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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Horowitz, Roberto Li, Perry |
| Copyright Year | 1992 |
| Description | Author affiliation: Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720 (Horowitz, Roberto; Li, Perry) |
| Abstract | In this paper, we consider the problem of determining an optimal trajectory for the execution of class of robot tasks using a learning-adaptive robot control systems. A quadratic cost functional which involves the reference trajectory and the actual control efforts is optimized on-line while the robot is learning how to execute the tasks. The control-optimization scheme presented in this paper has a hierarchical structure which consists of i) a trajectory tracking controller; ii) a "learning" algorithm which estimates the robot dynamics; and iii) a gradient flow algorithm which attempts to minimize the cost functional using the current estimate of the robot dynamics, and generates the reference trajectory for the tracking controller. The stability of the overall control-optimization system is analyzed and the system is proved to be asymptotically stable. The reference trajectory generated by the gradient flow algorithm converges to a local minimum as long as the training tasks are sufficiently rich. |
| Starting Page | 2623 |
| Ending Page | 2628 |
| File Size | 484602 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780302109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1992-06-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Robot control Trajectory Control systems Cost function Optimal control Stability analysis Adaptive control Programmable control Bibliographies Convergence |
| Content Type | Text |
| Resource Type | Article |
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