Robust control of interlinking converter using PSO and ABC algorithms

2020 ◽  
Vol 13 ◽  
Author(s):  
Mudita Juneja ◽  
Shyam Krishna Nagar
Keyword(s):  
Robust Control ◽  
Controller Design ◽  
Optimization Algorithms ◽  
Tracking Error ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Loop Shaping ◽  
Optimal Control Scheme

Objective: In this paper, an optimal control scheme for the Interlinking Converter (IC) system is achieved by the proper regulation of its gate switching functions through appropriate optimal feedback controller design. Methods: Proportional-Integral-Derivative (PID), Fractional Order Proportional-Integral-Derivative (FOPID) and Hinfinity loop shaping controller have been designed for the two-fold control objective of simultaneous improvement in system robustness and reduced tracking error using parameter tuning via Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) optimization algorithms. Results: The controller parameters are obtained by optimization algorithms. The comparative analysis of the controller performance is carried out through simulation in MATLAB platform to validate the effectiveness in the controller design under various changing situations. Conclusion: The optimized controller parameters obtained through ABC algorithm are better than that obtained through PSO algorithm in terms of both objective function values and execution time. The resultant robust control strategy for IC system obtained through H-infinity loop shaping controller provides reduced tracking error and improved stability as compared to PID and FOPID controller, as proved by the simulation results.

scholarly journals Fuzzy gain scheduling control apply to an RC Hovercraft

2020 ◽  
Vol 10 (3) ◽  
pp. 2434
Author(s):  
Huu Khoa Tran ◽  
Pham Duc Lam ◽  
Tran Thanh Trang ◽  
Xuan Tien Nguyen ◽  
Hoang-Nam Nguyen
Keyword(s):  
Controller Design ◽  
Tracking Error ◽  
Operation Time ◽  
Absolute Error ◽  
Gain Scheduling ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Motion Models ◽  
Complex Models ◽  
On Line

The Fuzzy Gain Scheduling (FGS) methodology for tuning the Proportional – Integral – Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional – Integral – Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller.

scholarly journals Optimization of the PID-PD parameters of the overhead crane control system by using PSO algorithm

2019 ◽  
Vol 255 ◽  
pp. 04001 ◽  
Author(s):  
Nur Iffah Mohamed Azmi ◽  
Nafrizuan Mat Yahya ◽  
Ho Jun Fu ◽  
Wan Azhar Wan Yusoff
Keyword(s):  
Pso Algorithm ◽  
Performance Criterion ◽  
Performance Criteria ◽  
Step Response ◽  
Pd Controller ◽  
Overhead Crane ◽  
Proportional Integral Derivative ◽  
Criterion Function ◽  
Proportional Integral ◽  
Load Swing

The development of combination of proportional-integral-derivative and proportional- derivative (PID-PD) controller for overhead crane is presented. Due to the pendulum-like settings, the swinging of load has caused many difficulties while operating the overhead crane. Swinging of the load causes unnecessary tension to the cable and structure of the overhead crane, which will compromise the safety of operator and other workers. Overhead cranes should have the ability to move the load to desired point as fast as possible while minimizing the load swing and maintaining the accuracy. Proportional-integral-derivative (PID) controller is used for overhead crane positioning and proportional-derivative (PD) controller for load oscillation. New time-domain performance criterion function is used in particle swarm optimization (PSO) algorithm for the tuning of the PID-PD controller rather than the general performance criteria using error of the system. This performance criterion function monitors the performance in terms of rise time, overshoot, settling time and steady state error of the overhead crane system. The performance of the optimised PID-PD controller is verified with simulation in MATLAB. The PSO optimized PID-PD controllers with new performance criterion are shown effective in improving the step response of the overhead crane position as well as controlled the load oscillation.

scholarly journals Particle Swarm Optimization

2017 ◽  
pp. 614-635 ◽  
Author(s):  
Alaa Tharwat ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
Basem E. Elnaghi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Search Space ◽  
Learning Problems ◽  
Swarm Optimization ◽  
Numerical Example

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

Online force control of a shape-memory-alloy-based 2 degree-of-freedom human finger via inverse model and proportional–integral–derivative compensator

2019 ◽  
Vol 30 (10) ◽  
pp. 1538-1548 ◽  
Author(s):  
F Mirzakhani ◽  
SM Ayati ◽  
P Fahimi ◽  
M Baghani
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Tracking Error ◽  
Reference Signal ◽  
Experimental Tests ◽  
Degree Of Freedom ◽  
Heat Transfer Analysis ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Arbitrary Reference

In this work, a model-based controller is developed to track the force at fingertip of an artificial hand. To do so, shape-memory-alloy wires are implemented as an actuator in the finger. Besides, different aspects of modeling, including force relations, kinematics, and heat transfer analysis, are investigated. A modified version of Brinson’s model is used to capture thermomechanical behavior of shape-memory-alloy wires. A controller is designed to control the applied potential difference between shape-memory-alloy wires and consequently control the electrical current in these wires based on the shape-memory-alloy wires model. The main goal of the proposed controller is force controlling of a 2-degree-of-freedom hand finger. This controller contains shape-memory-alloy constitutive model used for compensating system uncertainties. Furthermore, a proportional–integral–derivative controller/compensator is included in the closed-loop system. The compensator acts only on the derivative-type states, and this is one of the differences of this work compared to that of similar literature. The results of three arbitrary reference input signals are reported confirming the model prediction and simulation results are in good agreement with experimental tests. The analysis of the relative tracking error for an arbitrary reference signal is 11% in experimental test and 4% in the simulation.

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