A Hybrid Approach for Design of Stable Adaptive Fuzzy Controllers Employing Lyapunov Theory and Particle Swarm Optimization

This paper proposes a new approach for designing stable adaptive fuzzy controllers, which employs a hybridization of a conventional Lyapunov-theory-based approach and a particle swarm optimization (PSO) based stochastic optimization approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and can simultaneously provide satisfactory performance. The design methodology for the controller simultaneously utilizes the good features of PSO (capable of providing good global search capability, required to provide a high degree of automation) and Lyapunov-based tuning (providing fast adaptation utilizing a local search method). Three different variants of the hybrid controller are proposed in this paper. These variants are implemented for benchmark simulation case studies and real-life experimentation, and their results demonstrate the usefulness of the proposed approach.     

Optimal design of fractional-order PID controller for five bar linkage robot using a new particle swarm optimization algorithm

This paper introduces a new version of the particle swarm optimization (PSO) method. Two basic modifications for the conventional PSO algorithm are proposed to improve the performance of the algorithm. The first modification inserts adaptive accelerator parameters into the original velocity update formula of the PSO which speeds up the convergence rate of the algorithm. The ability of the algorithm in escaping from local optima is improved using the second modification. In this case, some particles of the swarm, which are named the superseding particles, are selected to be mutated with some probability. The proposed modified PSO (MPSO) is simple to be implemented, fast and reliable. To validate the efficiency and applicability of the MPSO, it is applied for designing optimal fractional-order PID (FOPID) controllers for some benchmark transfer functions. Then, the introduced MPSO is applied for tuning the parameters of FOPID controllers for a five bar linkage robot. Sensitivity analysis over the fractional order of the PID controller is also provided. Numerical simulations reveal that the MPSO can optimally tune the parameters of FOPID controllers.     

Designing Proportional Integral Controller for a Solar Powered DC‐DC Converter Using PIPSO Algorithm and Deep Learning approach in Intelligent Bio‐Medical Applications

Numerous engineering complexities are simplified using optimization algorithms. In a solar power system, the necessity of the voltage regulator is obvious. To control the regulator existent research works used PI, PID controllers that might have an unwanted transient response. To overcome such drawbacks here, a fresh scheme is proposed for the designing of the adaptive sliding mode (SM) controller of a solar powered LUO converter using optimization algorithms. The PSO (‘Particle Swarm Optimization') is proved to expedite the convergence characteristic for many applications. Here, an ameliorated PSO version is developed. This algorithm is termed the Parameter Improved‐PSO (PIPSO) algorithm. In this algorithm, the parameters, say, inertia weight, social along with cognitive agents is updated in every generation. The Proportional Integrator (PI) controller is used. The gain of this controller is tuned using the PIPSO. This algorithm's objective function is to lessen ISE (‘Integral Squared Error’) of the converter's output voltage. This parameter is picked as the objective function of the optimization algorithm. The proposed PIPSO is established to show better outcomes when contrasted to the traditional PSO concerning tuning a collection of parameters. An analysis is also made to evaluate the effect of usage of the solar panel () in the proposed work.     

Optimal fuzzy controller parameters using PSO for speed control of Quasi-Z Source DC/DC converter fed drive

The conventional controller suffers from uncertain parameters and non-linear qualities of Quasi-Z Source converter. However they are computationally inefficient extending to optimize the fuzzy controller parameters, since they exhaustively search the optimal values to optimize the objective functions. To overcome this drawback, a PSO based fuzzy controller parameter optimization is presented in this paper. The PSO algorithm is used to find the optimal fuzzy parameters for minimizing the objective functions. The feasibility of the proposed PSO technique has been simulated and tested. The results are bench marked with conventional fuzzy controller and genetic algorithm for two types of DC/DC converters namely double input Z-Source converter and Quasi-Z Source converter. The results of both the DC/DC converters for several existing methods illustrate the effectiveness and robustness of the proposed algorithm.     

Numerical simulation for Fuzzy-PID controllers and helping EP reproduction with PSO hybrid algorithm

Many Fuzzy-PID controller schemes used in industry today are based on some sort of simplified fuzzy reasoning methods and PID parameters. We present a design for Fuzzy-PID controllers using a novel PSO-EP-based hybrid algorithm. We succeed in making mathematical calculations and in encouraging EP reproduction with PSO. The main advantage of our design is that the integration of the PSO-EP-based hybrid algorithm structure generates new parameters for the Fuzzy-PID control schemes. The proposed algorithm is an improved variant of PSO, a relatively recently introduced stochastic optimization strategy for Fuzzy-PID controllers that is investigated in this study. The function of Fuzzy-PID controllers is illustrated by means of a model of the induction motor control system and a higher-order numerical model.     

Frequency-domain Tuning of Robust Fixed-structure Controllers via Quantum-behaved Particle Swarm Optimizer with Cyclic Neighborhood Topology

This paper presents a constrained particle swarm optimization (PSO) algorithm with a cyclic neighborhood topology inspired by the quantum behavior of particles, and describes its application to the frequency-domain tuning of robust fixed-structure controllers. Two main methodologies for improving the exploration and exploitation performance of the PSO framework are described. First, a PSO scheme with a neighborhood structure based on a cyclic network topology is presented. This scheme enhances the exploration ability of the swarm and effectively reduces the probability of premature convergence to local optima. Second, the above PSO scheme is hybridized using a distributed quantum-principle-based offspring creation mechanism. Such a hybridized PSO framework enables neighboring particles to concentrate the search around the region covered by those particles to refine the candidate solution. A frequency-domain tuning method for fixed-structure controllers is then demonstrated. This method guarantees certain preassigned performance specifications based on the developed PSO technique. A typical numerical example is considered, and the results clearly demonstrate that the proposed PSO scheme provides a novel and powerful impetus with remarkable reliability for robust fixed-structure controller syntheses. Further, an experiment was conducted on a magnetic levitation system to compare the proposed strategy with a well-known frequency-domain tuning method implemented in the MATLAB tool for Structured H_∞ Synthesis. The comparative experimental results validate the effectiveness of the proposed tuning strategy in practical applications.     

An evolutionary approach to design practical μ synthesis controllers

This paper proposes an evolutionary approach to solve μ synthesis problem. The goal is to achieve low order, practical μ synthesis controllers without any order reduction. In the proposed approach µ synthesis problem is solved as a constraint optimization problem in which robust stability and robust performance based on μ analysis are considered as the constraint and the cost function respectively. In order to solve the optimization problem an improved particle swarm optimization (PSO) is chosen to find the required coefficients of a structure-specified controller. The performance and robustness of the proposed controller are investigated by an uncertain mass-damper-spring system and is compared with the D-K iteration controller (the conventional solution to μ synthesis problem). Simulation results demonstrate the advantages of the proposed controller in terms of simple structure and robustness against plant perturbations and disturbances in comparison with D-K iteration controller.     

On-line tuning PID parameters in an idling engine based on a modified BP neural network by particle swarm optimization

PID control systems are widely used in many fields, and many methods to tune the parameters of PID controllers are known. When the characteristics of the object are changed, the traditional PID control should be adjusted by empirical knowledge. This may result in a worse performance by the system. In this article, a new method to tune PID parameters, called the back-propagation network modified by particle swarm optimization, is proposed. This algorithm combines conventional PID control with a back propagation neural network (BPNN) and particle swarm optimization (PSO). This method is demonstrated in the engine idling-speed control problem. The proposed method provides considerable performance benefits compared with a traditional controller in this simulation.     

Efficient Single-Stage Bridgeless AC to DC Converter Using Grey Wolf Optimization

Bridgeless single-stage converters are used for efficient (alternative current) AC-(direct current) DC conversion. These converters control generators, like electromagnetic meso- and micro-scale generators with low voltage. Power factor correction helps increase the factor of the power supply. The main advantage of the power factor is it shapes the input current for increasing the real power of the AC supply. In this paper, a two-switch bridgeless rectifier topology is designed with a power factor correction capability. For the proposed converter topology to have good power quality parameters, the closed loop scheme, which uses the grey wolf optimization (GWO) algorithm, is implemented. The successes of GWO encourage this research to implement GWO in the topology. The performance of the proposed topology is analyzed under different load conditions. Simulation is carried out using the MATLAB/Simulink environment, and the results are compared with those of conventional (proportional integral derivative) PID and (particle swarm optimization) PSO controllers. To validate the simulation results, a 350-W hardware prototype is implemented, and the voltage ripple, efficiency, and power factor under different load conditions are analyzed and tabulated. The comparative study clearly indicates that the proposed converter topology with a closed loop control scheme using the GWO algorithm improves the power factor to 0.9732 and reduces the voltage ripple to 0.12% with a conversion efficiency of 98.25%.     

Hyperchaos synchronization using PSO-optimized RBF-based controllers to improve security of communication systems

This paper studies the Lorenz hyperchaos synchronization and its application to improve the security of communication systems. Two methods are proposed to synchronize the general forms of hyperchaotic systems, and their performance in secure communication application is verified. These methods use the radial basis function (RBF)-based neural controllers for this purpose. The first method uses a standard RBF neural controller. Particle swarm optimization (PSO) algorithm is used to derive and optimize the parameters of the RBF controller. In the second method, with the aim of increasing the robustness of the RBF controller, an error integral term is added to the equations of RBF neural network. For this method, the coefficients of the error integral component and the parameters of RBF neural network are also derived and optimized via PSO algorithm. For better comparison, the proposed methods and an optimal PID controller optimized by PSO are applied to the Lorenz hyperchaotic system for secure communication. Simulation results show the effectiveness and superiority of the proposed methods in both performance and robustness in comparison with the PID controller.     

Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.     

Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances over a wide range of loading conditions and parameter variations and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.     

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.     

Simulation of novel hybrid method to improve dynamic responses with PSS and UPFC by fuzzy logic controller

In this paper, a hybrid method is proposed to damp frequency and power oscillations in the power system equipped with unified power flow controller (UPFC) and power system stabilizer (PSS) controllers. The method is robust with respect to operating point’s changes. This hybrid method consists of two stages: offline and online. In the offline stage, the coefficients of PSS and UPFC controllers for different operating points have been found by PSO algorithm; then in the second stage, online new fuzzy controller is proposed to select the best PSS and UPFC coefficients according to operating point. The proposed method is simulated for single machine infinite bus system-associated PSS and UPFC for three different operating points in MATLAB software, and results of proposed method simulation are investigated and compared with conventional PSS (CPSS) + UPFC, CPSS controllers. Simulation results show that the proposed method has a better performance.

     

基于粒子群优化的一类模糊控制器设计

针对一般模糊控制器存在稳态性能与动态性能之间的矛盾,提出一种参数自整定模糊控制器.该控制器结构简单,算法简便,具有良好的动态特性,能有效消除静态偏差,且有一定的鲁棒性.为避免模糊控制器设计中参数调试的复杂性,获得最佳的控制性能,应用改进的自适应粒子群优化算法对模糊控制器参数进行优化设计.通过典型的被控对象的仿真研究,验证了所提出算法的有效性和适应性以及所设计控制器的优越性.     

基于事件触发的Buck型DC-DC变换器有限时间控制

针对Buck型DC-DC变换器输出电压跟踪控制问题,提出了一种基于事件触发机制的有限时间控制方案。首先,将Buck变换器建模成一类反馈型非线性系统。然后,为能有效地避免通信资源的浪费,通过构造一种状态变换设计了一种事件触发机制;同时,利用反步法,设计了系统的状态反馈控制器,该控制器在事件触发时刻更新;然后,基于所设计的事件触发控制器,利用有限时间Lyapunov稳定性理论分析了系统的稳定性,并证明了所设计的控制方案不会发生Zeno现象;最后,通过Buck变换器仿真实例验证了所提出的事件触发控制方案的有效性,仿真结果表明了在所设计的控制方案下,Buck型DC-DC变换器的输出在有限时间内可以达到期望值,同时还能减少通信资源的浪费。     

Performance improvement of aircraft pitch angle control using a new reduced order fractionalized PID controller

In this paper, a new optimal reduced order fractionalized PID (ROFPID) controller based on the Harris Hawks Optimization Algorithm (HHOA) is proposed for aircraft pitch angle control. Statistical tests, analysis of the index of performance, and disturbance rejection, as well as transient and frequency responses, were all used to validate the effectiveness of the proposed approach. The performance of the proposed HHOA-ROFPID and HHOA-ROFPID controllers with Oustaloup and Matsuda approximations was then compared not only to the PID controller tuned by the original HHO algorithm but also to other controllers tuned by cutting-edge meta-heuristic algorithms such as the atom search optimization algorithm (ASOA), Salp Swarm Algorithm (SSA), sine-cosine algorithm (SCA), and Grey wolf optimization algorithm (GOA). Simulation results show that the proposed controller with the Matsuda approximation provides better and more robust performance compared to the proposed controller with the Oustaloup approximation and other existing controllers in terms of percentage overshoot, settling time, rise time, and disturbance rejection.     

基于SG的Buck变换器自适应反步法控制

针对Buck变换器的非线性特性,考虑在电感电流连续导通模式下的数学模型,采用自适应反步法设计其闭环控制器,同时基于System Generator提出了数字控制器的实现方法,并分析了其负载扰动和电源扰动特性,将仿真结果与PI控制方式相比较,结果表明自适应反步控制的优越性和System Generator设计开发的有效性,为FPGA实现Buck变换器的数字控制器提供了新的设计流程,也为进一步研究其他DC-DC变换器的非线性控制提供了新思路.     

Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications

This paper proposes the optimization of the type-2 membership functions for the average approximation of an interval of type-2 fuzzy controller (AT2-FLC) using PSO, where the optimization only considers certain points of the membership functions and, the fuzzy rules are not modified so that the algorithm minimizes the runtime. The AT2-FLC regulates the speed of a DC motor and is coded in VHDL for a FPGA Xilinx Spartan 3A. We compared the results of the optimization using PSO method with a genetic algorithm optimization of an AT2-FLC under uncertainty and the results are discussed. The main contribution of the paper is the design, simulation and implementation of PSO optimization of interval tye-2 fuzzy controllers for FPGA applications.     

基于主电流控制的并联DC-DC升压变换器改进

并联DC-DC升压变换器之间的负载电流分配关系到系统可靠性;针对并联DC-DC升压变换器,提出了一种基于主电流控制的改进下垂方法;该方法利用并联DC-DC升压变换器的算法,根据下垂法的负载调节特性自适应地调整每个变换器的参考电压;与传统的下垂法不同,在所有变换器的内环控制器中使用其中一个并联变换器的电流反馈信号,以避免并联变换器控制回路的时延差异;研究结果显示,该算法保证了负载均流与下垂法的负载调节特性一致,在并联变换器参数失配的情况下对该算法进行了测试,通过Matlab/Simulink仿真验证了该算法的有效性。