2021
DOI: 10.22266/ijies2021.0228.16
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An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Abstract: The diverse engineering and scientific applications are stated through complex and high-order systems. The significant difficulties of these systems are the complications of modeling, analyzing, and controlling. It is easier to examine simpler models for more physical insights than more complex models and result in lower-ordering controllers that are easier to implement. The model order reduction (MOR) was used to simplify the computational difficulty of such complications and was later developed intensively f… Show more

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Cited by 13 publications
(6 citation statements)
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“…To locally improve the optimal solution of PSO, CPSO (chaotic particle swarm optimization) algorithm is introduced to enhance convergence accuracy and speed. The chaotic sequences of CPSO are generated by iteration, and the range of chaotic variables is corresponded to the value space of optimization variables by carrier mode [ 32 ]. Applicable to a variety of complex optimization problems and combinatorial optimization problems, the chaotic series are used to initialize the position and velocity of the particles.…”
Section: Itcs Depth-controller Optimization Methods Designmentioning
confidence: 99%
“…To locally improve the optimal solution of PSO, CPSO (chaotic particle swarm optimization) algorithm is introduced to enhance convergence accuracy and speed. The chaotic sequences of CPSO are generated by iteration, and the range of chaotic variables is corresponded to the value space of optimization variables by carrier mode [ 32 ]. Applicable to a variety of complex optimization problems and combinatorial optimization problems, the chaotic series are used to initialize the position and velocity of the particles.…”
Section: Itcs Depth-controller Optimization Methods Designmentioning
confidence: 99%
“…The advantages of the PSO algorithm are (1) fast convergent speed, (2) few control parameters, (3) certain parallelism. On the other hand, the disadvantage of this method is simple to place down into local optimal value [32].…”
Section: Pso Optimizationmentioning
confidence: 99%
“…The first flight path-finding algorithm uses CPSO algorithm, in which the chaos method is incorporated into the PSO algorithm to generate more randomness in the search for the PSO algorithm and to solve the local minima problem [25]. Therefore, the particle updates its velocity and position equations as can be expressed below [25,26]:…”
Section: Flight-path Planning Algorithmsmentioning
confidence: 99%