2020
DOI: 10.3390/app10238472
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Using Simplified Swarm Optimization on Multiloop Fuzzy PID Controller Tuning Design for Flow and Temperature Control System

Abstract: This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is interactive. In this work, we use five methods to design and compare the PID controllers in MATLAB and Simulink, including Ziegler–Nicolas PID tuning, particle swarm optimization (PS… Show more

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Cited by 23 publications
(13 citation statements)
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“…The local best of all particles is compared and the best position in the swarm is defined as global best. At every iteration, the termination criteria are checked, and if not met, an update of the velocity and position of all particles is done [ 36 , 48 ]. The velocity of particles is given in (11) , while the position of individual particles is updated by (12) , where, is the particle position, is the particle velocity, is the individual particle best position, is swarm best position, is the weight inertia used to ensure convergence, and are the cognitive and social parameters while and are random numbers between 0 and 1 [ 36 ] [ 49 ].…”
Section: Statcom Operation and Controlmentioning
confidence: 99%
“…The local best of all particles is compared and the best position in the swarm is defined as global best. At every iteration, the termination criteria are checked, and if not met, an update of the velocity and position of all particles is done [ 36 , 48 ]. The velocity of particles is given in (11) , while the position of individual particles is updated by (12) , where, is the particle position, is the particle velocity, is the individual particle best position, is swarm best position, is the weight inertia used to ensure convergence, and are the cognitive and social parameters while and are random numbers between 0 and 1 [ 36 ] [ 49 ].…”
Section: Statcom Operation and Controlmentioning
confidence: 99%
“…e local best of all particles is compared and the best position in the swarm is defined as the global best. At every iteration, the termination criteria are checked, and if not met, an update of the velocity and position of all particles is done [44]. e velocity of particles is given by…”
Section: Particle Swarm Optimization (Pso) Particle Swarmmentioning
confidence: 99%
“…P ON Total = P NSA ON + P SA ON + P additional ON (16) where P additional ON is the additional appliances' power consumption. If P ON Total > E max or P ON Total > E max , the additional appliance that the user desires to operate is not feasible to run.…”
Section: P Onmentioning
confidence: 99%
“…In this regard, the authors of [14,15] have developed scheduling algorithms based on consumption cost reduction and consumer preference to manage residential appliances, which achieve the desired trade-off between economic benefits and consumer preference. Similarly, machine learning techniques, linear and dynamic programming, particle swarm optimization (PSO), fuzzy methods, and game theory are among the optimization techniques used in home energy management systems to schedule and control home appliances to provide economic benefits to consumers [16][17][18][19][20][21]. However, consumers are still not able to attain both user satisfaction and cost savings together, which are the drawbacks of the existing DR programs for DSM.…”
Section: Introductionmentioning
confidence: 99%