A fast new algorithm for a robot neurocontroller using inverse QR decomposition
A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array. Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.
- Research Organization:
- Univ. of Sheffield (GB)
- OSTI ID:
- 20005689
- Journal Information:
- International Journal of Robotics Research, Journal Name: International Journal of Robotics Research Journal Issue: 1 Vol. 19; ISSN IJRREL; ISSN 0278-3649
- Country of Publication:
- United States
- Language:
- English
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