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PSO-based nonlinear predictive control for unmanned bicycle robot stabilization

100%

Ziętkiewicz J.

Studia z Automatyki i Informatyki

2017

tom T. 42

87--96

The paper considers vertical stabilization of an unmanned bicycle-like robot. Nonlinear predictive control is utilized for the purpose; at every step optimization of a nonlinear cost function using particle swarm optimization is performed. This allows to find optimal global solution or solution close to this optimum, depending on the employed time, even for nonconvex functions. Simulations show that this approach to model predictive control can provide satisfactory results.

W artykule rozwiązany jest problem stabilizacji robota - bezzałogowego roweru. W tym celu stosowane jest nieliniowe sterowanie predkcyjne z zastosowaniem optymalizacji opartej na roju cząsteczek (Particle Swarm Optimization, PSO), w każdym kroku algorytmu predykcji. Podejście to pozwala na znajdowanie minimum globalnego lub bliskiego mu rozwiązania, nawet dla niewypukłych funkcji. Symulacje pokazują, że takie podejście do nieliniowej regulacji predykcyjnej pozwala otrzymać satysfakcjonujące wyniki.

Study on forward and inversion modeling of array laterolog logging in a horizontal/highly deviated well

84%

Zhu P. , Li Z. , Chen M. , Dong Y.

Acta Geophysica

2019

tom Vol. 67, no. 5

1307--1318

Electric feld synthesis was carried out using the multi-feld superposition method according to the working principle of the array laterolog electrode system. The feld distribution of each subfeld was simulated with the 3D finite element method, and the laterolog response of the array was obtained using the linear superposition principle of electric feld. The detection depth and thin layer response at diferent angles of the array laterolog were analyzed. The forward response calculation shows that the radial detection depth of the array laterolog is smaller than the deep laterolog detection depth. When the inclination angle of the well is less than 15°, the logging response of the array laterolog is less afected by the well inclination, and the well inclination correction need not be performed. The logging response values of highly deviated wells with inclination angles exceeding 60° and horizontal wells are quite diferent from those of vertical wells; thus, well deviation correction must be performed. To improve the stability of array laterolog logging inversion using the accurate forward response, a Newton–singular value decomposition method based on particle swarm optimization is proposed to realize inversion of array laterolog logging, and the stability and reliability of logging inversion are greatly improved. Thus, application of the theoretical model and actual data processing and analysis show that the proposed method can efectively and accurately eliminate the infuence of a complex logging environment and obtain real formation parameters.

Allocation of real power generation based on computing over all generation cost: an approach of Salp Swarm Algorithm

67%

Devarapalli R. , Sinha N. K. , Rao B. V. , Knypiński Ł. , Lakshmi N. J. N. , García Márquez F. P.

Archives of Electrical Engineering

2021

tom Vol. 70, nr 2

337--349

Economic Load Dispatch (ELD) is utilized in finding the optimal combination of the real power generation that minimizes total generation cost, yet satisfying all equality and inequality constraints. It plays a significant role in planning and operating power systems with several generating stations. For simplicity, the cost function of each generating unit has been approximated by a single quadratic function. ELD is a subproblem of unit commitment and a nonlinear optimization problem. Many soft computing optimization methods have been developed in the recent past to solve ELD problems. In this paper, the most recently developed population-based optimization called the Salp Swarm Algorithm (SSA) has been utilized to solve the ELD problem. The results for the ELD problem have been verified by applying it to a standard 6-generator system with and without due consideration of transmission losses. The finally obtained results using the SSA are compared to that with the Particle Swarm Optimization (PSO) algorithm. It has been observed that the obtained results using the SSA are quite encouraging.