scholarly journals Continuous Control Strategy of Planar 3-Linkage Underactuated Manipulator Based on Broad Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 249
Author(s):  
Siyu Chen ◽  
Yawu Wang ◽  
Pan Zhang ◽  
Chun-Yi Su
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Control Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Position Control ◽  
Target Position ◽  
Continuous Control ◽  
Swarm Optimization ◽  
Coupling Relationship ◽  
Underactuated Manipulator

For the position control of a planar 3-linkage underactuated manipulator (PTUM) with a passive first linkage, a continuous control strategy is developed in this paper. In particular, a broad neural network (BNN)-based model is first established to accurately describe the motion coupling relationship between the passive linkage and the second linkage. Based on this model, by using the particle swarm optimization algorithm, the target angles of all linkages are calculated combining the start states of all linkages and the target position of the PTUM. Then, the target angles of the active linkages are directly achieved by their respective actuators, and that of the passive linkage is also achieved by the rotation of the second linkage. By carrying out several experiments, the effectiveness of the above strategy is verified.

scholarly journals Position Control of Ultrasonic Motor Using IMC-PID Combined with Tribes Type NN Algorithm

2011 ◽  
Vol 2-3 ◽  
pp. 12-17
Author(s):  
Sheng Lin Mu ◽  
Kanya Tanaka
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Pid Control ◽  
Position Control ◽  
Back Propagation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Ultrasonic Motor ◽  
Swarm Optimization

In this paper, we propose a novel scheme of IMC-PID control combined with a tribes type neural network (NN) for the position control of ultrasonic motor (USM). In this method, the NN controller is employed for tuning the parameter in IMC-PID control. The weights of NN are designed to be updated by the tribes-particle swarm optimization (PSO) algorithm. This method makes it possible to compensate for the characteristic changes and nonlinearity of USM. The parameter-free tribes-PSO requires no information about the USM beforehand; hence its application overcomes the problem of Jacobian estimation in the conventional back propagation (BP) method of NN. The effectiveness of the proposed method is confirmed by experiments.

Using Particle Swarm Optimization Algorithm as an Optimization Tool Within Developed Neural Networks

2018 ◽  
pp. 215-244
Author(s):  
Goran Klepac
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Algorithm ◽  
Recommendation System ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Combination ◽  
Swarm Optimization

Developed neural networks as an output could have numerous potential outputs caused by numerous combinations of input values. When we are in position to find optimal combination of input values for achieving specific output value within neural network model it is not a trivial task. This request comes from profiling purposes if, for example, neural network gives information of specific profile regarding input or recommendation system realized by neural networks, etc. Utilizing evolutionary algorithms like particle swarm optimization algorithm, which will be illustrated in this chapter, can solve these problems.

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