scholarly journals A New Method for Identifying Kinetic Parameters of Industrial Robots

Actuators ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 2
Author(s):  
Bin Kou ◽  
Shijie Guo ◽  
Dongcheng Ren
Keyword(s):  
Simulated Annealing ◽  
Kinetic Parameters ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Search Pattern ◽  
Dynamic Parameters ◽  
Swarm Optimization

Identifying the kinetic parameters of an industrial robot is the basis for designing a controller for it. To solve the problems of the poor accuracy and easy premature convergence of common bionic algorithms for identifying the dynamic parameters of such robots, this study proposed simulated annealing with similar exponential changes based on the beetle swarm optimization (SEDSABSO) algorithm. Expressions for the dynamics of the industrial robot were first obtained through the SymPyBotics toolkit in Python, and the required trajectories of excitation were then designed to identify its dynamic parameters. Following this, the search pattern of the global optimal solution for the beetle swarm optimization algorithm was improved in the context of solving for these parameters. The global convergence of the algorithm was improved by improving the iterative form of the number N of skinks in it by considering random perturbations and the simulated annealing algorithm, whereas its accuracy of convergence was improved through the class exponential change model. The improved beetle swarm optimization algorithm was used to identify the kinetic parameters of the Zhichang Kawasaki RS010N industrial robot. The results of experiments showed that the proposed algorithm was fast and highly accurate in identifying the kinetic parameters of the industrial robot.

scholarly journals Research on Microgrid Optimization Based on Simulated Annealing Particle Swarm Optimization

2019 ◽  
Vol 118 ◽  
pp. 01038
Author(s):  
Shuyi Li ◽  
Xifeng Zhou ◽  
Qiangang Guo
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Storage Device ◽  
Swarm Optimization

Based on the pursuit of different goals in the operation of the microgrid, it is not possible to meet the lowest cost and the least pollution at the same time. From the perspective of economy and environmental protection, a microgrid model including photovoltaic power generation, wind power generation, micro gas turbine, fuel cell and energy storage device is proposed. This paper establishes a comprehensive benefit objective function that considers both microgrid fuel cost, maintenance management cost, depreciation cost, interaction cost with public grid and pollutant treatment cost. In order to avoid the defect that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper uses the combination of simulated annealing algorithm and particle swarm optimization algorithm to compare with the traditional particle swarm optimization algorithm to obtain a more suitable method for microgrid operation. Finally, a typical microgrid in China is taken as an example to verify the feasibility of the algorithm.

Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm

Robotica ◽  
2022 ◽  
pp. 1-16
Author(s):  
Peng Zhang ◽  
Junxia Zhang
Keyword(s):  
Optimization Algorithm ◽  
Robot Control ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Identification Accuracy ◽  
Dynamic Parameters ◽  
Dynamics Modeling ◽  
Swarm Optimization ◽  
Exoskeleton Robot

Abstract Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

scholarly journals Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Correlation Algorithm ◽  
Annealing Algorithm

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

Research on the Initial Value of the Simulated Annealing

2013 ◽  
Vol 774-776 ◽  
pp. 1770-1773
Author(s):  
Xiao Wen Liang ◽  
Wei Gong ◽  
Wen Long Fu ◽  
Jing Qi
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Hill Climbing ◽  
Local Optimum ◽  
Initial Value ◽  
Good Convergence ◽  
Annealing Algorithm

Simulated Annealing Algorithm is one of the top ten classical optimization algorithm, and it has been successfully applied to various fields. Simulated annealing is a optimization algorithm which can find the global optimal solution, compares to neural network algorithm, it is so easily to implement that has higher probability to be adopted, but it has own shortcomings like other optimization algorithms, its result largely depends on initial value, The initial value of the traditional simulated annealing algorithm began with a random number, its convergence speed is often slow very much and the effect is bad. In this paper, a new simulated annealing algorithm that based on genetic algorithm and hill-climbing method was brought up, because of hill-climbing algorithm was easy to fall into local optimum, and simulated annealing can just solve the problem, it not only escaped from local optimum, but also got good convergence speed and results.

Research of Improved Simulated Annealing Optimization Algorithm Based on the Global Harmony Search Mechanism

2012 ◽  
Vol 482-484 ◽  
pp. 2500-2503
Author(s):  
Jin Hua Zhang
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Computational Efficiency ◽  
Simulated Annealing Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Computational Accuracy ◽  
Search Mechanism ◽  
Annealing Algorithm ◽  
Conventional Optimization

The traditional simulated annealing algorithm adopts serial optimization structure, and it has the defects of slow annealing process, low computational efficiency and so on. On the basis of the traditional simulated annealing algorithm, the improved simulated annealing optimization algorithm with the global harmony search Mechanism is put forward, which can quicken convergence rate and raise search efficiency at the same time of ensuring algorithm to fall in the global optimal solution. In this paper, by using conventional optimization algorithm, traditional simulated annealing algorithm and the improved optimization algorithm to optimize the famous global optimization function, the results show that the improved optimization algorithm has the advantages of high computational efficiency, high computational accuracy, etc.

scholarly journals Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

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