Design and co-simulation of a fuzzy gain-scheduled PID controller based on particle swarm optimization algorithms for a quad tilt wing unmanned aerial vehicle

2018 ◽  
Vol 40 (14) ◽  
pp. 3933-3952 ◽  
Author(s):  
Khaled Ben Khoud ◽  
Soufiene Bouallègue ◽  
Mounir Ayadi
Keyword(s):  
Particle Swarm Optimization ◽  
Computer Aided Design ◽  
Pid Controller ◽  
Particle Swarm ◽  
Data Communication ◽  
Pso Algorithm ◽  
Pid Controllers ◽  
Stochastic Algorithm ◽  
Swarm Optimization ◽  
Gain Scheduled

This paper deals with the systematic design and hardware co-simulation of a fuzzy gain-scheduled proportional–integral–derivative (GS-PID) controller for a quad tilt wing (QTW) type of unmanned aerial vehicles (UAVs) based on different variants of the particle swarm optimization (PSO) algorithm. The fuzzy PID gains scheduling problem for the stabilization of the roll, pitch and yaw dynamics of the QTW vehicle is formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. PSO algorithms with variable inertia weight (PSO-In), PSO with constriction factor (PSO-Co) and PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim further to improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. The robustness of the designed PSO-based fuzzy GS-PID controllers under actuators faults is shown on the non-linear model of the QTW. All optimized fuzzy GS-PID controllers are then co-simulated within a processor-in-the-loop (PIL) framework based on an embedded NI myRIO-1900 board and a host PC. Such a proposed software (SW) and hardware (HW) computer aided design (CAD) platform is based on the Control Design and Simulation (CDSim) module of the LabVIEW environment as well as a set-up Network Streams-based data communication protocol. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based fuzzy gains scheduled PID controllers for the QTW’s attitude flight stabilization.

scholarly journals Design and simulation of a 2DOF PID controller based on particle swarm optimization algorithms for a thermal phase of hybrid vehicle

2018 ◽  
Vol 7 (4) ◽  
pp. 4644
Author(s):  
Mohamed Hedi Hmidi ◽  
Ines Ben Salem ◽  
Lilia El Amraoui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Hybrid Vehicle ◽  
Pid Controllers ◽  
Stochastic Algorithm ◽  
Systematic Design ◽  
Swarm Optimization ◽  
Vehicle Velocity ◽  
Two Degree Of Freedom

This paper deals with the systematic design of a PID regulators with two degree of freedom 2DOF for a Hybrid vehicle driving cycle based on different variants of the Particle Swarm Optimization (PSO) algorithm. The PID 2DOF problem for the stabilization of the velocity dynamics of the hybrid vehicle are formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. Both PSO algorithm with variable inertia weight (PSO-In), PSO with Constriction factor (PSO-Co), PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim to further improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. All optimized 2DOF PID controllers are then simulated within a Matlab Simulink. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based 2 DOF controllers for the hybrid Vehicle velocity stabilization.  

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

scholarly journals Design Tunable Robust Controllers for Unmanned Aerial Vehicle Based on Particle Swarm Optimization Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 89-100
Author(s):  
Baqir Abdul-Samed ◽  
Ammar Aldair
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Low Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Controlled System ◽  
Main Challenge ◽  
Optimal Values

PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

scholarly journals Performance of PID Controller Tuning based on Particle Swarm Optimization and Firefly Algorithm

2019 ◽  
Vol 8 (3S2) ◽  
pp. 225-230 ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Transient Responses ◽  
Swarm Optimization ◽  
Pid Controller Tuning ◽  
Tank System

This paper shows the study of tuning the Proportional-Integral-Derivatives (PID) in the application of coupled tank system. The controller was tuned by using an optimization technique which is a Firefly Algorithm (FA) and a Particle Swarm Optimization (PSO) Algorithm. Both FA and PSO performance were evaluated by using performance index of Integral Time Square Error (ITSE). The systems response of FA and PSO were gathered and compared in term of transient responses, ITSE and standard deviation by considering the system condition of with and without a disturbance. The simulation is conducted by using MATLAB software. The result shows that the FA giving a better system performance compared to PSO in term of overall transient responses.

scholarly journals PID Controller Design for Cruise Control System using Particle Swarm Optimization

2017 ◽  
Vol 43 (2) ◽  
pp. 30-35
Author(s):  
Ahmed Abdulnabi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Controller Design ◽  
Robustness Analysis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Peak Time ◽  
Cruise Control ◽  
Swarm Optimization ◽  
Best Response

This paper presents a design of a Proportional-Integral-Derivative (PID) controller for automobile cruise controlsystem. The parameters of the PID controller, which are the proportional ( ), derivative ( ) , and integrator ( ), have beenselected using Particle Swarm Optimization (PSO) algorithm. In this study, the overall system performance has beencompared with other predesigned controllers (conventional PID, Fuzzy logic PID, state space, and Genetic algorithm basedPID controller). The simulation result illustrates that PSO based PID controller gives the best response in terms of settlingtime, rise time, peak time, and maximum overshot. The robustness analysis shows that the system is robust despite thedeviations in some of the system parameters.

scholarly journals A Fractional PID Controller Based on Particle Swarm Optimization Algorithm

2020 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Yinglei Song
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameter Design ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Control Accuracy

Fractional PID controller is a convenient fractional structure that has been used to solve many problems in automatic control. The fractional scale proportional-integral-differential controller is a generalization of the integer order PID controller in the complex domain. By introducing two adjustable parameters  and , the controller parameter tuning range becomes larger, but the parameter design becomes more complex. This paper presents a new method for the design of fractional PID controllers. Specifically, the parameters of a fractional PID controller are optimized by a particle swarm optimization algorithm. Our simulation results on cold rolling APC system show that the designed controller can achieve control accuracy higher than that of a traditional PID controller.

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