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First/second-order predefined-time convergent ZNN models for time-varying quadratic programming and robotic
Hongsong Wen1, Youran Qu1, Xing He1
1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, 400715, China.
ISA Transactions
|December 21, 2023
Summary
Two novel Zeroing Neural Network (ZNN) models, FPTZNN and SPTZNN, achieve predefined-time convergence for time-varying quadratic programming. These models offer enhanced performance and adjustable convergence times for optimization tasks.
Area of Science:
- Computational Neuroscience
- Optimization Theory
- Control Systems Engineering
Background:
- Zeroing Neural Network (ZNN) models are crucial for computation and optimization.
- Existing ZNN models often lack predefined-time convergence for dynamic problems.
- Time-varying quadratic programming (TVQP) presents significant computational challenges.
Purpose of the Study:
- To propose two new ZNN models for solving the TVQP problem with predefined-time convergence.
- To enhance the capabilities of traditional ZNN models for dynamic optimization.
- To demonstrate flexible control over convergence time.
Main Methods:
- Development of a first-order predefined-time convergent ZNN (FPTZNN) model.
- Extension to a second-order predefined-time convergent ZNN (SPTZNN) model.
- Application of Lyapunov stability theory and predefined-time stability concepts.
Main Results:
- Both FPTZNN and SPTZNN models exhibit predefined-time convergence for TVQP.
- Convergence time is adjustable via predefined-time control parameters.
- Simulation experiments confirm superior performance compared to existing ZNN models.
Conclusions:
- The proposed FPTZNN and SPTZNN models effectively solve the TVQP problem.
- These models offer faster and more predictable convergence than traditional ZNNs.
- The FPTZNN model's practicality is validated through successful application in robot motion planning.
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