Tiwari, Shivendra N and Padhi, Radhakant (2018) Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design. In: INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 49 (2). pp. 246-263.
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Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as `Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.
| Item Type: | Journal Article |
|---|---|
| Publication: | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE |
| Publisher: | TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND |
| Additional Information: | Copy right for this article belong to TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 16 Apr 2018 20:09 |
| Last Modified: | 16 Apr 2018 20:09 |
| URI: | http://eprints.iisc.ac.in/id/eprint/59600 |
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