于该控制回路中。系统利用遗传算法来优化模糊控制器的隶属函数及量化因子和比例因子的初值,并且根据模糊控制查询表的输出来在线调整量化因子和比例因子。仿真结果表明,用该方法设计的模糊控制器具有一定的自适应能力,将该控制器应用于UC轧机中间辊弯辊控制回路可以使二次型板形缺陷得到快速有效的控制,具有良好的控制性能。     

基于遗传算法优化的高速公路匝道PI控制器

研究遗传算法优化PI控制器的参数,并应用到高速公路匝道控制中。阐述了匝道控制目标,建立了高速公路交通流模型,给出了遗传算法优化的步骤,并对入口匝道PI控制器的参数进行了优化。仿真结果表明,该方法性能优越,用于高速公路入口匝道控制中效果良好。     

一种两轮轮式机器人点镇定智能控制实现

针对实际RoboCup机器人,分析了采用双闭环轮速跟随电机执行系统的两轮轮式移动机器人的数学模型,考虑了实际电机系统必然存在的速度饱和、加速度饱和限制对点镇定控制的影响,基于动觉图式的仿人智能控制理论,提出了一种两轮轮式机器人点镇定的分段比例智能控制器,利用遗传算法整定了控制器的参数.比较了比例控制器,比例余弦控制器和提出的智能控制器的仿真控制效果,并在实际RoboCup机器人上有效实现了提出的控制器.仿真和实际系统实验都证明了该控制器的有效性.     

基于改进双种群遗传算法的模糊控制器的优化

遗传算法是一种自适应、启发式、群体型、概率性、迭代式全局收敛算法,利用遗传算法的良好的搜索特性来优化模糊控制器,可以取得很好的控制效果.本文对传统的双种群遗传算法进行了归纳和分析,在此基础上提出了一种改进的双种群遗传算法(CGDPGA).将此改进算法用于优化模糊控制器的隶属度函数、量化因子和比例因子来实现模糊控制器的全...     

伪并行遗传算法对PID控制器参数的优化仿真

在对PID控制器参数优化中引入了伪并行遗传算法,在一定程度上克服了常规遗传算法容易产生的"早熟"现象,收敛速度也进一步提高.由对PID控制器参数优化的计算结果及仿真来看,该算法和常规遗传算法比较起来,超调量减小,调整时间也减小,说明该法是可行而有效的,对其它控制如PI控制等的参数优化也有借鉴作用.     

基于遗传算法的管道机器人分层模糊控制

为了适应空调管道的复杂环境,提高空调管道清扫机器人控制性能,降低控制复杂度,构建了一个分层的自适应模糊控制器.通过多输入系统进行分层输入和叠加输出,大量简化了模糊逻辑推理,减少了控制规则数.应用遗传算法对控制器参数进行优化,有效地融合了控制信息,达到了较理想的控制特性.实验和仿真结果表明,模糊控制器适应性强,优化后模糊控制器具有很好的控制效果,实现了机器人在管道中自主导航.     

基于遗传算法优化的机器人模糊控制系统

本文提出一种基于遗传算法优化的模糊控制系统并将之用于五自由度关节型机器人轨迹跟踪控制,该系统将五关节的位置误差和误差变化率作为模糊控制器的输入,输出为五关节的转矩,同时采用先进的遗传算法在线优化调整控制器参数,既避免建立复杂的机器人数学模型,又能达到精确的控制效果.仿真结果表明该控制系统用于机器人轨迹跟踪控制具有很好的性能,较好地实现了机器人的实时智能控制,并大幅提高了其控制的自适应性和鲁棒性,最后给出相关的实验和结论.     

智能采样变比例PI控制器

针对过程控制领域中的大时滞控制对象,提出了一种智能采样变比例PI(proportional-integral)控制器.通过智能采样环节来补偿被调过程的时滞特性,通过变比例PI控制器来加快调节过程.利用RSLogix5000及Emulate 5000进行程序编制和仿真.仿真结果表明,该智能采样变比例PI控制器超调量小,过渡时间短,同时抗干扰能力强.     

Multiobjective evolution based fuzzy PI controller design for nonlinear systems

This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators.     

机器人的鲁棒预测控制

本文提出基于误差预测的机器人鲁棒控制器。考虑到机器人的动力学建模误差影响其控制性能,本文建立机器人的误差模型,给出预测建模误差对运动轨迹偏差的作用的有效方法,并提出建模误差的鲁棒性补偿。本文分别在关节空间和直角空间针对冗余机器人和非冗余机器人提出鲁棒预测控制器设计,其有效性由仿真例子检验。     

Robust PI controller design for nonlinear systems via fuzzymodeling approach

The design problem of proportional and proportional-plus-integral (PI) controllers for nonlinear systems is studied. First, the Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties is used to approximate the nonlinear systems. Then a numerically tractable algorithm based on the technique of iterative linear matrix inequalities is developed to design a proportional (static output feedback) controller for the robust stabilization of the system in T-S fuzzy model. Next, we transform the problem of PI controller design to that of proportional controller design for an augmented system and thus bring the solution of the former problem into the configuration of the developed algorithm. Finally, the proposed method is applied to the design of robust stabilizing controllers for the excitation control of power systems. Simulation results show that the transient stability can be improved by using a fuzzy PI controller when large faults appear in the system, compared to the conventional PI controller designed by using linearization method around the steady state     

Integrated Environment for Modelling, Simulation and Control Design for Robotic Manipulators

In this paper an integrated environment for the design of robotic controllers implemented on a PC is described. It is based on the Planar Manipulators Toolbox for dynamic simulation of redundant planar manipulators. The tools are fully integrated in the MATLAB/SIMULINK and hence, a lot of standard tools are available for the analysis and control design. Using the real-time simulation it is possible to apply the developed controllers to a real robot manipulator, which can be included in the system via corresponding interfaces, without any additional coding. The main advantage is the flexibility in fast prototyping of different algorithms in the field of control of robotic systems, especially for redundant manipulators.     

A new nonlinear gain structure for PD‐type controllers in robotic applications

This article introduces a generic enhancement to certain proportional plus differential (PD)‐type, joint‐space controllers for robotic manipulators that can be utilized to reduce peak levels of servo output without increasing the duration of the transient response. This enhancement takes the form of nonlinear, partially hyperbolic multipliers that are applied to re‐scale the error signal gains. The rationale prompting the design of the aforementioned gain structure is discussed at length, and a mathematical formulation of the modified control law is presented for application to both position and full trajectory control. The stability of the suggested control strategy is addressed, and numerical simulations involving a three degree of freedom (3‐DOF) robot model are presented to demonstrate quantitatively the potential benefits of implementing such. ©1999 John Wiley & Sons, Inc.     

OPTIMIZATION OF DECENTRALIZED PI/PID CONTROLLERS BASED ON GENETIC ALGORITHM

In this paper, an optimization method of tuning decentralized PI/PID controllers based on genetic algorithms is presented. First, the existence of decentralized PI controllers with integrity is examined. Then, stable regions of each PI/PID controller parameters are calculated as the feasible area to be exploited, and the optimal PI/PID controllers are obtained by using a real‐coded genetic algorithm with elitist strategy, to meet the design specifications for the whole control system. The proposed method is applied to six examples from literature. Simulation results demonstrate that the proposed decentralized PI control is compatible to the referenced method while the decentralized PID control is better than the referenced method, and the proposed method is feasible for more complicated control systems optimizations.     

Polynomial family of PD-type controllers for robot manipulators

This paper addresses the problem of position control for robot manipulators. A new polynomial family of PD-type controllers with gravity compensation for the global position of robots manipulators is presented. The previous results on the linear PD controller are extended to the proposed polynomial family. The classical PD controller can be found among this large class of controllers when its proportional gain is a diagonal matrix. The main contribution of this paper is to prove that the closed-loop system composed by full nonlinear robot dynamics and the proposed family of controllers is globally asymptotically stable in agreement with Lyapunov's direct method and LaSalle's invariance principle. Besides the theoretical results, a real-time experimental comparison is also presented to illustrate the performance of the proposed family with other well-known control algorithms such as PD and PID schemes on a three degrees of freedom direct-drive arm.     

Dynamics and Noncollocated Model‐Free Position Control for a Space Robot with Multi‐Link Flexible Manipulators

In this paper, both the dynamics and noncollocated model‐free position control (NMPC) for a space robot with multi‐link flexible manipulators are developed. Using assumed modes approach to describe the flexible deformation, the dynamic model of the flexible space robotic system is derived with Lagrangian method to represent the system dynamic behaviors. Based on Lyapunov's direct method, the robust model‐free position control with noncollocated feedback is designed for position regulation of the space robot and vibration suppression of the flexible manipulators. The closed‐loop stability of the space robotic system can be guaranteed and the guideline of choosing noncollocated feedback is analyzed. The proposed control is easily implementable for flexible space robot with both uncertain complicated dynamic model and unknown system parameters, and all the control signals can be measured by sensors directly or obtained by a backward difference algorithm. Numerical simulations on a two‐link flexible space robot are provided to demonstrate the effectiveness of the proposed control.     

Dynamic state feedback control of two cooperative manipulators

Dynamic coordinated control of two robot manipulators that rigidly grasp a common object is studied. A dynamic coordinated control model for the two manipulators is derived that is suitable for system analysis and design in state space. The model takes into account kinematic and dynamic constraints between the two manipulators, and is explicitly described by non-linear state equtions and non-linear output equations in the state space. Since coordinated control requires the control of forces applied to the object by manipulators, the output equations include both position components and force components. While robotic systems with position outputs can be linearized using a static state feedback, systems with force outputs, such as the present two robot system, require a dynamic non-linear state feedback for exact linearization. By using dynamic non-linear feedback, coordinated control of two robotic manipulators is converted into a control problem of linear systems.     

Saturated proportional derivative control of flexible-joint manipulators

In this paper, the control of flexible-joint robotic manipulators while avoiding actuator saturation is investigated. Several proportional derivative controllers are developed, all of which disallow actuator saturation by guaranteeing that the applied torque is less than a specified maximum value. In particular, a Gibbs parameterization of the joint angles is included in the control laws, which allows for an increased control torque as compared to an Euler angle parameterization. An equilibrium point of the closed-loop system is proven to be asymptotically stable using the Lyapunov stability analysis. Moreover, the proposed control laws do not require any knowledge of the manipulator?s mass, stiffness, or dissipation properties, and as such, are robust to modelling errors. The proposed controllers are tested on a single-link flexible-joint manipulator experimentally and on a two-link flexible-joint manipulator in simulation, and are compared to the performance of controllers found in the literature.