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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hao Xu Wenxin Liu |
| Copyright Year | 2015 |
| Description | Author affiliation: Coll. of Sci. & Eng., Texas A&M Univ. - Corpus Christi, Corpus Christi, TX, USA (Hao Xu) || Dept. of Electr. & Comput. Eng., Lehigh Univ., Bethlehem, PA, USA (Wenxin Liu) |
| Abstract | In this paper, a novel time-based finite horizon distributed optimal tracking control is proposed for multi-agent systems with completely unknown system dynamics. First, the distributed optimal tracking control problem has been formulated for multi-agent systems. Next, an online Neural Network (NN) identifier is introduced to approximate the control coefficient matrices of multi-agent systems which is subsequently utilized to develop distributed optimal tracking control inputs. Then, in order to achieve the optimality and satisfy the terminal cost at the fixed final time, a novel actor-critic scheme is proposed to determine the time-based finite horizon distributed optimal tracking control in a forward-in-time manner by using critic NN and actor NN efficiently. Eventually, Lyapunov theory is used to demonstrate that all closed-loop signals and NN weights are uniformly ultimately bounded (UUB) with ultimate bounds being a function of initial conditions and fixed final time. Further, the approximated distributed control signal converges close to theoretical distributed optimal value within fixed final time even while the multi-agent systems dynamics are completely unknown. Multiple-agent systems tracking example is included in simulation section to show the effectiveness of the proposed scheme. |
| Starting Page | 935 |
| Ending Page | 940 |
| File Size | 160590 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479987283 |
| e-ISBN | 9781479987306 |
| DOI | 10.1109/CYBER.2015.7288070 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural Network System dynamics Neuro Dynamic Programming Optimal control Multi-agent Systems Artificial neural networks Cost function Style Approximation methods Optimal Control Multi-agent systems |
| Content Type | Text |
| Resource Type | Article |
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