Kodali et al., 2022 - Google Patents
development of an Optimal pId Controller for the 4-dOF manipulator Using Genetic AlgorithmKodali et al., 2022
- Document ID
- 16606689399685622918
- Author
- Kodali S
- Mandava R
- Rao B
- Publication year
- Publication venue
- Recent Advances in Industrial Production: Select Proceedings of ICEM 2020
External Links
Snippet
Tuning of the PID controller is a time consuming and laborious task and several researchers are working on providing better solutions. In this research article, the authors implemented an optimal PID controller for the 4-DOF planar robotic manipulator which will aid in smooth …
- 230000002068 genetic 0 title abstract description 15
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/40—Robotics, robotics mapping to robotics vision
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/39—Robotics, robotics to robotics hand
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1607—Calculation of inertia, jacobian matrixes and inverses
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Le et al. | An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator | |
Elkhateeb et al. | Novel PID tracking controller for 2DOF robotic manipulator system based on artificial bee colony algorithm | |
Köker et al. | A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study | |
Wang et al. | Adaptive neural network-based visual servoing control for manipulator with unknown output nonlinearities | |
Zhang et al. | A time-varying-constrained motion generation scheme for humanoid robot arms | |
Azar et al. | Fuzzy logic controller with color vision system tracking for mobile manipulator robot | |
Rezaei-Shoshtari et al. | Cascaded gaussian processes for data-efficient robot dynamics learning | |
Kumar et al. | A nonlinear fractional order PID controller applied to redundant robot manipulator | |
Kodali et al. | development of an Optimal pId Controller for the 4-dOF manipulator Using Genetic Algorithm | |
Jangid et al. | Trajectory tracking optimization and control of a three link robotic manipulator for application in casting | |
Tanveer et al. | Real-time machine learning control for robotic manipulator by LNB: Lion Naïve Bayes algorithm | |
Fan et al. | Real-time robust finger gaits planning under object shape and dynamics uncertainties | |
Khan et al. | Real-time inverse kinematics using dual particle swarm optimization DPSO of 6-DOF robot for nuclear plant dismantling | |
Zhang et al. | Time delay compensation of a robotic arm based on multiple sensors for indirect teaching | |
Mahmoodabadi et al. | Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode‐Based Feedback Linearization Controller | |
Thi et al. | A neural network-based fast terminal sliding mode controller for dual-arm robots | |
Xiao et al. | Joint torque prediction of industrial robots based on PSO-LSTM deep learning | |
Lopes et al. | A Review on Quadruped Manipulators | |
Mishra et al. | Development of a flexible assembly system using industrial robot with machine vision guidance and dexterous multi-finger gripper | |
Vochten et al. | Shape-preserving and reactive adaptation of robot end-effector trajectories | |
Budin et al. | Modelling and proportional-integral-derivative controller design for position analysis of the 3-degree of freedom | |
Ruppel et al. | Direct policy optimization with differentiable physical consistency for dexterous manipulation | |
Shi et al. | A deep regression model for safety control in visual servoing applications | |
Gillawat et al. | Human upper limb joint torque minimization using genetic algorithm | |
Uran et al. | Neural-network estimation of the variable plant for adaptive sliding-mode controller |