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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 third control 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 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 third control 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.
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