Data-driven hybrid switched-gain estimation for hybrid systems with periodic jumps
Xuan Jia1, Junfeng Zhang1, Tarek Raïssi2
1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.
ISA Transactions
|December 18, 2025
Summary
This study introduces data-driven methods for state estimation in unknown hybrid systems with periodic jumps, bypassing the need for precise models. The approach guarantees global exponential stability using measured data, demonstrated in walking robot simulations.
Area of Science:
- Control Systems Engineering
- Systems Theory
- Robotics
Background:
- Accurate state estimation is crucial for controlling hybrid systems.
- Model-based observers require precise system models, which are often unavailable, especially for systems with periodic jumps.
- Existing methods struggle with unknown hybrid systems and periodic dynamics.
Purpose of the Study:
- To develop data-driven estimation approaches for unknown hybrid systems with periodic jumps.
- To eliminate the need for explicit system model identification.
- To ensure theoretical guarantees on estimation stability.
Main Methods:
- Utilizing the S-lemma to construct estimation laws directly from noisy input/measured data.
- Applying behavioral systems theory to derive systems that explain the data.
- Employing data-based linear matrix inequalities for stability criteria.
Main Results:
- Two classes of data-driven estimation methods are presented.
- A data-driven hybrid estimation scheme is designed, removing the model identification step.
- Global exponential stability is theoretically guaranteed under mild conditions.
Conclusions:
- The proposed data-driven methods effectively address state estimation for unknown hybrid systems with periodic jumps.
- The approach was successfully applied to a data-driven hybrid switched-gain estimation framework.
- Simulations on walking robots validated the effectiveness of the data-driven estimation techniques.
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