Optimisation-Based Iterative Learning Control for Distributed Consensus Tracking
- Resource Type
- Conference
- Authors
- Zhang, Yueqing; Chen, Bin; Chu, Bing; Shu, Zhan
- Source
- 2024 UKACC 14th International Conference on Control (CONTROL) Control (CONTROL), 2024 UKACC 14th International Conference on. :119-124 Apr, 2024
- Subject
- Robotics and Control Systems
System dynamics
Heuristic algorithms
Scholarships
Predictive models
Prediction algorithms
Performance analysis
Reliability
- Language
- ISSN
- 2766-6522
This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.