Digital redesign of uncertain interval systems based on time-response resemblance via particle swarm optimization

Area of Science:

• Control Systems Engineering
• Computational Intelligence
• Optimization Algorithms

Background:

• Designing digital controllers for systems with interval plants presents challenges due to difficulties in obtaining time-response envelopes.
• Existing methods like open-loop discretization may not yield optimal performance for such systems.

Purpose of the Study:

• To propose a particle swarm optimization (PSO) based approach for deriving an optimal digital controller for redesigned digital systems with interval plants.
• To ensure time-response resemblance between the redesigned digital system and its continuous counterpart.

Main Methods:

• The design problem is formulated as minimizing a cost function based on aggregated deviation between step responses of the digital and continuous systems.
• An evolutionary framework incorporating three PSO algorithms is used to find optimal digital controller parameters.
• The method addresses the perturbation of uncertain plant parameters.

Main Results:

• Computer simulations demonstrate that digital controllers derived using the proposed PSO approach lead to improved system performance.
• Redesigned digital systems with PSO-derived controllers show superior performance compared to those using conventional open-loop discretization.

Conclusions:

• The proposed PSO-based method effectively designs optimal digital controllers for interval plant systems.
• This approach offers a viable alternative to conventional discretization methods, achieving better system performance and response approximation.