scholarly journals Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4177
Author(s):  
Ye Li ◽  
Dazhi Wang ◽  
Shuai Zhou ◽  
Xian Wang
Keyword(s):  
Parameter Identification ◽  
Motion Control ◽  
Integration Method ◽  
Classical Method ◽  
Search Algorithm ◽  
Probabilistic Neural Network ◽  
Gravitational Search Algorithm ◽  
Optimization Approach ◽  
Parameter Mismatch ◽  
Servo Controller

With the rise of smart robots in the field of industrial automation, the motion control theory of the robot servo controller has become a research hotspot. The parameter mismatch of the controller will reduce the efficiency of the equipment and damage the equipment in serious cases. Compared to other parameters of servo controllers, the moment of inertia and friction viscous coefficient have a significant effect on the dynamic performance in motion control; furthermore, accurate real-time identification is essential for servo controller design. An improved integration method is proposed that increases the sampling period by redefining the update condition in this paper; it then expands the applied range of the classical method that is more suitable for the working characteristics of a robot servo controller and reducesthe speed quantization error generated by the encoder. Then, an optimization approach using the incremental probabilistic neural network with improved Gravitational Search Algorithm (IGSA-IPNN) is proposed to filter the speed error by a nonlinear process and provide more precise input for parameter identification. The identified inertia and friction coefficient areused for the PI parameter self-tuning of the speed loop. The experiments prove that the validity of the proposed method and, compared to the classical method, it is more accurate, stable and suitable for the robot servo controller.

scholarly journals Accurate battery model parameter identification using heuristic optimization

2020 ◽  
Vol 11 (1) ◽  
pp. 333 ◽  
Author(s):  
Mohd Afifi Jusoh ◽  
Muhamad Zalani Daud
Keyword(s):  
Human Error ◽  
Classical Method ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Lithium Ion ◽  
Heuristic Optimization ◽  
Model Parameters ◽  
Optimization Approach ◽  
Simulation Studies ◽  
Battery Model

<span lang="EN-MY">This paper presents an accurate Lithium-ion battery model representation in Matlab/Simulink. The Tremblay's battery model was used as a BES model platform, where the determination of the model parameters was obtained based on heuristic optimization approach. This approach is simple but more accurate compared to the conventional method. In the classical method, it requires the user to manually select the battery model parameters from relevant points on the manufacturer discharge curves. However, this way of battery parameters extraction normally exposed to the human error and would easily result in an inaccurate selection of battery parameters for the BES simulation studies. Therefore, an easy and accurate approach using heuristic optimization for determining battery model parameters was introduced. The simulation studies utilized three different optimization algorithms for comparison purposes, i.e. 1) Particle Swarm Optimization (PSO), 2) Gravitational Search Algorithm (GSA), and 3) Genetic Algorithm (GA). The performance of BES model discharge accuracy with respect to the test data from three different algorithms was compared and the results showed that the GA approach gives the best results in terms of accuracy and execution time. </span><span lang="EN-MY">Finally, the validated results of GA-optimized battery model showed the accuracy of 98% compared to the conventional approach.</span>

scholarly journals Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Intissar Khoja ◽  
Taoufik Ladhari ◽  
Anis Sakly ◽  
Faouzi M’sahli
Keyword(s):  
Wastewater Treatment ◽  
Particle Swarm Optimization ◽  
Activated Sludge ◽  
Parameter Identification ◽  
Treatment Process ◽  
Classical Method ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Wastewater Treatment Process ◽  
Swarm Optimization

The current paper is entirely devoted to show the applicability of Particle Swarm Optimization (PSO) algorithm as a parameter identification method for a representative model of an Activated Sludge Wastewater Treatment Process (ASWWTP) with alternating phases. The model of identification is composed of two linear submodels: one for the aerobic phase and the other for the anoxic phase. In order to prove the efficiency of the proposed method, its performance is compared with another classical method called Simplex Search Algorithm (SSA) as well as with the experimental data.

A novel optimization-based method to find multiple solutions for path synthesis of planar four-bar and slider-crank mechanisms

2021 ◽  
pp. 095440622098336
Author(s):  
Soheil Zarkandi
Keyword(s):  
Feasible Solution ◽  
Heuristic Algorithms ◽  
Classical Method ◽  
Search Algorithm ◽  
Error Function ◽  
Continuation Method ◽  
Gravitational Search Algorithm ◽  
Planar Mechanisms ◽  
All Solutions ◽  
Path Synthesis

The classical method to find possible solutions for path synthesis problem of planar mechanisms is continuation method. However, this method has some disadvantages such as producing unwanted extraneous and degenerate solutions and also producing mechanisms having defects. Moreover, many of the solutions are cognate of each other which can be obtained geometrically. Thus, finding the most feasible solution among all solutions is a cumbersome and time-consuming task. The main purpose of this paper is to explore applicability of heuristic algorithms to find multiple cognate- and defect-free solutions for path synthesis problems of planar four-bar and slider-crank mechanisms. To this aim, a new modified error function and an optimization-based algorithm is presented. The gravitational search algorithm (GSA) is utilized to minimize the modified error function. Efficiency of the method is proved through five case studies for path synthesis of four-bar and slider-crank mechanisms with and without prescribed timing.

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