Andere Kunden interessierten sich auch für
![Mob Control: Models of Threshold Collective Behavior]( "Mob Control: Models of Threshold Collective Behavior")
Mob Control: Models of Threshold Collective Behavior74,99 €![Information Security Technologies in the Decentralized Distributed Networks]( "Information Security Technologies in the Decentralized Distributed Networks")
Information Security Technologies in the Decentralized Distributed Networks139,09 €![Food Quality Control: Methods, Importance and Latest Measures]( "Food Quality Control: Methods, Importance and Latest Measures")
Food Quality Control: Methods, Importance and Latest Measures183,99 €![Educational Robotics in the Context of the Maker Movement]( "Educational Robotics in the Context of the Maker Movement")
Educational Robotics in the Context of the Maker Movement110,99 €![Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction]( "Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction")
Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction37,99 €![Artificial Intelligence and Robotics]( "Artificial Intelligence and Robotics")
Artificial Intelligence and Robotics125,99 €![Artificial Intelligence and Robotics]( "Artificial Intelligence and Robotics")
Artificial Intelligence and Robotics125,99 €
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors.
This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF).
The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold.
The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network.
The thirdcontrol scheme applies a decentralized neural inverse optimal control for stabilization.
The fourth decentralized neural inverse optimal control is designed for trajectory tracking.
This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.
This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF).
The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold.
The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network.
The thirdcontrol scheme applies a decentralized neural inverse optimal control for stabilization.
The fourth decentralized neural inverse optimal control is designed for trajectory tracking.
This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.