This paper proposes an improved digital redesign (DR) technique for sampled-data fuzzy controllers in nonlinear systems, based on a Takagi–Sugeno (T–S) fuzzy model. To improve the performance of the DR technique, two methodologies are used: a state-matching error cost function, and a continuous-time fuzzy Lyapunov function. Using these two methodologies, a novel DR technique is proposed to guarantee both the stability and state-matching conditions of the sampled-data fuzzy control system. Further, the proposed DR technique is represented as an optimal problem using the linear matrix inequality (LMI) format. Finally, some simulation examples are provided to verify the effectiveness of the proposed technique in comparison with previous techniques.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence