A data-driven PID control system using particle swarm optimisation

2011 ◽  
Vol 13 (1/2) ◽  
pp. 88 ◽  
Author(s):  
Makoto Tokuda ◽  
Toru Yamamoto
Keyword(s):  
Control System ◽  
Pid Control ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Data Driven

Multi-objective particle swarm optimisation approach for production-inventory control systems

2018 ◽  
Vol 13 (4) ◽  
pp. 1037-1056 ◽  
Author(s):  
Huthaifa AL-Khazraji ◽  
Colin Cole ◽  
William Guo
Keyword(s):  
Control System ◽  
Control Systems ◽  
Inventory Control ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Inventory Level ◽  
Content Type ◽  
Multi Objective ◽  
Production Inventory

Purpose This paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO). Design/method/approach The production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation. Findings The results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions. Research limitations/implications This research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level. Originality/value PSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

Novel morphing wing actuator control-based Particle Swarm Optimisation

2019 ◽  
Vol 124 (1271) ◽  
pp. 55-75 ◽  
Author(s):  
S. Khan ◽  
T. L. Grigorie ◽  
R. M. Botez ◽  
M. Mamou ◽  
Y. Mébarki
Keyword(s):  
Control System ◽  
Wind Tunnel ◽  
Experimental Model ◽  
Experimental Testing ◽  
Pressure Sensors ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Morphing Wing ◽  
Software Model ◽  
Actuation System

AbstractThe paper presents the design and experimental testing of the control system used in a new morphing wing application with a full-scaled portion of a real wing. The morphing actuation system uses four similar miniature brushless DC (BLDC) motors placed inside the wing, which execute a direct actuation of the flexible upper surface of the wing made from composite materials. The control system of each actuator uses three control loops (current, speed and position) characterised by five control gains. To tune the control gains, the Particle Swarm Optimisation (PSO) method is used. The application of the PSO method supposed the development of a MATLAB/Simulink® software model for the controlled actuator, which worked together with a software sub-routine implementing the PSO algorithm to find the best values for the five control gains that minimise the cost function. Once the best values of the control gains are established, the software model of the controlled actuator is numerically simulated in order to evaluate the quality of the obtained control system. Finally, the designed control system is experimentally validated in bench tests and wind-tunnel tests for all four miniature actuators integrated in the morphing wing experimental model. The wind-tunnel testing treats the system as a whole and includes, besides the evaluation of the controlled actuation system, the testing of the integrated morphing wing experimental model and the evaluation of the aerodynamic benefits brought by the morphing technology on this project. From this last perspective, the airflow on the morphing upper surface of the experimental model is monitored by using various techniques based on pressure data collection with Kulite pressure sensors or on infrared thermography camera visualisations.

Design of Fuzzy PID Controller Based on Adaptive Particle Swarm Optimization with Cloud Theory

2013 ◽  
Vol 706-708 ◽  
pp. 720-723
Author(s):  
Ming Feng Ying ◽  
Li Xin Zai ◽  
Hai Xiang Wang
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Excitation Control ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Cloud Theory

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

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