PERFORMANCE OF PID CONTROLLER OF NONLINEAR SYSTEM USING SWARM INTELLIGENCE TECHNIQUES

In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system. Particle swarm optimization (PSO), Artificial bee colony (ABC), Bacterial foraging optimization (BFO) is some example of swarm intelligence techniques which are focused for PID controller tuning. These algorithms are also tested on perturbed ball and hoop model. Integral square error (ISE) based performance index is used for finding the best possible value of controller parameters. Matlab software is used for designing the ball and hoop model. It is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods

Review on load frequency control for power system stability

Power system stability is the capability of power systems to maintain load magnitude within specified limits under steady state conditions in electrical power transmission. In modern days, the electrical power systems have grown in terms of complexity due to increasing interconnected power line exchange. For that, an inherent of controllers were essential to correct the deviation in the presence of external disturbances. This paper hence aims to review the basic concepts of power system stability in load frequency control. Various control techniques were analyzed and presented. Power system stability can be classified in terms of method to improve power system stability, which are rotor angle stability, frequency stability and voltage stability. It is found that each method has different purpose and focus on solving different types of problem occurred. It is hoped that this study can contribute to clarify the different types of power system stability in terms of where it occurs, and which is the best method based on different situation

FLOWER POLLINATION ALGORITHM UNTUK OPTIMASI PENGENDALI PID PADA PENGENDALIAN KECEPATAN MOTOR INDUKSI

AbstractThe use of Proportional Integral Derivative (PID) controller in induction motors is becoming more and more popular, because of its simple structure. PID controller requires proper parameter setting for optimal performance on the induction motor. The most commonly used method is by trial and error  to determine parameters of the PID controller, but the results obtained are not optimal and incorrect PID controller’s parameters will damage the system. For that reason, in this research it will be shown one of PID parameters tuning method by using Flower Pollination Algorithm (FPA) to optimize and determine the exact parameters of the PID. FPA is a method that is being adapted and applied as a smart algorithm to solve optimization problem. The PID parameters tuning in this study  gives results that the value of kp, ki and kd are  0.4213, 0.2337 and 0.027 respectively. As a comparison, this study has also used Firefly, Cuckoo Search, Particle Swarm, Imperialist Competitive, Ant Colony, Differential Evolution, and Bat method. The FPA method can well tune the PID parameters, so that the resulting overshoot is very small in comparison with the other methods, it is  at 1,019 from the set point.  Compared with other methods, the settling time is also very fast, that is  0.3second. Keywords: PID, FPA, Bee-Colony, Cuckoo, Firefly ABSTRAKPenggunaan pengendali Proportional Integral Derivative (PID) pada motor induksi menjadi semakin populer, karena strukturnya yang sederhana. Pengendali PID memerlukan pengaturan parameter yang tepat untuk kinerja optimal pada motor induksi. Metode yang paling umum digunakan adalah dengan metode trial and  error untuk menentukan parameter pengendali PID, namun hasil yang didapat tidak optimal dan parameter pengendali PID yang tidak tepat akan merusak sistem. Oleh karena itu, dalam penelitian ini, diperlihatkan  salah satu metode penalaan parameter PID dengan menggunakan metode Flower Pollination Algorithm (FPA) untuk mengoptimalkan dan menentukan parameter PID yang tepat. FPA adalah salah satu metode yang diadaptasi dan diterapkan sebagai algoritma cerdas untuk mengatasi masalah optimasi. Hasil penalaan yang diperoleh adalah nilai kp,   k i, dan kd masing-masing  sebesar  0,4213, 0,2337, dan 0,0274. Sebagai perbandingan, penelitian ini juga menggunakan metode Firefly, Cuckoo Search, Particle Swarm, Imperialist Competitive, Ant Colony, Diferential Evolution, dan metode Bat. Metode FPA dapat menala parameter PID  sehingga overshoot yang dihasilkan sangat kecil dibandingkan dengan metode lainnya yaitu sebesar1,019 terhadap  set point. Waktu settling yang diperoleh juga sangat cepat dibandingkan dengan metode lainnya. yaitu 0,3 detik. Kata kunci: PID, FPA, Bee-Colony, Cuckoo, Firefl

Review On Load Frequency Control For Power System Stability

Power system stability is the capability of power systems to maintain load magnitude within specified limits under steady state conditions in electrical power transmission. In modern days, the electrical power systems have grown in terms of complexity due to increasing interconnected power line exchange. For that, an inherent of controllers were essential to correct the deviation in the presence of external disturbances. This paper hence aims to review the basic concepts of power system stability in load frequency control. Various control techniques were analyzed and presented. Power system stability can be classified in terms of method to improve power system stability, which are rotor angle stability, frequency stability and voltage stability. It is found that each method has different purpose and focus on solving different types of problem occurred. It is hoped that this study can contribute to clarify the different types of power system stability in terms of where it occurs, and which is the best method based on different situation

Input shaping-based control schemes for a three dimensional gantry crane

The motion induced sway of oscillatory systems such as gantry cranes may decrease the efficiency of production lines. In this thesis, modelling and development of input shaping-based control schemes for a three dimensional (3D) lab-scaled gantry crane are proposed. Several input shaping schemes are investigated in open and closed-loop systems. The controller performances are investigated in terms of trolley position and sway responses of the 3D crane. Firstly, a new distributed Delay Zero Vibration (DZV) shaper is implemented and compared with Zero Vibration (ZV) shaper and Zero Vibration Derivative (ZVD) shaper. Simulation and experimental results show that all the shapers are able to reduce payload sway significantly while maintaining desired position response specifications. Robustness tests with ±20% error in natural frequency show that DZV shaper exhibits asymmetric robustness behaviour as compared to ZV and ZVD shapers. Secondly, as analytical technique could only provide good performance for linear systems, meta-heuristic based input shaper is proposed to reduce sway of a gantry crane which is a nonlinear system. The results show that designing meta-heuristic-based input shapers provides 30% to 50% improvement as compared to the analytical-based shapers. Subsequently, a particle swarm optimization based optimal performance control scheme is developed in closed-loop system. Simulation and experimental results demonstrate that the controller gives zero overshoot with 60% and 20% improvements in settling time and integrated absolute error value of position response respectively, as compared to a specific designed PID-PID anti swing controller for the lab-scaled gantry crane. It is found that crane control with changing cable length is still a problem to be solved. An adaptive input shaping control scheme that can adapt to variation of cable’s length is developed. Simulation with real crane dimensions and experimental results verify that the controller provides 50% reduction in payload sway for different operational commands with hoisting as compared to the average travel length approach

Hybrid active force control for fixed based rotorcraft

Disturbances are considered major challenges faced in the deployment of rotorcraft unmanned aerial vehicle (UAV) systems. Among different types of rotorcraft systems, the twin-rotor helicopter and quadrotor models are considered the most versatile flying machines nowadays due to their range of applications in the civilian and military sectors. However, these systems are multivariate and highly non-linear, making them difficult to be accurately controlled. Their performance could be further compromised when they are operated in the presence of disturbances or uncertainties. This dissertation presents an innovative hybrid control scheme for rotorcraft systems to improve disturbance rejection capability while maintaining system stability, based on a technique called active force control (AFC) via simulation and experimental works. A detailed dynamic model of each aerial system was derived based on the Euler–Lagrange and Newton-Euler methods, taking into account various assumptions and conditions. As a result of the derived models, a proportional-integral-derivative (PID) controller was designed to achieve the required altitude and attitude motions. Due to the PID's inability to reject applied disturbances, the AFC strategy was incorporated with the designed PID controller, to be known as the PID-AFC scheme. To estimate control parameters automatically, a number of artificial intelligence algorithms were employed in this study, namely the iterative learning algorithm and fuzzy logic. Intelligent rules of these AI algorithms were designed and embedded into the AFC loop, identified as intelligent active force control (IAFC)-based methods. This involved, PID-iterative learning active force control (PID-ILAFC) and PID-fuzzy logic active force control (PID-FLAFC) schemes. To test the performance and robustness of these proposed hybrid control systems, several disturbance models were introduced, namely the sinusoidal wave, pulsating, and Dryden wind gust model disturbances. Integral square error was selected as the index performance to compare between the proposed control schemes. In this study, the effectiveness of the PID-ILAFC strategy in connection with the body jerk performance was investigated in the presence of applied disturbance. In terms of experimental work, hardware-in-the-loop (HIL) experimental tests were conducted for a fixed-base rotorcraft UAV system to investigate how effective are the proposed hybrid PID-ILAFC schemes in disturbance rejection. Simulated results, in time domains, reveal the efficacy of the proposed hybrid IAFC-based control methods in the cancellation of different applied disturbances, while preserving the stability of the rotorcraft system, as compared to the conventional PID controller. In most of the cases, the simulated results show a reduction of more than 55% in settling time. In terms of body jerk performance, it was improved by around 65%, for twin-rotor helicopter system, and by a 45%, for quadrotor system. To achieve the best possible performance, results recommend using the full output signal produced by the AFC strategy according to the sensitivity analysis. The HIL experimental tests results demonstrate that the PID-ILAFC method can improve the disturbance rejection capability when compared to other control systems and show good agreement with the simulated counterpart. However, the selection of the appropriate learning parameters and initial conditions is viewed as a crucial step toward this improved performance

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed

Internal Model Control Using a Self-Recurrent Wavelet Neural Network Trained by an Artificial Immune Technique for Nonlinear Systems

This paper presents a Self-Recurrent Wavelet Neural Network (SRWNN)-based Internal Model Control (IMC) for nonlinear systems. As the internal model, a Nonlinear Autoregressive Moving Average (NARMA-L2) is employed for obtaining a forward system model. Then, this model is directly used to formulate the control law. The proposed SRWNN-based IMC is an enhanced version of a previously published Wavelet Neural Network (WNN)-based IMC scheme. Particularly, the enhancement was attained by considering three modifications, which include the use of an initialization phase for the parameters of the wavelon layer, the utilization of self-feedback connections in the wavelon layer, and the exploitation of RASP1 as the mother wavelet function. The modified Micro Artificial Immune System (modified Micro-AIS) is employed as the training method. From the simulation results, the efficiency of the suggested methodology have been proved concerning control precision and disturbance rejection ability. Moreover, the superiority of the SRWNN over the WNN and the Multilayer Perceptron (MLP) as the IMC controllers has been confirmed from a comparative study. Furthermore, the modified Micro-AIS has accomplished better results compared to the Genetic Algorithm (GA) concerning control precision