Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

In this paper, the Twin rotor MIMO system (TRMS) is tuned by Nonlinear PID controller using Evolutionary Computation methods. The proposed Nonlinear PID controller, used to tune TRMS, improves the system performance with additional degrees of freedom. Evolutionary Computation methods such as Differential Search Algorithm (DSA), real coded Genetic Algorithm (RGA) with simulated binary crossover (SBX) and Particle Swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are used to determine the optimal parameters of the proposed controller by minimizing Integral Square Error (ISE) for rotor response of TRMS. SIMULINK MATLAB software is used for simulating the system. The statistical performance of the controller is analysed among twenty independent trials by taking best, worst, mean and standard deviations of ISE. Simulation results reveal that TRMS system tuned by nonlinear PID controller using Particle Swarm optimization (PSO) is better than the other methods

Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system

Ant Colony Optimization Algorithm Applied to Ship Steering Control

AbstractThe article describes the application of an ant algorithm to optimize parameters of the ship course controller, based on the algorithm of PID control. The ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located. The procedure of parameter tuning for the ship course controller was applied to the case when the controller was changing the course of the ship and the integral action was turned off. Tuned parameters of the ship course controller are evaluated by the ant colony algorithm, which makes use of the course error based objective function and a given rudder deflection. The results were compared with equivalent results obtained using a genetic algorithm. Moreover, the effectiveness of PID controller parameter tuning was assessed using the ant colony optimization algorithm

Modern Optimization Techniques for PID Parameters of Electrohydraulic Servo Control System

Electrohydraulic servo system has been used in industry in a wide number of applications. Its dynamics are highly nonlinear and also have large extent of model uncertainties and external disturbances. In order to in-crease the reliability, controllability and utilizing the superior speed of response achievable from electrohydraulic systems, further research is required to develop a control software has the ability of overcoming the problems of system nonlinearities. In This paper, a Proportional Integral Derivative (PID) controller is designed and attached to electrohydraulic servo actuator system to control its stability. The PID parameters are optimized by using four techniques: Particle Swarm Optimization (PSO), Bacteria Foraging Algorithm (BFA), Genetic Algorithm (GA), and Ant colony optimization (ACO). The simulation results show that the steady-state error of system is eliminated; the rapidity is enhanced by PSO applied on Proportional Integral Derivative (PPID), Bacteria Foraging Algorithm applied on Proportional Integral Derivative (BPID), GA applied on Proportional Integral Derivative (GPID), and ACO Algorithm applied on Proportional Integral Derivative (ACO-PID) controllers when the system parameter variation was happened, and has good performances using in real applications. A comparative study between used modern optimization techniques are described in the paper and the tradeoff between them

Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid control system by GA-LQR based PID controller

This research work presents an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled and the formulated in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively adopted to stabilize the 3DOF helicopter system

Frequency Control of Microgrid with Renewable Generation using PID Controller based Krill Herd

The main of this paper is to provide optimal control of a state microgrid system. The proposed configuration composes of renewable generation systems such as solar photovoltaic system and wind turbine generator with a Diesel Engine Generator and Fuel-Cell. An Aqua electrolyzer and other energy storage systems such as battery and flywheel are also used in the proposed microgrid. A standard PID (Proportional Integral Derivative) controller scheme is introduced whose its parameters are determined using different optimizations algorithm such as Algorithm Genetic, Particle Swarm Optimization, and Krill Herd algorithm for minimizing frequency and power deviations, in order to enhance the operation of this system. The PID controller gains are optimized by resolving an objective function. The simulation results are shown, and given that the Krill Herd algorithm improves the performance of the system in comparison with GA and PSO based on PID. The efficiency of the system is improved

Velocity control of a unicycle type of mobile robot using optimal pid controller

A unicycle model of control a mobile robot is a simplified modeling approach modified from the differential drive mobile robots. Instead of controlling the right speed, VR and the left speed, VL of the drive systems, the unicycle model is using u and ω as the controller parameters. Tracking is much easier in this model. In this paper, the dynamic of the robot parameter is controlled using two blocks of Proportional-Integral-Derivative(PID) controllers. The gains of the PID are firstly determined using particle swarm optimization(PSO) in offline mode. After the optimal gain is determined, the tracking of the robot’s trajectory is performed online with optimal PID controller. The achieved results of the proposed scheme are compared with those of dynamic model optimized with genetic algorithm(GA) and manually tuned PID controller gains. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error(IAE) performance index. The simulation results obtained reveal advantages of the proposed PSO-PID dynamic controller for trajectory tracking of a unicycle type of mobile robot. A MATLAB-Simulink program is used to simulate the designed system and the results are graphically plotted. In addition, numerical simulations using 8-shape as a reference trajectory with several numbers of iterations are reported to show the validity of the proposed scheme

Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (~0.005 s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E-06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system

Optimasi Kendali PID menggunakan Algoritma Genetika untuk Penerbangan Quadrotor

Quadrotor is square-form unmanned aerial vehicle (UAV) type with four motor in each arms. Quadrotor has ability to take-off and landing vertically. This research designs and creates a system that capable to stabilize the quadrotor flight also able to maintain roll, pitch and yaw angle using PID controller optimized by genetic algorithm, one of evolutionary algorithms.PID is a common applied controller including to control the quadrotor. Tunning or setting PID parameter process is needed to obtain fit PID parameters. Tunning is very important to reach quadrotor flight stability. This research applies Ziegler-Nichols tunning to obtain PID parameters. Then the PID parameters will be a reference for genetic algorithm optimization process to obtain the suitest PID parameter to control roll, pitch ,and yaw angle.Optimization process result show quadrotor controller capable to reach stability with steady state error for pitch angle in range 2,34 degree conterclockwise to 3,37 degree clockwise, for roll angle in range 2,99 degreee counterclockwise to 2,27 degree clockwise, and for yaw angle in range 8,39 degree counterclockwise to 3,89 degree clockwise