The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design

Controlling continuum robots with precision is particularly a challenging task due to the complexity of their mathematical models and inaccuracies in modeling approaches. Therefore, most advanced control schemes have shown poor performances, especially in trajectory tracking accuracy. This paper presents a proposed Nonlinear Model Predictive Control (NMPC) scheme to solve the trajectory tracking of a class of continuum robots, namely Cable-Driven Continuum Robot (CDCR). However, since NMPC schemes are often limited by the computational burden associated with the optimization algorithms to be solved at each sampling time, the Particle Swarm Optimization (PSO) algorithm is used to solve the arising optimization problem NMPC, thanks to its simplicity and fast convergence. The proposed NMPC-PSO scheme is applied to the developed kinematic and dynamic models of the considered CDCR. Based on the kinematic and dynamic model, the two proposed controllers have been validated against numerical simulations of two-dimensional CDCR with two bending sections for set-point stabilization and point-to-point trajectory tracking. For both controllers, the performance of tracking accuracy and computation time is analyzed and compared. Moreover, the obtained simulation results are compared to the available literature works. In view of the results obtained on the considered CDCR, the proposed NMPC-PSO scheme can track in real-time the desired trajectory with high accuracy and much less execution time than other advanced control schemes, which makes it an alternative for real-time applications.

Section: Optimization Methodsmentioning

confidence: 99%

Nonlinear model predictive control of a class of continuum robots using kinematic and dynamic models

Amouri¹,

Cherfia²,

Merabti³

et al. 2022

“…The blade vibration under flatwise, edgewise, torsional, and extensional deformations were studied, and the first ten modes have been identified for stationary and rotating blade conditions. Wind farm layout design was optimized using particle swarm optimization was examined in [17]. It is found that, the acceleration coefficients impact the final layout quality, leading to better overall energy output.…”

Section: Introductionmentioning

confidence: 99%

Aeroelastic analysis of straight-bladed vertical axis wind turbine blade

Fadlalla¹,

Şahin²,

Ouakad³

2022

To prevent flutter phenomena in a wind turbine, minimize vibration and increase the blades' life, a systematic analysis is required to investigate the effects between the cyclic aerodynamic loads and the structural performance of the turbine. A dynamic analysis of a straight-bladed vertical axis wind turbine (SB-VAWT) blade is investigated in this paper, and a simplified approach for the energy equations of an Eulerian beam subjected to twist and transverse bending deflections is introduced. The aerodynamic loads are estimated using the double multiple stream tube models. They are introduced into the dynamic model in the aeroelastic coupling, where the structural displacements are fed back to update the aerodynamic loads by utilizing the average acceleration method for the numerical integration of the equations. Reduced order modeling is then imposed based on the first modes of vibration. It is found that the structural displacement has little effect on the aerodynamic loads, and SBVAWTs experience higher transverse displacements compared with those in curved-blade VAWTs.

“…As one of the modern optimization approaches, meta-heuristic algorithms are exploited to solve nonconvex, nonlinear, and multimodal problems with linear or nonlinear constraints and continuous or discrete vari-ables. For example, the Particle Swarm Optimization Algorithm has been effectively used to solve the wind farm layout design problem [11], or the Dingo Optimi-zation Algorithm for solving continuous engineering problems [12]. Further, an optimal load frequency control is optimally designed using the African Vultures Optimization Algorithm (AVOA) [12].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy controller optimized by the African vultures algorithm for trajectory tracking of a two-link gripping mechanism

Jovanović¹,

Bugarić²,

Vesović³

et al. 2022

This paper presents the proportional-derivative fuzzy controller for trajectory tracking of the gripping mechanism with two degrees of freedom. Aiming to achieve movement of the gripping mechanism without sudden starting and stopping, a polynomial velocity profile is utilized. The African vultures optimization, as one of the latest metaheuristic algorithms, is used to obtain the optimal input/output scaling gains of the proposed fuzzy controller according to the selected fitness function. The results obtained by this algorithm are compared with the other three new and popular metaheuristic algorithms: the whale optimization, the ant lion optimization and the sine cosine algorithm. Moreover, a simulation study was done for the defined initial position and for the scenario where there is a certain deviation because the gripping mechanism is not at its original initial position. Finally, the robustness of the controller is tested for the case when the masses of the segments increase three times. The results revealed that the suggested controller was capable of dealing with nonlinearities of the gripping mechanism, initial position and parameter changes. The movement of the gripping mechanism is smooth and follows the defined trajectory.