PSO-scaled fuzzy logic to load frequency control in hydrothermal power system

This paper, presents the particle swarm optimization-based fuzzy logic controller (PSO FLC) design for load frequency control in a two-area interconnected hydrothermal power system. Flexible alternating current transmission system devices and energy storage devices are being installed to improve the reliability and stability of the system under dynamic conditions. One such devices namely thyristor-controlled phase shifter (TCPS) is connected in series with the tie-line to damp out the power swings and frequency oscillations. Similarly at the terminal of one control area, a fast acting energy storage device of superconducting magnetic energy storage (SMES) is connected to meet the sudden changes in demand. The existing conventional controllers are unable to provide the satisfactory performance over a wide range of operating conditions due to system nonlinearity and plant parameter variations. To improve the dynamic performance of the system, this work proposes an intelligent tuning approach using a combination of particle swarm optimization (PSO) and fuzzy logic technique. In this work, PSO algorithm is employed for the optimal selection of membership function parameters of the proposed fuzzy PI, TCPS and SMES controllers by minimizing the time domain objective function. The simulation study is performed by the proposed PSO FLC in a two-area interconnected power system. To show the effective performance of the proposed controller, a comparative study has been made with the conventional, genetic algorithm and fuzzy logic-based optimized controller under varying load conditions.     

A comparative experimental study of type-1/type-2 fuzzy cascade controller based on genetic algorithms and particle swarm optimization

In this study, we introduce the design methodology of an optimized fuzzy controller with the aid of particle swarm optimization (PSO) for ball and beam system.The ball and beam system is a well-known control engineering experimental setup which consists of servo motor, beam and ball. This system exhibits a number of interesting and challenging properties when being considered from the control perspective. The ball and beam system determines the position of ball through the control of a servo motor. The displacement change of the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor.The fixed membership function design of type-1 based fuzzy logic controller (FLC) leads to the difficulty of rule-based control design when representing linguistic nature of knowledge. In type-2 FLC as the expanded type of type-1 FL, we can effectively improve the control characteristic by using the footprint of uncertainty (FOU) of the membership functions. Type-2 FLC exhibits some robustness when compared with type-1 FLC.Through computer simulation as well as real-world experiment, we apply optimized type-2 fuzzy cascade controllers based on PSO to ball and beam system. To evaluate performance of each controller, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time, and a steady-state error. In the sequel, the optimized fuzzy cascade controller is realized and also experimented with through running two detailed comparative studies including type-1/type-2 fuzzy controller and genetic algorithms/particle swarm optimization.     

Type-2 fuzzy control for a flexible-joint robot using voltage control strategy

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.     

基于混合滑模控制器和反正切观测器的SPMSM直接转矩控制

针对基于传统PI控制的表贴式永磁同步电机(SPMSM)直接转矩控制系统抖振和相位延迟等问题,在转速环节设计新型趋近律,采用模糊自适应方法,实现趋近律参数的动态调节,并通过Lyapunov方法证明稳定性.利用super-twisting滑模策略生成参考电压矢量,完成混合滑模控制器的设计,建立基于反正切函数的滑模观测器,并对转子位置进行合理补偿.仿真实验表明,与PI控制、基于指数趋近律的滑模控制器相比,所设计的控制器在电机空载起动和外加干扰情况下均能有效提高系统响应,显著降低抖振,与其他模型参考自适应观测器相比,所设计观测器能有效减小相位延迟,转子位置辨识结果更准确.     

Fault tolerant control based on neural network interval type-2 fuzzy sliding mode controller for octorotor UAV

In this paper, a robust controller for a six degrees of freedom (6 DOF) octorotor helicopter control is proposed in presence of actuator and sensor faults. Neural networks (NN), interval type-2 fuzzy logic control (IT2FLC) approach and sliding mode control (SMC) technique are used to design a controller, named fault tolerant neural network interval type-2 fuzzy sliding mode controller (FTNNIT2FSMC), for each subsystem of the octorotor helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the number of rules for the fuzzy controller, and guaranteeing the stability and the robustness of the system. The simulation results show that the FTNNIT2FSMC can greatly alleviate the chattering effect, tracking well in presence of actuator and sensor faults.     

PID-type fuzzy logic controller tuning based on particle swarm optimization

In this paper, a new PID-type fuzzy logic controller (FLC) tuning strategy is proposed using a particle swarm optimization (PSO) approach. In order to improve further the performance and robustness properties of the proposed PID-fuzzy approach, two self-tuning mechanisms are introduced. The scaling factors tuning problem of these PID-type FLC structures is formulated and systematically resolved, using a proposed constrained PSO algorithm. The case of an electrical DC drive benchmark is investigated, within a developed real-time framework, to illustrate the efficiency and superiority of the proposed PSO-based fuzzy control approaches. Simulation and experimental results show the advantages of the designed PSO-tuned PID-type FLC structures in terms of efficiency and robustness.     

Study on the sliding mode fault tolerant predictive control based on multi agent particle swarm optimization

For a class of uncertain discrete-time systems with time varying delay, the problem of robust fault-tolerant control for such systems is studied by combining the design of sliding mode control (SMC) and model predictive control (MPC). A sliding mode fault tolerant predictive control based on multi agent particle swarm optimization (PSO) is presented, and the design, analysis and proof of the scheme are given in detail. Firstly, the sliding mode prediction model of the system is designed by assigning poles of the output error of the system. The model has time varying characteristics, and it can improve the motion quality of the system while ensuring the sliding mode is stable. Secondly, a new discrete reference trajectory considering time-delay systems subjected simultaneously to parameter perturbations and disturbances is proposed, which not only can ensure that the state of the system has good robustness and fast convergence in the process of approaching sliding mode surface, but also can inhibit chattering phenomenon. Thirdly, the multi agent PSO improves the receding-horizon optimization, which can quickly and accurately solve the control laws satisfying the input constraints, and can effectively avoid falling into local extrema problem of the traditional PSO. Finally, the theoretical proof of robust stability of the proposed control scheme is given. Experimental results of quad-rotor helicopter semi physical simulation platform show that the state of uncertain discrete-time systems with time varying delay is stable under the action of the proposed control scheme in this paper. The advantages of fast response, less overshoot and small control chattering prove the feasibility and effectiveness of the proposed control scheme.     

Fuzzy sliding mode control for a robot manipulator

This work presents the design of a robust control system using a sliding mode controller that incorporates a fuzzy control scheme. The presented control law superposes a sliding mode controller and a fuzzy logic controller. A fuzzy tuning scheme is employed to improve the performance of the control system. The proposed fuzzy sliding mode control (FSMC) scheme utilizes the complementary cooperation of the traditional sliding mode control (SMC) and the fuzzy logic control (FLC). In other words, the proposed control scheme has the advantages which it can guarantee the stability in the sense of Lyapunov function theory and can ameliorate the tracking errors, compared with the FLC and SMC, respectively. Simulation results for the trajectory tracking control of a two-link robot manipulator are presented to show the feasibility and robustness of the proposed control scheme. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot

Ankle rehabilitation robots have recently attracted great attention since they provide various advantages in terms of rehabilitation process from the viewpoints of patients and therapists. This paper presents development and evaluation of a fuzzy logic based adaptive admittance control scheme for a developed 2-DOF redundantly actuated parallel ankle rehabilitation robot. The proposed adaptive admittance control scheme provides the robot to adapt resistance/assistance level according to patients' disability level. In addition, a fuzzy logic controller (FLC) is developed to improve the trajectory tracking ability of the rehabilitation robot subject to external disturbances which possibly occur due to human-robot interaction. The boundary scales of membership functions of the FLC are tuned using cuckoo search algorithm (CSA). A classical proportional-integral-derivative (PID) controller is also tuned using the CSA to examine the performance of the FLC. The effectiveness of the adaptive admittance control scheme is observed in the experimental results. Furthermore, the experimental results demonstrate that the optimized FLC significantly improves the tracking performance of the ankle rehabilitation robot and decreases the steady-state tracking errors about 50% compared to the optimized PID controller. The performances of the developed controllers are evaluated using common error based performance indices indicating that the FLC has roughly 50% better performance than the PID controller.     

Variable Structure Control with Chattering Reduction of a Generalized T‐S Model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non‐linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the state variables. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi‐Sugeno (T‐S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T‐S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T‐S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T‐S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T‐S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC‐VSC shows that both alleviation of chattering and robust performance are achieved.     

A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control

This paper presents a novel fuzzy logic control technique tuned by particle swarm optimization (PSO-FLC) for maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed PV system composes of a current-mode boost converter (CMBC) with bifurcation control. An optimal slope compensation technique is used in the CMBC to keep the system adequately remote from the first bifurcation point in spite of nonlinear characteristics and instabilities of this converter. The proposed PSO technique allows easy and more accurate tuning of FLC compared with the trial-and-error based tuning. Consequently, the proposed PSO-FLC method provides faster tracking of maximum power point (MPP) under varying light intensities and temperature conditions. The proposed MPPT technique is simple and particularly suitable for PV system equipped with CMBC. Experimental results are shown to confirm superiority of the proposed technique comparing with the conventional PVVC technique and the trial-and-error based tuning FLC.     

PHANTOM Omni机器人的自适应模糊滑模控制

针对PHANTOM Omni机器人的位置轨迹跟踪问题,采用了一种基于模糊逻辑的自适应模糊滑模控制方案。利用滑模控制中的切换函数作为输入,根据模糊系统的逼近能力设计控制器,并基于李雅谱诺夫方法设计自适应律对控制器所需参数进行实时调节。仿真中将其与传统的滑模控制进行了比较,仿真结果表明：自适应模糊滑模控制能使PHANTOM Omni机器人更好地实现期望的位置轨迹跟踪并有效地减轻抖振现象,从而证明了该方法在PHANTOM Omni机器人上实施的可行性。     

空间飞行器姿态控制器设计与仿真

该文对空间飞行器姿态系统设计了一种变结构控制器。首先,应用误差四元数法描述空间飞行器姿态运动。同时,在考虑结构摄动和外界扰动的界未知情况下,其基本方法是采用模糊规则优化滑模变结构控制的设计,并且能够在线对结构摄动和外界扰动的界进行估计,使得系统轨迹既能快速趋近滑动面又能降低抖振,从而提高了变结构控制系统的品质。仿真结果表明,该系统对模型：不确定性和外来干扰具有较强的鲁棒性,同时避免了变结构系统固有的抖振问题,性能令人满意。     

Trajectory tracking optimization of mobile robot using artificial immune system

In this paper, an optimization method that provides quick response using artificial immune system, is proposed and applied to a mobile robot for trajectory tracking. The study focuses on the immune theory to derive a quick optimization method that puts emphasis on immunity feedback using memory cells by the expansion and suppression of the test group rather than to derive a specific mathematical model of the artificial immune system. Various trajectories were selected in mobile environment to evaluate the performance of the proposed artificial immune system. The global inputs to the mobile robot are reference position and reference velocity, which are time variables. The global output of mobile robot is a current position. The tracking controller makes position error to be converged to zero. In order to reduce position error, compensation velocities on the track of trajectory are necessary. Input variables of fuzzy are position errors in every sampling time. The output values of fuzzy are compensation velocities. Immune algorithm is implemented to adjust the scaling factor of fuzzy automatically. The results of the computer simulation proved the system to be efficient and effective for tracing the trajectory to the final destination by the mobile robot.

     

A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm

The main aim of this work consists of proposing a new three-step adjusting approach for an improved version of PID-type fuzzy structure in order to determine its design parameters based on a novel hybrid PSO search technique called PSOSCALF, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution. In addition, conventional and self-tuning controllers are designed to get a better understanding of the performance and robustness of the proposed PID-type FLC approach. At first, the proposed PID-type FLC structure is defined as an optimization problem and then the PSOSCALF algorithm is applied to resolve it systematically. Evaluation of the performance quality of the proposed fuzzy structure is accomplished based on the stabilization and tracking control of a nonlinear Inverted Pendulum (IP) system. To make a complete comparison, the performance of three other optimization techniques namely simple PSO, Differential Evolution (DE) and Cuckoo Search (CS) are examined against the hybrid PSOSCALF algorithm. The simulation results demonstrate that the proposed PSOSCALF-tuned PID-type FLC structure is able to decrease the overshoot and integral square error amounts by about 25% and 10%, respectively compared to the self-tuning controllers. Finally, for more validation, all the controllers are tested under four different disturbance scenarios. Obtained results show that the proposed PID-type FLC can better stabilize the pendulum angle under all the scenarios compared to the PID and self-tuning controllers.     

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。     

永磁球形电机的少保守性滑模控制

永磁球形电机轨迹跟踪控制方法常常利用高增益的控制输出来保证系统的鲁棒性及跟踪控制的快速性.但这种保守控制会带来较大的控制作用,甚至导致执行器饱和.为了减少控制的保守性,本文设计了一种带有非线性干扰观测器的模糊滑模控制器来解决球形电机的轨迹跟踪问题.利用干扰观测器对不确定性、摩擦、外界干扰、负载扰动等进行估计,并在控制输入端进行补偿实现对干扰的抑制.并利用滑模控制器抵消干扰观测器的干扰观测误差及不可观测部分的干扰,为了减少滑模的抖振,本文利用模糊逻辑对该部分进行逼近,并利用模糊的输出增益代替滑模的切换增益.此外通过Lyapunov方程证明了本文控制器的稳定性.仿真结果表明在存在模型不确定性及各种干扰的情况下,本文的轨迹跟踪控制具有良好的动静态性能和少保守性.     

Path‐Tracking Control of a Riderless Bicycle Via Road Preview and Speed Adaptation

In this study, a genetic‐fuzzy control system is used to control a riderless bicycle where control parameters can adapt to the speed change of the bicycle. The equations of motion are developed for a bicycle with constraints of rolling‐without‐slipping contact condition between the wheels and ground. This controller consists of two loops: the inner is a roll‐angle‐tracking controller which generates steering torque to control the roll angle while guaranteeing the stability, and the outer is a path‐tracking controller which generates the reference roll angle for the inner loop. The inner loop is a sliding‐mode controller (SMC) designed on the basis of a linear model obtained from a system identification process. By defining a stable sliding surface of error dynamics and an appropriate Lyapunov function, the bicycle can reach the roll‐angle reference in a finite time and follow that reference without chattering. The outer loop determines the proper reference roll‐angle by using a fuzzy‐logic controller (FLC) in which previewing and tracking errors are taken into consideration. The robustness of the proposed controller against speed change and external disturbances is verified by simulations.     

Type-2 fuzzy multi-intersection traffic signal control with differential evolution optimization

This paper proposes a multi-agent type-2 fuzzy logic control (FLC) method optimized by differential evolution (DE) for multi-intersection traffic signal control. Type-2 fuzzy sets can deal with models’ uncertainties efficiently because of its three-dimensional membership functions, but selecting suitable parameters of membership functions and rule base is not easy. DE is adopted to decide the parameters in the type-2 fuzzy system, as it is easy to understand, simple to implement and possesses low space complexity. In order to avoid the computational complexity, the expert rule base and the parameters of membership functions (MF) are optimized by turns. An eleven-intersection traffic network is studied in which each intersection is governed by the proposed controller. A secondary layer controller is set in every intersection to select the proper phase sequence. Furthermore, the communication among the adjacent intersections is implemented using multi-agent system. Simulation experiments are designed to compare communicative type-2 FLC optimized by DE with type-1 FLC, fixed-time signal control, etc. Experimental results indicate that our proposed method can enhance the vehicular throughput rate and reduce delay, queue length and parking rate efficiently.