Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator

A two-link robotic manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order Fuzzy Proportional-Integral-Derivative (FOFPID) controller for a two-link planar rigid robotic manipulator for trajectory tracking problem is investigated. Robustness testing of FOFPID controller for model uncertainties, disturbance rejection and noise suppression is also investigated. To study the effectiveness of FOFPID controller, its performance is compared with other three controllers namely Fuzzy PID (FPID), Fractional Order PID (FOPID) and conventional PID. For tuning of parameters of all the controllers, Cuckoo Search Algorithm (CSA) optimization technique was used. Two performance indices namely Integral of Absolute Error (IAE) and Integral of Absolute Change in Controller Output (IACCO) having equal weightage for both the links are considered for minimization. Numerical simulation results clearly indicate the superiority of FOFPID controller over the other controllers for trajectory tracking, model uncertainties, disturbance rejection and noise suppression.     

Experimental investigations on Ant Colony Optimized PI control algorithm for Shunt Active Power Filter to improve Power Quality

Active Power Filters (APFs) have become a potential option in mitigating the harmonics and reactive power compensation in single-phase and three-phase AC power networks with Non-Linear Loads (NLLs). Conventionally, the assessment of gain values for Proportional plus Integral (PI) controllers used in APF employs model based controllers. The gain values obtained using traditional method may not give better results under various operating conditions. This paper presents Ant Colony Optimization (ACO) technique to optimize the gain values of PI controller used in Shunt Active Power Filter (SAPF) to improve its dynamic performance. The minimization of Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE) are considered as cost functions for the proposed system. The proposed SAPF is modeled and simulated using MATLAB software with Simulink and SimPowerSystem Blockset Toolboxes. The simulation results of the SAPF using the proposed methodology demonstrates improved settling time (T_s) with ISE as cost function. For instance, the T_s for ISE 4.781 is found to be 28.5 ms. Finally, hardware implementation of the proposed SAPF system is done using Xilinx XCS500E Spartan 3E FPGA board.     

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.     

An improved power conditioning system for grid integration of solar power using ANFIS based FOPID controller

Power loss become common while integrating with common grid and in specific when power produced through Solar. This is the very lacking area which this proposal implements an Adaptive Neuro Fuzzy Inference System (ANFIS) based controller of Fractional Order Proportional Integral Derivative (FOPID) used for Tracking of Maximum PP of Grid Integrated Solar Power Conditioning System. The proposed work advances with different ambient light conditions for maximum power point traction. In this work a clear-cut Photo Voltaic (PV Cell) model has been developed and an intensive and operative training data have been extracted from the developed controller. This produced dataset have been the feeder input for the ANFIS structure in turn to locate the Tracking of Maximum PP (MPPT). Traction of MPPT is done, the FOPID controller is enforced by matching the voltage from the array of Photo Voltaic cell with attained or reference voltage produced by the ANFIS structure. In the meantime driving this PV array, DC to DC converter's duty cycle is controlled for producing maximum power from the structure. The duty cycle in FOPID controller is generated through calculating the error within the reference voltage and PV voltage. Those values are then simulated through Math Lab and the Simulation results show that this proposed work efficiency is better than the regularly employed controllers in the solar power production and conditioning system     

Covariance matrix adaptation evolution strategy based design of fixed structure robust H∞ loop shaping controller

This paper proposes the application of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in fixed structure H_∞ loop shaping controller design. Integral Time Absolute Error (ITAE) performance requirement is incorporated as a constraint with an objective of maximization of stability margin in the fixed structure H_∞ loop shaping controller design problem. Pneumatic servo system, separating tower process and F18 fighter aircraft system are considered as test systems. The CMA-ES designed fixed structure H_∞ loop-shaping controller is compared with the traditional H_∞ loop shaping controller, non-smooth optimization and Heuristic Kalman Algorithm (HKA) based fixed structure H_∞ loop shaping controllers in terms of stability margin. 20% perturbation in the nominal plant is used to validate the robustness of the CMA-ES designed H_∞ loop shaping controller. The effect of Finite Word Length (FWL) is considered to show the implementation difficulties of controller in digital processors. Simulation results demonstrated that CMA-ES based fixed structure H_∞ loop shaping controller is suitable for real time implementation with good robust stability and performance.     

DC–DC Converter with Pi Controller for BLDC Motor Fuzzy Drive System

The Brushless DC Motor drive systems are used widely with renewable energy resources. The power converter controlling technique increases the performance by novel techniques and algorithms. Conventional approaches are mostly focused on buck converter, Fuzzy logic control with various switching activity. In this proposed research work, the QPSO (Quantum Particle Swarm Optimization algorithm) is used on the switching state of converter from the generation unit of solar module. Through the duty cycle pulse from optimization function, the MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) of the Boost converter gets switched when BLDC (Brushless Direct Current Motor) motor drive system requires power. Voltage Source three phase inverter and Boost converter is controlled by proportional-integral (PI) controller. Based on the BLDC drive, the load utilized from the solar generating module. Experimental results analyzed every module of the proposed grid system, which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics (PV) power is generated and the QPSO with Duty cycle switching state is determined. The Boost converter module is boost stage based on generation and load is obtained. Single Ended Primary Inductor Converter (SEPIC) and Zeta converter model is compared with the proposed logic; the proposed boost converter achieves the results. Three phase inverter control, PI, and BLDC motor drive results. Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures. Overall design model is done by using MATLAB/Simulink 2020a.     

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.     

基于免疫粒子群优化算法的增量式PID控制

基于粒子群优化算法的收敛速度快简单易实现的特点和免疫算法的免疫记忆、免疫自我调节和多峰值收敛的特点,本文设计出免疫粒子群算法,并将其应用于PID控制器中。仿真结果表明,免疫粒子群优化算法适用于增量式PID控制,并且基于免疫粒子群优化算法的增量式PID控制的跟踪效果和抗干扰能力比粒子群优化算法的PID控制和基于免疫算法的增量式PID控制跟踪效果和抗干扰能力都要好。     

基于权重QPSO算法的PID控制器参数优化

传统的PID控制器参数优化方法容易产生振荡和较大的超调量,因此智能算法如遗传算法（SGA）和粒子群算法（PSO）被用于参数优化,弥补传统算法的不足,但是遗传算法在进化过程中收敛速度慢,粒子群算法存在易于早熟的缺点。在分析量子粒子群算法（QPSO）的基础上,在算法中引入了权重系数,提出使用改进的量子粒子群算法（WQPSO）优化PID控制器参数。将改进量子粒子群算法与量子粒子群算法、粒子群算法通过benchmark测试函数进行了比较。最后,通过三个传递函数实例,分别使用Z-N、GA、PSO方法和改进的量子粒子群算法进行了PID控制器参数优化设计,并对结果进行了分析。     

基于Adaptive Interaction理论的PID控制器的设计及应用

本文在Adaptive Interaction理论的基础上,提出了一种新的自调整 PID 控制器。这种新的控制器根据输入及其误差信号进行在线训练,通过误差评价函数的最小化,在模型未知的情况下能很好地调整比例、积分、微分三个参数。对于被控对象的变化具有鲁棒性,很大程度上解决了传统的 PID 控制器对于非线性、不稳定系统控制效果不佳及在线调整困难的问题。通过仿真实例,验证了应用 Adaptive Interaction 理论的 PID 控制器的有效性和实用性。     

Design and Robust Performance Evaluation of a Fractional Order PID Controller Applied to a DC Motor

This paper proposes a methodology for the quantitative robustness evaluation of PID controllers employed in a DC motor. The robustness analysis is performed employing a 23 factorial experimental design for a fractional order proportional integral and derivative controller (FOPID), integer order proportional integral and derivative controller (IOPID) and the Skogestad internal model control controller (SIMC). The factors assumed in experiment are the presence of random noise, external disturbances in the system input and variable load. As output variables, the experimental design employs the system step response and the controller action. Practical implementation of FOPID and IOPID controllers uses the MATLAB stateflow toolbox and a NI data acquisition system. Results of the robustness analysis show that the FOPID controller has a better performance and robust stability against the experiment factors.      

模糊PID控制器在无刷直流永磁电机控制系统中的应用

PID算法是一个广泛应用于工业控制的算法,但它对非线性和不确定性系统适应性不够理想。模糊PID控制将模糊逻辑推理引．NPID参数的在线自调整,是目前一种较为先进的控制器。本文以一台无刷直流永磁电动机为控制对象,通过Matlab中的Simulink模块和模糊控制工具箱实现模糊PID的控制系统,并与常规PID控制进行了分析比较,有较好的控制效果,适应性强。     

Intelligent maximum power tracking control of a PMSG wind energy conversion system

A comparative control study for a maximum power tracking strategy of variable speed wind turbine is provided. The system consists of a direct drive permanent magnet synchronous generator (PMSG) and an uncontrolled rectifier followed by a DC/DC switch‐mode step down converter connected to a DC load. The buck converter is used to catch the maximum power from the wind by generating an efficient duty cycle. Distinct Maximum Power Point Tracking (MPPT) algorithms are analyzed and compared: a classical Proportional‐Integral controller (PI) and two based Fuzzy Logic Controllers (FLC), including a conventional Fuzzy‐PI and an Adaptive FLC‐PI. The main aim of the presented study is to develop an advanced control scheme for wind generators to ensure a high level operating of the system and a maximum power extraction from the wind. This is achieved by analyzing the behavior of the system under fluctuating wind conditions employing Matlab Simpower Systems tool. Simulation results confirm that the Adaptive FLC‐PI controller algorithm has better performances in terms of dynamic response and efficiency especially in comparison with the ones of a PI controller under variable wind speed.     

Fractional fixed-structure H∞ controller design using Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity

The optimization of a fixed-structure controller is in general computationally intractable, posing a challenge to control system engineers. This paper introduces a novel technique for designing such control techniques by formulating it as a nonlinear non-convex constrained problem. A novel PSO algorithm, namely the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity (ALPSOFV), is proposed for improving the convergence rate. The structure of the controller is selectable and, therefore, the Fractional Order Proportional Integral Derivative (FOPID) algorithm is chosen in the scope of the study. The results for three test examples show the good performance of the ALPSOFV.     

基于动态概率变异的Cauchy粒子群优化

介绍了标准粒子群优化（SPSO）算法,在两种粒子群改进算法Gaussian Swarm和Fuzzy PSO的基础上提出了Cauchy粒子群优化（CPSO）算法,并将遗传算法中的变异操作引入粒子群优化,形成了动态概率变异Cauchy粒子群优化（DMCPSO）算法。用3个基准函数进行实验,结果表明,DMCPSO算法性能优于SPSO和CPSO算法。     

Ant colony optimization technique tuned controller for frequency regulation of single area nuclear power generating system

Obtaining energy from the power generating unit is a more critical issue in recent days due to the sudden increasing load demand than that of the past. In this proposed work the Load Frequency Control (LFC) of nuclear power system is studied by implementing the Proportional Integral Derivative (PID) controller as a secondary controller. The controller gain values are optimized by utilizing Ant Colony Optimization Technique (ACOT) by considering three different cost functions (Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). Also, the effect of different energy storage units Hydrogen Aqua Electrolyzer (HAE), Superconducting Magnetic Energy Storage (SMES), Redox Flow Battery (RFB) is also verified by considering one percent step load disturbance in the investigated system. Finally, the simulation result obviously shows that ITAE cost function gives better result in terms of minimal domain specification parameters with good stability. Also, SMES improves the performance of system over HAE and RFB energy storage unit.     

基于粒子群优化算法的永磁同步电机H2/H∞混合控制

为了提高永磁同步电机(PMSM)控制系统的控制性能,设计了一种混合H2/H∞控制器。通过混合灵敏度方法设计系统的H∞次优控制器,采用粒子群优化(PSO)算法选取H2性能指标最小的控制器,从而得到混合H2/H∞控制器。采用仿真对所设计控制器的性能与比例积分(PI)控制器进行了对比测试,测试结果证明了混合H2/H∞控制器具有比PI控制器更好的控制效果,同时也表明采用PSO算法进行控制器设计是有效、可行的。     

Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System: a hybrid method for feature selection and land pattern classification

Adaptive Neuro-Fuzzy Inference System (ANFIS) is a robust method in solving non-linear classification by employing a human-readable interpretation manner. This paper verified a hybrid model, named WANFIS, where Whale Optimization Algorithm (WOA) was used for feature selection and tuning parameters of the ANFIS for land-cover classification. Hanoi, the capital of Vietnam, was selected as a case study, because of its complex surface morphology. The model was trained and validated with different data sets, which were subsets of the segmented objects from SPOT 7 satellite data (1.5 m in panchromatic and 6 m multiple spectral bands). The image segmentation was carried out by using PCI Geomatics software (evaluation version), and output objects with associated spectral, shape, and metric information were selected as input data to train and validate the proposed model. For accuracy assessment, the performance of the model was compared to several benchmarked classifiers by using standard statistical indicators such as Receiver Operator Characteristics, Area under ROC, Root Mean Square Error, Absolute Mean Error, Kappa index, and Overall accuracy. The results showed that WANFIS outperformed the other in almost all training data sets for both operations. It could be concluded that the examination of the classification model in different training data sizes is significant, and the proper determination of predictor variables and training sizes would improve the quality of classification of remotely sensed data.     

新型防抱死制动系统(E-ABS)的应用研究

本文介绍了一种新型的汽车防抱死制动系统，电动防抱死制动系统(E—ABS)，并对于简化的汽车制动模型运用PID控制器及Fuzzy—PID控制器进行仿真试验，份真结果表明这两种控制率能取得较好的控制效果。     

Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

This paper proposes an optimal power control strategy for inverter-based Distributed Generation (DG) units in autonomous microgrids. It consists of power, voltage, and current controllers with Proportional-Integral (PI) regulators. The droop concept is used for the power control strategy. Static parameters in PI regulators may not ensure the most optimal solution due to inevitable changes happening in microgrid configuration and loads. In the proposed method, after occurring a load change in a standalone microgrid, parameters of the PI controller are dynamically adjusted to get the most optimal operating point that satisfies objective functions. The optimization problem is formulated as a multi-objective programming with objective functions of minimizing overshoot/undershoot, settling time, rise time, and Integral Time Absolute Error (ITAE) in the output voltage. These objective functions are combined using fuzzy memberships. The Hybrid Big Bang-Big Crunch algorithm (HBB-BC) is used to solve the optimization problem. The proposed methodology is simulated on a case study and according to obtained results, the suggested tuning of PI parameters leads to a better voltage response than previous methods. The case study is also solved using the Particle Swarm Optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms and it is found that the HBB-BC gives a better solution than the PSO and BB-BC.