Anticipations and Time-Varying Modeling in Adaptive Control System

2015 ◽  
pp. 144-159
Author(s):  
Dipak Basu ◽  
Victoria Miroshnik
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Time Varying ◽  
Adaptive Control System

Adaptive control of a robot using neural networks

Robotica ◽  
1994 ◽  
Vol 12 (6) ◽  
pp. 553-561 ◽  
Author(s):  
D. T. Pham ◽  
S. J. Oh
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Control System ◽  
Inverse Dynamics ◽  
Dynamic Interaction ◽  
Variable Load ◽  
Time Varying ◽  
Adaptive Control System ◽  
The Third ◽  
Articulated Robot

SummaryThis paper describes an adaptive control system for an articulated robot with n joints carrying a variable load. The robot is a complex nonlinear time-varying MIMO plant with dynamic interaction between its inputs and outputs. However, the design of the control system is relatively straightforward and does not require any prior knowledge about the plant. This is because the control system is based on using neural networks which can capture the dynamic characteristics of the plant automatically. Three neural networks are employed in total, the first to learn the dynamics of the robot, the second to model its inverse dynamics and the third, a copy of the second neural network, to control the robot.

An Integrated Lattice Filter Adaptive Control System for Time-Varying CMM Structural Vibration Control, Part 1: Theory and Simulation

1996 ◽  
Vol 118 (1) ◽  
pp. 67-76 ◽  
Author(s):  
E. Lu ◽  
J. Ni ◽  
S. M. Wu
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Computational Cost ◽  
Structural Vibration ◽  
Time Varying ◽  
Adaptive Control System ◽  
Line System ◽  
Lattice Filter ◽  
On Line ◽  
Order Determination

An integrated lattice filter adaptive control system is developed for the control of time-varying CMM structural vibrations. An efficient algorithm is developed to provide a link between the adaptive lattice filter and the minimum variance control by directly utilizing the lattice filter parameters at time t − 1 for control. The approach avoids the conversion to system parameters and is therefore computationally efficient for applications of real time control. To fully utilize the benefit of the lattice filter, a heuristic criterion for on-line order determination is developed using the lattice filter parameters. With a linear computational cost, the developed algorithm will perform on-line system order determination, parameter tracking, and control calculation at each sampling instance. The simulation result shows that the approximation of output prediction is reasonable and the integrated lattice filter adaptive control can reduce the system settling time by 82 percent as compared with no control.

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