Power-Type Varying-Parameter RNN for Solving TVQP Problems: Design, Analysis, and Applications
This repository contains the MATLAB implementation of Power-Type Varying-Parameter RNN for Solving TVQP Problems: Design, Analysis, and Applications (VP-RNN).
An RNN Model for Solving Time-Varying Control Problems
Many practical control problems can be solved by being formulated as time-varying quadratic minimization and time-varying quadratic programing problems. In this project, a power-type varying-parameter recurrent neural network is proposed and analyzed to effectively solve the resulting time-varying problems, as well as the original practical problems. For a clear understanding, we introduce this model from three aspects: design, analysis, and applications. Specifically, the reason why and the method we use to design this neural network model for solving online TVQP problems subject to time-varying linear equality/inequality are described in detail. The theoretical analysis confirms that when activated by six commonly used activation functions, the proposed network achieves a superexponential convergence rate. In contrast to the traditional zeroing neural network with fixed design parameters, the proposed model has better convergence performance. Comparative simulations with state-of-the-art methods confirm the advantages. Furthermore, the application of the proposed to a robot motion planning problem verifies the feasibility, applicability, and efficiency.
Keywords: time-varying systems, recurrent neural networks, convergence, robustness, dynamic programming.
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Copyright © School of Automation Science & Engineering, South China University of Technology. All rights reserved.
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Contact: ldkong@ieee.org.
@ARTICLE{8589008,
author={Z. {Zhang} and L. {Kong} and L. {Zheng}},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Power-Type Varying-Parameter RNN for Solving TVQP Problems: Design, Analysis, and Applications},
year={2019},
volume={30},
number={8},
pages={2419-2433},
}
@ARTICLE{8463509,
author={Z. {Zhang} and L. {Kong} and L. {Zheng} and P. {Zhang} and X. {Qu} and B. {Liao} and Z. {Yu}},
journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
title={Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications},
year={2020},
volume={50},
number={12},
pages={5106-5118},
}