Gain tuning of position domain PID control using particle swarm optimization

Pano, V.; Ouyang, P. R.

doi:10.1017/s0263574714002331

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…Although the accuracy and generalization ability of the model were improved, the evolutionary algorithm used in this high-dimensional optimization problem generally has the drawbacks of fast convergence and is easy to converge into local optimization. The Kalman particle swarm optimization (KPSO) [17] combines the Kalman filter principle into the PSO [18], which reduces the number of iterations for the algorithm to find the global optimum in solving high-dimensional optimization problems. After combining the principle of the Kalman filter, the optimization ability of PSO has been improved, and it has been further improved and applied to practical engineering [19].…”

Section: Introductionmentioning

confidence: 99%

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

et al. 2022

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With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.

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Section: Introductionmentioning

confidence: 99%

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

et al. 2022

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“…The matrix composed of features of two channels is a 10-dimensional matrix, and there are interference items or redundancy items in the matrix, so it is necessary to optimize the characteristic components. Particle swarm optimization (PSO) [30, 31] is a parallel global search strategy based on the population. Its advantages are less adjustment parameters, fast convergence speed, wide application range, and optimization in a high-dimensional space.…”

Section: Methodsmentioning

confidence: 99%

Online pattern recognition of lower limb movements based on sEMG signals and its application in real-time rehabilitation training

Ye,

Zhu,

et al. 2023

Robotica

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An online pattern recognition method of lower limb movements is proposed based on the personalized surface electromyography (sEMG) signals, and the corresponding experimental researches are performed in the rehabilitation training. Further, a wireless wearable acquisition instrument is used. Based on this instrument, a host computer for the personal online recognition and real-time control of rehabilitation training is developed. Three time-domain features and two features in the nonlinear dynamics are selected as the joint set of the characteristic values for the sEMG signals. Then a particle swarm optimization (PSO) algorithm is used to optimize the feature channels, and a k-nearest neighbor (KNN) algorithm and the extreme learning machine (ELM) algorithm are combined to classify and recognize individual sample data. Based on the multi-pose lower limb rehabilitation robot, the real-time motion recognition and the corresponding rehabilitation training are carried out by using the online personalized classifier. The experimental results of eight subjects indicate that it takes only 6 min to build an online personalized classifier for the four types of the lower limb movements. The recognition between switches of different rehabilitation training movements is timely and accurate, with an average recognition accuracy of more than 95%. These results demonstrate that this system has a strong practicability.

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“…In the studies so far conducted on PSO-based controller, different types fitness functions such as Integral of Squared-Error (ISE), Integral of Absolute Error (IAE), Integral of Time-Weighted Squared-Error (ITSE) and Integral of Time-Weighted Absolute Error (ITAE) have been proposed to realize the update process depending on the state of each particle [25,26]. In this study, an ISE-based criterion given in Eq.…”

Section: Control Of the Energy Conversion Systemmentioning

confidence: 99%

PSO-based PID controller design for an energy conversion system using compressed air

Atacak

Küçük

2017

Teh. vjesn.

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Original scientific paper In this study, an optimal control algorithm is proposed to overcome low performance problems arising from the non-linear characteristics of pneumatic motor in compressed air-based energy conversion systems. The effectiveness of the proposed algorithm is tested on an energy conversion system which includes a compressor, a proportional valve, a pneumatic motor (PM), a permanent magnet direct current (PMDC) generator and a control card. The control function of the system is carried out by driving the proportional valve with the control signals which is obtained depending on the PMDC generator output voltage error. In this structure, an optimal proportional-integral-derivative (PID) controller which tunes on-line its own gain parameters by particle swarm optimization (PSO) algorithm according to the operating conditions of the system used. In order to observe the effects of PSO-based PID controller on the system performance, the energy conversion system is also controlled by a discrete time PID controller. The experimental results show that PSO-based PID controller provides more robust control performance than discrete time PID controller under various operating conditions. Keywords: compressed air-based energy conversion; optimal PID controller; particle swarm optimization algorithm; pneumatic motor Konstrukcija PID regulatora zasnovanog na PSO za sustav pretvorbe energije pomoću stlačenog zraka Izvorni znanstveni članak U ovom se radu predlaže optimalni kontrolni algoritam za rješavanje problema niske performanse zbog nelinearnih značajki pneumatskog motora u sustavima za pretvorbu energije pomoću stlačenog zraka. Učinkovitost predloženog algoritma se ispituje na sustavu za pretvorbu energije koji uključuje kompresor, proporcionalni ventil, pneumatski motor (PM), generator istosmjerne struje s trajnim magnetom (PMDC) i kontrolnu karticu. Kontrolna funkcija sustava provodi se pogonjenjem proporcionalnog ventila s kontrolnim signalima što se postiže ovisno o greški napona na izlazu PMDC generatora. U toj konstrukciji, optimalni proporcionalni-integralni-derivativni (PID) regulator izravno podešava vlastite parametre pojačanja algoritmom optimizacije roja čestica -particle swarm optimization (PSO) u skladu s radnim uvjetima primijenjenog sustava. U svrhu promatranja učinaka PIDregulatora zasnovanog na PSO na rad sustava, sustav za pretvorbu energije se kontrolira PID regulatorom diskretnog vremena. Eksperimentalni rezultati pokazuju da PID regulator zasnovan na PSO osigurava robustniju regulaciju rada nego PID regulator diskretnog vremena kod različitih radnih uvjeta.Ključne riječi: algoritam optimizacije roja čestica; optimalni PID regulator; pneumatski motor; pretvorba energije pomoću stlačenog zraka

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Gain tuning of position domain PID control using particle swarm optimization

Cited by 11 publications

References 22 publications

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

Online pattern recognition of lower limb movements based on sEMG signals and its application in real-time rehabilitation training

PSO-based PID controller design for an energy conversion system using compressed air

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