New techniques for genetic development of a class of fuzzycontrollers

Presents three novel techniques for enhancing the power of a genetic algorithm (GA) used to design fuzzy systems: a new context-dependent coding (CDC) technique, a simple chromosome reordering operator to maximize efficiency, and the coevolution of controller set tests to force competence in all areas of state space. These measures are shown to lead to a considerable improvement over conventional GAs when used to design controllers for a standard problem, such as the cart-pole problem. We use an analysis of GAs by L. Altenberg (1994) to determine a performance measure that demonstrates that our coding scheme and reordering operator improve the ability of the GA to organize itself and evolve chromosomal structures that not only produce high scores, but improve the search efficiency of the genetic operators. We investigate the algorithm in a controller to provide parallel parking maneuvers for mobile robots. It is shown that the controllers developed are robust to the systematic errors that inevitably arise when controllers are transferred from a simulated environment to the real world     

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.     

基于FPGA的模糊PID控制器设计

针对实现传统模糊PID控制器时,需要建立比例、积分和微分三个模糊控制器,存在模糊规则较繁杂、运算量大、速度慢等问题,提出了以PD模糊控制器代替PI模糊控制器,采用两个PD模糊控制器,并引入FPGA技术,实现模糊PID控制器.通过Quartus Ⅱ和Matlab联合仿真,比较了基于FBC和SBC实现的模糊PID控制器的控制效果,验证了设计方案的正确性和可行性.     

A method for improving the robustness of PID control

In this paper, an effective method is proposed for robust proportional-integral-derivative (PID) control that is easily implementable on commonly used equipment such as programmable logic controller (PLC) and programmable automation controller (PAC). The method is based on a two-loop model following control (MFC) system containing a nominal model of the controlled plant and two PID controllers. Basic features exhibited by the MFC structure are presented, and a technique to tune both component controllers is given. The proposed structures have been implemented in a programmable logic controller and tested on control plants with perturbed parameters. Also, the proposed control system has been checked for its performance in cases when the operation of PID controllers is based on fuzzy logic. Tuning rules for the fuzzy controllers in the presented MFC system have been proposed. Results of tests lend support to the view that the proposed control structures may find wide application to robust control of plants with time-varying parameters.     

Design of incremental fuzzy PI controllers for a gas-turbine plant

In this paper, incremental fuzzy proportional integral (PI) speed and temperature controllers for a heavy-duty gas-turbine plant are presented. To improve performance, an analysis of incremental fuzzy PI control is provided, and new fuzzy control rules are proposed. In applying the fuzzy PI control to a gas-turbine plant, all gains are optimized by an adaptive genetic algorithm. We show the performance improvement of the proposed controller compared with conventional PI controller via simulations.     

Hybrid fuzzy proportional-integral plus conventional derivativecontrol of linear and nonlinear systems

This paper presents a new approach toward the optimal design of a hybrid proportional-integral-derivative (PID) controller applicable for controlling linear as well as nonlinear systems using genetic algorithms (GAs). The proposed hybrid PID controller is derived by replacing the conventional PI controller by a two-input normalized linear fuzzy logic controller (FLC) and executing the conventional D controller in an incremental form. The salient features of the proposed controller are as follows: (1) the linearly defined FLC can generate nonlinear output so that high nonlinearities of complex systems can be handled; (2) only one well-defined linear fuzzy control space is required for both linear and nonlinear systems; (3) optimal tuning of the controller gains is carried out by using a GA; and (4) it is simple and easy to implement. Simulation results on a temperature control system (linear system) and a missile model (nonlinear system) demonstrate the effectiveness and robustness of the proposed controller     

Lyapunov-function-based design of fuzzy logic controllers and itsapplication on combining controllers

This paper presents the design of fuzzy logic controllers (FLCs) for nonlinear systems with guaranteed closed-loop stability and its application on combining controllers. The design is based on heuristic fuzzy rules. Although each rule in the FLC refers to a stable closed-loop subsystem, the overall system stability cannot be guaranteed when all these rules are applied together. In this paper, it is proved that if each subsystem is stable in the sense of Lyapunov (ISL) under a common Lyapunov function, the overall system is also stable ISL. Since no fuzzy plant model is involved, the number of subsystems generated is relatively small, and the common Lyapunov function can be found more easily. To probe further, an application of this design approach to an inverted pendulum system that combines a sliding-mode controller (SMC) and a state feedback controller (SFC) is reported. Each rule in this FLC has an SMC or an SFC in the consequent part. The role of the FLC is to schedule the final control under different antecedents. The stability of the whole system is guaranteed by the proposed design approach. More importantly, the controller thus designed can keep the advantages and remove the disadvantages of the two conventional controllers     

Hybridized ABC-GA optimized fractional order fuzzy pre-compensated FOPID control design for 2-DOF robot manipulator

The robotic manipulator is an extremely nonlinear, multi-input multi-output (MIMO), highly coupled, and complex system wherein the parameter uncertainties and external disturbances adversely affect the performance of this system. From this, it necessitates that the controllers designed for such system must overcome these complexities. In this paper, we develop a novel fractional order fuzzy pre-compensated fractional order PID (FOFP-FOPID) controller for 2-degree of freedom (2-DOF) manipulator dealing with trajectory tracking problem. In order to optimize the controller’s parameters while minimizing integral of time absolute error (ITAE), a metaheuristic optimization technique, viz., artificial bee colony-genetic algorithm (ABC-GA) is presented. The efficacy of our proposed controller is demonstrated by comparing it with some existing controllers, such as integer order fuzzy pre-compensated PID (IOFP-PID), fuzzy PID (FPID), and conventional PID controllers. Furthermore, the robustness analysis for proposed controllers is also investigated for parameter variations and external disturbances. The simulation results indicate that FOFP-FOPID controller can not only guarantee the best trajectory tracking but also ameliorate the system robustness for parameter variations as well as external disturbances.     

Fuzzy two-degrees-of-freedom speed controller for motor drives

A fuzzy two-degrees-of-freedom (2-DOF) controller and its application to the speed control of an induction motor drive are presented in this paper. The proposed controller is composed of two fuzzy controllers to obtain good tracking and regulating responses. Unlike the conventional fuzzy controller, the error between the outputs of a reference model and the controlled drive is used to drive the proposed fuzzy controller. The drive rotor speed response can closely follow the trajectory produced by the reference model, and good load speed regulating response can also be obtained simultaneously owing to the possession of two-degrees-of-freedom in structure. Moreover, these performances are rather insensitive to the operating condition changes. The dynamic signal analysis as well as the construction of fuzzy control algorithms are described in detail. Some simulated and measured results are provided to demonstrate the effectiveness of the proposed fuzzy controller     

基于Matlab的模糊PID控制系统设计及仿真

模糊PID控制是利用PID参数整定经验来使模糊控制器自动整定其参数,从而使PID控制器以变应变。文中采用Matlab软件设计模糊PID控制器,并应用于控制锅炉液位。通过实验仿真比较研究PID控制、模糊控制及模糊PID控制的控制效果。实验结果显示,模糊PID控制效果理想,具有较好的应用前景。     

Experimental design of a fuzzy controller for improving power factor of boost rectifier

This article presents the design and the implementation of dSPACE DS1104 controller board-based PI and fuzzy logic peak current-mode controllers in the voltage loop and two controllers in the current loop based first on a standard fixed hysteresis band control, followed by a variable hysteresis band control to achieve constant switching frequency for a single-phase active power factor corrector in the continuous conduction mode. All these controllers have been verified via simulation in Simulink and a real-time implementation is performed on an experimental test bench utilising a rapid prototyping tool. The controllers are experimentally compared for steady-state performance and transient response. It is shown that the PI and fuzzy logic controllers give a superior steady-state performance, whereas the fuzzy logic inference based controller can achieve better dynamic response than its PI counterpart under large load disturbance and plant uncertainties. Furthermore, the variable hysteresis band control in the current loop gives a low total harmonic distortion of the input current compared to a standard fixed hysteresis band control.     

A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional–integral–derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.     

An online learning fuzzy controller

A fuzzy controller with online learning capability is reported in this paper. The controller learns from a standard proportional plus derivative (PD) controller. It is implicitly assumed that the tuning parameters of the PD controller are already known. The learning is realized via Wang's table lookup scheme. The controllers are applied successfully to control an open-loop unstable system, i.e., the ball and plate system. Experimental studies have demonstrated the performance of the proposed controller.     

模糊PID控制系统的设计与研究

为了解决工程中二阶系统的控制问题,在此对PID控制与模糊控制的原理进行了研究,并将二者的优势相互结合,设计了一种具有参数自整定功能的模糊PID控制系统。对PID参数初值的确定,隶属度函数的选取,模糊控制规则表的设计做了较为深入的研究。并利用Matlab/Simulink软件对控制系统进行了仿真研究。对阶跃输入下PID控制系统与该文设计的模糊PID控制系统的响应情况做出了定量的比较。结果表明对于二阶延迟系统,模糊PID控制器的超调量与调节时间均小于传统的PID控制,能显著提高控制效果。     

Fuzzy logic control of a space-vector PWM current regulator forthree-phase power converters

This paper presents a fuzzy logic implementation of space-vector pulse-width modulation (PWM) for three-phase power converters. The conventional space-vector PWM current regulator implementation is generally computationally complex. The fuzzy logic controller implementation relieves the processor of a number of computations, thereby accommodating a less expensive microprocessor. The AC-side rectifier voltages are used as fuzzy-state variables. The fuzzy logic control has two outputs: magnitude and angle of reference voltage. Both conventional space-vector PWM and the fuzzy logic controller are implemented to evaluate performance using a 16-b microcontroller (68HC16). Experimental results are provided for both controllers at the same operating point, where the power drawn by the load is about 3 kW. The fuzzy logic controller reduces the computational burden on the processor by about 30%     

Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

“智能控制”课程中的模糊PID控制类型

模糊控制是"智能控制"课程的重要内容,模糊PID控制是模糊控制与传统控制的交叉内容。在"智能控制"课程中对这一控制类型的介绍是十分必要的。本文总结了目前已有的三大类Mamdani模糊PID控制类型,并简要介绍了每种类型的表现形式。这为学生更直观、更容易地理解这部分内容提供帮助。     

A New Neuro-Fuzzy Adaptive Genetic Algorithm

Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization.