The Walking Robot Control System that is Adaptive to Changes in the Kinematics

2021 ◽  
Vol 22 (11) ◽  
pp. 601-609
Author(s):  
A. S. Samoylova ◽  
S. A. Vorotnikov
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Robot Control ◽  
Control Algorithm ◽  
Human Life ◽  
Support Surface ◽  
Machine Modeling ◽  
Cargo Delivery ◽  
Formal Classification

The walking mobile robots (WMR) have recently become widely popular in robotics. They are especially useful in the extreme cases: search and rescue operations; cargo delivery over highly rough terrain; building a map. These robots also serve to explore and describe a partially or completely non-deterministic workspace, as well as to explore areas that are dangerous to human life. One of the main requirements for these WMR is the robustness of its control system. It allows WMR to maintain the operability when the characteristics of the support surface change as well as under more severe conditions, in particular, loss of controllability or damage of the supporting limb (SL). We propose to use the principles of genetic programming to create a WMR control system that allows a robot to adapt to possible changes in its kinematics, as well as to the characteristics of the support surface on which it moves. This approach does not require strong computational power or a strict formal classification of possible damage to the WMR. This article discusses two main WMR control modes: standard, which accord to a serviceable kinematics, and emergency, in which one or more SL drives are damaged or lost controllability. As an example, the structure of the control system of the WMP is proposed, the kinematics of which is partially destroyed in the process of movement. We developed a method for controlling such robot, which is based on the use of a genetic algorithm in conjunction with the Mealy machine. Modeling of modes of movement of WMR with six SL was carried out in the V-REP program for two cases of injury: absent and not functioning limb. We present the results of simulation of emergency gaits for these configurations of WMP and the effectiveness of the proposed method in the case of damage to the kinematic scheme. We also compared the performance of the genetic algorithm for the damaged WMR with the standard control algorithm.

Implementation of Robot Control Algorithms by Real-Time Control System

2015 ◽  
Vol 18 ◽  
pp. 112-119 ◽  
Author(s):  
Viliam Fedak ◽  
Frantisek Durovsky ◽  
Robert Uveges ◽  
Karol Kyslan ◽  
Milan Lacko
Keyword(s):  
Control System ◽  
Real Time ◽  
Robot Control ◽  
Control Algorithm ◽  
Industrial Robot ◽  
Angular Position ◽  
Control Algorithms ◽  
Real Time Control ◽  
Frequency Converters ◽  
Time Control

The paper deals with development and implementation of the direct and inverse kinematics to control of 6 DOF industrial robot SEF-ROBOTER SR25 by a real time control system. To obtain the angular position of each joint an iterative algorithm is applied that is developed in the Simulink program. This solution creates a basis for real time control of the robot drives utilizing features of SIEMENS SINAMICS family of frequency converters. The developed control system presents a universal platform enabling to debug any robot control algorithm and also easy to change a desired trajectory of the end effector. The equipment is suitable for testing different trajectories of the robot and is suitable also for educational purposes.

The Temperature Control of Electric Furnace Based on PID Genetic Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 828-834 ◽  
Author(s):  
Di Lu ◽  
Jian Xin Wang ◽  
Jia Feng Li
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Quick Response ◽  
Actual Operation ◽  
Furnace Control ◽  
Pure Delay ◽  
Operation Results

The characteristics of Mathematical model for the temperature’s control of resistance-heated furnace are non-linear, strong inertia, time-variant and pure delay. Adaptive genetic algorithm(AGA) was designed by the principle of the traditional PID control system, and dynamically Simulated an industrial furnace control system. Simulation and actual operation results show that using the optimization algorithm has properties of no overshoot, quick response, good robust and the algorithm is simple. It is proved that this control algorithm is a more effective one on improving the temperature’s control of the resistance-heated furnace.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

Sign in / Sign up

Export Citation Format

Share Document