Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters Kρ, Ti^* and Td^* can be obtained,which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kρ, Ti^* and Td^* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.     

The Application and Simulation of Fuzzy Adaptive PID in Household Heating Metering System

After heat is metered in each house unit,the heating system is regulated by variable flow.The temperature of the return w ater is controlled to regulate the flow to realize the temperature regulation.According to the characteristics of the temperature control w ith big inertia,pure time-delay and degeneration,a fuzzy adaptive PID controller is designed w ith the advantages of the fuzzy control and PID algorithm,and the simulation model is established according to the characteristics of heating metering system.Simulation results show that the fuzzy adaptive PID controller proposed has small overshoot,short oscillation cycle,high precision and strong anti-jamming capability in comparison w ith conventional PID controller,w hich could meet the requirement of the dynamic and steady-state performance of the heating process.     

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal FID controller with incomplete derivation is proposed based on fuzzy inference and the genetic algorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part and the on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step response as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters Kp* , Ti* , and Td* are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , 77 , and Td* and according to the current system error e and its time derivative, a dedicated program is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mechanism to ensure that the system response has optimal dynamic and steady-state performance. The controller has been used to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. Th     

LSSVM Predictive Control for Calcination Zone Temperature in Rotary Kiln with IHS Algorithm

The calcination zone temperature control is an important problem in rotary kiln production process. In order to solve this problem,a predictive control method based on improved harmony search algorithm( IHS)and least square support vector machine( LSSVM) is proposed. LSSVM is utilized to bulid the nonlinear predictive model of calcination zone temperature in rotary kiln. The calcination zone temperature can be predicted through input control variable,the error and error correction of output feedback. The performance index function is established by deviation and control variable. An IHS algorithm with better fitness and faster convergence speed is proposed. The optimal control variable can be obtained by rolling optimization through this IHS algorithm. The stability of this predictive control method is proved to be feasible. The simulation and actual experiment results show that the proposed predictive control method has good control performance.     

Kinematics/Fuzzy Logic Combined Controller for Formation Control of Mobile Robots

A kinematics and fuzzy logic combined formation controller was proposed for leader-follower based formation control using backstepping method in order to accommodate the dynamics of the robot. The kinematics controller generates desired linear and angular velocities for follower robots, which make the configuration of follower robots coverage to the desired. The fuzzy logic controller takes dynamics of the leader and followers into consideration, which is built upon Mamdani fuzzy model. The force and torque acting on robots are described as linguistic variables and also 25 if-then rules are designed. In addition, the fuzzy logic controller adopts the Centroid of Area method as defuzzification strategy and makes robots＇ actual velocities converge to the expected which is generated by the kinematics controller. The innovation of the kinematics and fuzzy logic combined formation controller presented in the paper is that the perfect velocity tracking assumption is removed and real- time performance of the system is improved. Compared with traditional torque-computed controller, the velocity error convergence time in case of the proposed method is shorter than traditional torque-computed controller. The simulation results validate that the proposed controller can drive robot members to form the desired formation and formation tracking errors which can coverage to a neighborhood of the origin. Additionally, the simulations also show that the proposed method has better velocity convergence performance than traditional torque-computed method.     

H∞ Theory and its Application to the Position of Rolling Mill Control System

The controller designed according to classical or modern control theory will not satisfy the performance requirements when the controlled object in industrial field can not be described by exact mathematical model or the disturbance of the controlled system. In order to make the controlled system stable and having good performance, H∞ control theory was put forward to solve this practical problem. Taking the position of a rolling mill as the controlled object, it was rectified by optimal engineering way. Then, three different plans were put forward according to Bang-Bang control, LQ control and H∞ control, respectively. The result of the simulation shows that the controller designed according to H∞ method whose robust performance and ability to restrain colors disturbance is satisfactory.     

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     

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 complex control system based on the fuzzy PID control and state predictor feedback control

A multi-mode adaptive controller was proposed. The controller features in the combination of Bang-bang and Fuzzy PID controls with state predictor. When large error exists, the controller operates in Bang-bang mode, otherwise it works as a fuzzy PID controller. For only few parameters to be adjusted, the real time controlled system achieveed good stability and fast response. Furthermore, the introduction of state observer was also discussed to extend the capability of the proposed controller to the plant with time-delay factors. The classical PID controller and the multi-mode controller were applied to the same second-order system successively. By comparison of the simulation results, the effectiveness of the controller were shown. At last, on electric-wire production line, this approach was practiced to control electric-wire diameter with an additive random disturbance signal. The test result further proved the effectiveness of the multi-mode controller.     

Control of convergence in a computational fluid dynamic simulation using fuzzy logic

A fuzzy control method was used to accelerate iteration convergence in numerical fluid dynamic simulation using SIMPLER algorithm. The residual ratio of momentum or energy equation between two successive iterations was used as the input variable. A fuzzy logic algorithm was developed in order to obtain the relative increment of the under-relaxation factor and its new value was then used for the next iteration. The algorithm was tested by four benchmark problems. In all cases considered, when the fuzzy control logic was used, convergence was achieved with nearly the minimum number of iterations, showing the feasibility of the proposed method.     

一种智能双模数控伺服进给控制器设计

以数控伺服进给控制器为研究对象,采用在全论域范围内带有自调整模糊规则因子和模糊比例因子的自适应控制策略,提出一种可提高数控伺服进给系统动态性能的智能Fuzzy-PID双模控制器设计方法．仿真分析证明该方法在不同的工作状态下,根据不同的响应阶段的动态性能要求在线自动调整控制器的控制参数和控制算法,可有效地克服传统控制算法存在的扰动、超调量大、调节时间长等缺点．Matlab软件仿真证明该控制器较常规PID、模糊控制器具有响应快、超调小、鲁棒性强和自寻优等特点．     

直驱式永磁同步风电系统变桨距控制算法研究

以直驱式永磁同步风电系统为研究对象,在各部分数学模型的基础上,建立了整个风电系统的模型。基于此模型,考虑到风电机组非线性强、转动惯量大导致变桨距控制困难的问题,提出了基于模糊自适应PID和模糊前馈结合的变桨距控制算法并对该算法进行了仿真。仿真结果表明,当风速高于额定风速时,该控制算法能有效地控制风电机组的输出。最后在相同风速条件下比较了该控制算法与模糊控制算法、传统的PID控制算法的控制效果。比较结果表明,当风速高于额定风速时,虽然3种控制算法均能控制风电机组的输出,但是与模糊控制算法和传统的PID控制算法相比,本文提出的算法具有更好的稳定性和动态特性。     

模型不确定时滞欠驱动AUV的模糊变结构控制

为了实现具有时变水动力系数、未建模动态和时滞等不确定性的欠驱动自主水下航行器的鲁棒控制,以变结构控制的切换函数及其变化率为模糊控制器的输入,以变结构控制律的变化率为模糊控制器的输出设计了一个模糊变结构控制器;为了得到更好的性能,引入了压缩扩张隶属度函数,设计一定的模糊规则自适应地调整模糊控制器中的比例因子和量化因子;将该控制器和模糊变结构控制器,准滑模控制器进行了仿真对比,证明本文设计的控制器有更好的性能;最后基于该控制器对具有水动力系数不确定性,未建模动态和控制输入时滞的AUV运动模型进行了数值仿真,结果表明该控制器有很强的鲁棒性,而且控制成本很小,只在某些情况有极弱的抖振.     

基于MATLAB的液位模糊控制系统设计

对水箱系统设计一个两维模糊控制器,模糊控制器设计为两个输入一个输出,模糊控制器的输出用来控制阀门的开度,调节水箱的液位。运用MATLAB模糊工具箱实现双输入单输出的模糊控制器,并结合Simulink仿真得到实际液位跟踪给定液位的曲线,仿真结果证实该模糊控制器优越于常规的PID控制器,水箱模糊控制系统获得良好的控制性能指标。     

基于模拟退火遗传算法的PID参数整定与优化

结合模拟退火算法和遗传算法的思想,提出模拟退火遗传算法,用此算法进行PID参数整定与优化．同时使用自适应交叉率、变异率以及适应度拉伸方法对传统遗传算法进行改进．模拟退火遗传算法有效抑制早熟,且具有收敛性快、全局寻优与局部寻优能力．仿真结果表明,基于此算法寻优设计的PID控制器动态品质和稳定性更好、鲁棒性更强．     

混沌粒子群优化的模糊神经PID控制器设计

针对常规PID控制的线性局限性及传统模糊控制和模糊PID控制中积分误差规则难以获取,系统存在稳态误差的问题,提出一类以模糊神经网络和PID神经网络组成的模糊神经PID控制器．以整个神经网络的权值为优化参数,利用基于混沌策略的粒子群全局优化算法离线优化和误差反传算法在线调整相结合的方法获得控制器参数,并设计了混沌优化与粒子群结合的两步方案．仿真结果表明:与传统PID、模糊、模糊PID控制相比,系统的瞬态和稳态性能有了明显提高,且保持了一定的鲁棒性及高跟踪精度．该方法有效地拓展了PID控制的使用范围,并为智能方法与PID控制的结合提供了一种新的参考方案．     

进给伺服系统模糊自整定控制建模与仿真

分析了模糊自整定PID控制器的特点,建立了二输入三输出的单变量二维速度环自整定模糊控制器,并将建立好的模糊PID控制器与数控机床进给伺服系统结合起来,构成了数控机床进给伺服系统模糊PID控制系统仿真模型,仿真结果表明,模糊自整定控制系统具有比普通PID控制系统更好的控制性能。     

时滞系统的T-S模糊PID控制

以输出误差e、误差积累Σe、误差变化率△e为控制器输入,设计了三输入单输出的T-S模糊PID控制器。针对时滞系统,分别采用传统PID控制器、Smith预估补偿器、T-S模糊PID控制器进行控制并仿真比较,结果表明：T-S模糊PID控制器具有控制精度高、响应快速、适应性强的特点;当时滞系统的时滞发生较大变化时,控制器仍具有很好的控制效果,系统稳定。     

钢管捆自动成形系统电液比例控制研究

为了满足钢管生产过程中对钢管捆成形和包装的需要，对钢管捆自动成形电液比例系统进行了模糊比例-积分-微分(PID)控制研究.基于该系统的组成和工作原理，分析了双缸管排水平定位和竖直定位堆放控制问题，给出了管排竖直定位堆放误差的补偿算法.采用专家智能控制与模糊PID控制技术相结合，得到了具有二级结构的模糊PID控制策略，并对实际系统设计了相应的模糊PID控制器.系统控制结果表明，模糊PID控制器能修正各控制参数，提高了系统控制的自适应性和鲁棒性.与常规的PID控制器相比较，该控制器具有更好的系统运行性能.