An optimal fuzzy PID controller

This paper introduces an optimal fuzzy proportional-integral-derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting controller does not need to execute any fuzzy rule base, and is actually a conventional PID controller with analytical formulae. The main improvement is in endowing the classical controller with a certain adaptive control capability. The constant PID control gains are optimized by using the multiobjective genetic algorithm (MOGA), thereby yielding an optimal fuzzy PID controller. Computer simulations are shown to demonstrate its improvement over the fuzzy PID controller without MOGA optimization     

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.     

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     

Hybrid control of a three-pole active magnetic bearing

The design and implementation of the hybrid control method for a three-pole active magnetic bearing (AMB) is proposed in this paper. The system is inherently nonlinear and conventional nonlinear controllers are a little complicated while the proposed hybrid controller has a piecewise linear form, i.e., linear in each sub-region. A state-feedback hybrid controller is designed in this study and the unmeasurable states are estimated by an observer. The gains of the hybrid controller are obtained by the LQR method in each sub-region. To evaluate the performance, the designed controller is implemented on an experimental setup. The experimental results show that the proposed method can efficiently stabilize the three-pole AMB system. The simplicity of design, domain of attraction, uncomplicated control law and computational time are advantages of this method over other nonlinear control strategies in AMB systems.     

Fuzzy-PI-based centralised control of semi-isolated FP-SEPIC/ZETA BDC in a PV/battery hybrid system

Multiport converters with centralised controller have been most commonly used in stand-alone photovoltaic (PV)/battery hybrid system to supply the load smoothly without any disturbances. This study presents the performance analysis of four-port SEPIC/ZETA bidirectional converter (FP-SEPIC/ZETA BDC) using various types of centralised control schemes like Fuzzy tuned proportional integral controller (Fuzzy-PI), fuzzy logic controller (FLC) and conventional proportional integral (PI) controller. The proposed FP-SEPIC/ZETA BDC with various control strategy is derived for simultaneous power management of a PV source using distributed maximum power point tracking (DMPPT) algorithm, a rechargeable battery, and a load by means of centralised controller. The steady state and the dynamic response of the FP-SEPIC/ZETA BDC are analysed using three different types of controllers under line and load regulation. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller. The power balance between the ports is achieved by pseudorandom carrier modulation scheme. The response of the FP-SEPIC/ZETA BDC is also validated experimentally using hardware prototype model of 500 W system. The effectiveness of the control strategy is validated using simulation and experimental results.     

Kinematics control of a pneumatic system by hybrid fuzzy PID

In a pneumatic system, normally, the piston can stop at only two terminal endpoints. In order to extend the capabilities of the system, this research is conducted to develop a kinematics control-based pneumatic system. Both position and velocity of the pneumatic piston are controlled in such a way that the controlled piston is able to move with the specified velocity to the target position. A hybrid of fuzzy and proportional-plus-integral-plus-derivative (PID) control algorithm is proposed in this paper as the solution. The control algorithm is separated into two parts: fuzzy control and PID control. The fuzzy controller is used to control the piston when the piston locates far away from the target position whereas the PID controller is applied when the piston is near the desired position. The development starts with designing of a position sensor to detect position information of the piston. The sensor-manipulating circuit consisting of potentiometer, inverting amplifier, summing amplifier, low-pass filter and analog-to-digital converter is then designed and realized. Next, the proposed hybrid of fuzzy and PID control is implemented and programmed on the microprocessor. In order to test performance of the system, settling time and steady-state error of five control algorithms – proportional (P) control, proportional-plus-integral (PI) control, proportional-plus-derivative (PD) control, PID control, and hybrid of fuzzy and PID control – are investigated. The results from the experiments show that the proposed hybrid of fuzzy and PID control gives the most satisfied settling time and steady-state error.     

基于模糊自适应PID控制的速度调节器设计与仿真

针对异步电动机非线性的特点,考虑到传统PID控制难以对其进行有效的控制的问题。文中设计了一种模糊自适应PID控制器,并将这种控制器应用到系统的转速调节的环节中。利用Matlab的Simulink工具搭建了模糊自适应PID控制的异步电机的仿真模型。仿真实验结果证明了设计的优越性,模糊自适应PID控制下的系统超调变小,反应速度变快,与原先的PID控制方式相比提高了系统的稳定性、动态响应性能以及鲁棒性能。     

Hybrid fuzzy-logic and neural-network controller for MIMO systems

This study developed a hybrid fuzzy-logic and neural-network controller (HFNC) for multiple-input multiple-output (MIMO) systems. The HFNC consists of a fuzzy logic controller (FLC) which was designed to control each degree of freedom (DOF) of a MIMO system individually and an additional coupling neural network which was incorporated into the FLC to compensate for the dynamic coupling effects between each DOF of the MIMO system. Stability and robustness of the HFNC have been demonstrated using a state-space approach. From the simulation results of the 2-link robotic manipulator application and the experimental results of the 6-DOF robot tests, the HFNC demonstrated more superior control performance than the FLC.     

Using fuzzy controller for improvement of power decoupling performance in PV systems

ABSTRACT

In photovoltaic systems in order to ensure the stable operation of the module, ripple value of the input voltage should be mitigated to a small value; for this purpose, the power decoupling circuit can be used. In this work, considering the four-switch-based power decoupling circuit developed, the conventional Proportional-Integral (PI) controller has been replaced with well-known fuzzy controller, which enjoys a wide range of control gain variation, both linear and nonlinear rules' capability, robustness and more flexibility. The operating modes of the circuit are addressed and accordingly the space state model is derived. Then, the proposed controller has been replaced and the performance of the system will be analysed. The power decoupling circuit along with the proposed controller is implemented by MATLAB/Simulink software. The simulation results show the ability of the proposed fuzzy controller to improve the performance of the system in comparison to the conventional PI controller. A deeper comparison of two controllers confirms that the system would experience more accuracy, faster response and lower total harmonic distortion (THD) in presence of the proposed fuzzy controller.     

A Novel FPSM Controller for DC-DC Switching Converters

This paper presents a novel fuzzy pulse skip modulation （FPSM） controller for switching direct current to direct current （DC-DC） converters based on fuzzy ratiocination modeling approach. Owing to the optimal consideration during the design and the nonlinear characteristics of the controller, improved dynamic responses of the FPSM controller can be achieved over conventional controllers. Compared with conventional proportion integral derivative （PID） control, FPSM control has 60% lower overshoot and 10% lower setting time under the same input voltage and output load change. The presented approach is general and can be applied to other types of DC-DC converters.     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

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.     

基于模糊自整定PID的汽轮机数字电液控制系统

基于模糊控制技术和汽轮机DEH系统的数学模型,将模糊自整定理论应用于汽轮机DEH系统,设计了一种适用于汽轮机数字电液控制系统的模糊自整定PID控制器。他利用偏差及偏差变化率作为模糊控制器的输入,并制定了模糊规则进行控制,实现PID参数的实时自整定,从而改善汽轮机调节系统的控制特性。仿真结果和分析表明在调节稳定性,超调量和自整定PID参数等方面,模糊自整定PID控制算法均优于传统PID控制算法。     

基于模糊控制纸张抗张试验机调速系统设计

纸张抗张试验机是检测纸张抗张强度和拉伸率的材料力学设备。为了能使试验机具有更加精确的调速系统,在控制策略上将模糊控制技术与PID有效结合,设计出基于模糊PID控制器的双闭环直流调速系统。通过Matlab仿真实验与常规PID控制器进行比较,模糊PID控制器的仿真结果要明显优越于常规PID控制,其响应速度、鲁棒性、稳态精度等均达到预期效果。     

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.     

T-S fuzzy controllers for nonlinear interconnected systems with multiple time delays

This paper investigates the effectiveness of a passive tuned mass damper (TMD) and fuzzy controller in reducing the structural responses subject to the external force. In general, TMD is good for linear systems. We proposed here an approach of Takagi-Sugeno (T-S) fuzzy controller to deal with the nonlinear system. To overcome the effect of modeling error between nonlinear multiple time-delay systems and T-S fuzzy models, a robustness design of fuzzy control via model-based approach is proposed in this paper. A stability criterion in terms of Lyapunov's direct method is derived to guarantee the stability of nonlinear multiple time-delay interconnected systems. Based on the decentralized control scheme and this criterion, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear multiple time-delay interconnected system and the H/sup /spl infin// control performance is achieved at the same time. Finally, the proposed methodology is illustrated by an example of a nonlinear TMD system.     

基于模糊PID控制的水轮机调节系统应用与仿真研究

针对水轮机调节系统的非线性、时变性及大惯性等特点,建立了其较为精确的数学模型,研究了模糊控制的基本原理,在此基础上构建了水轮机调节系统的模糊PID控制模型,并设计了适合水轮发电机组的模糊控制器,最后利用Matlab软件做了深入细致的仿真研究。研究表明,与常规的PID控制算法相比,引入模糊PID控制的水轮发电机组的调节特性得到了明显改善,并具有良好的动态品质。     

圆柱-球体三自由度超声电机模糊自适应控制

针对圆柱-球体三自由度超声电机难以建立精确数学模型的特点,设计了模糊自适应定位控制系统。该控制系统由模糊控制器和在线参数自调整环节构成,在线参数自调整环节使该系统的动态特性、稳态性能更好地兼顾,克服了固定量化因子控制性能不理想的缺点。应用该控制器,实现了电机精确定位控制。结果表明,参数自调整模糊自适应控制器的性能优于传统PID控制器及固定量化因子的模糊控制器,得到了满意的控制效果。     

A hybrid fuzzy logic and neural network algorithm for robot motioncontrol

Robotic manipulators are multivariable nonlinear coupling dynamic systems. Industrial robots were controlled by using a traditional controller, the control performance of which may change with respect to operating conditions. Since the robotic manipulators have complicated nonlinear mathematical models, control systems based on the system model are difficult to design. In this paper, a model-free hybrid fuzzy logic and neural network algorithm was proposed to control this multi-input/multi-output (MIMO) robotic system. First, a fuzzy logic controller was designed to control individual joints of this 4-degree-of-freedom (DOF) robot. Secondly, a coupling neural network controller was introduced to take care of the coupling effect among joints and refine the control performance of this robotic system. The experimental results showed that the application of this control strategy effectively improved the trajectory tracking precision