PPubMed8 Sep 2021Cited 4×

Robust Controller Design for Multi-Input Multi-Output Systems Using Coefficient Diagram Method

Kai Liu, Fanwei Meng, Shengya Meng, Chonghui Wang, Quanmin Zhu, Giuseppe Fusco, Jing Na, Weicun Zhang, Ahmad Taher Azar

Abstract

'Giuseppe Fusco' 'Jing Na' 'Weicun Zhang' 'Ahmad Taher Azar'] The coupling between variables in the multi-input multi-output (MIMO) systems brings difficulties to the design of the controller. Aiming at this problem, this paper combines the particle swarm optimization (PSO) with the coefficient diagram method (CDM) and proposes a robust controller design strategy for the MIMO systems. The decoupling problem is transformed into a compensator parameter optimization problem, and PSO optimizes the compensator parameters to reduce the coupling effect in the MIMO systems. For the MIMO system with measurement noise, the effectiveness of CDM in processing measurement noise is analyzed. This paper gives the control design steps of the MIMO systems. Finally, simulation experiments of four typical MIMO systems demonstrate the effectiveness of the proposed method.

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This paper combines the particle swarm optimization (PSO) with the coefficient diagram method (CDM) and proposes a robust controller design strategy for the MIMO systems.

What’s new

i.Combines PSO with CDM for robust MIMO controller design
ii.Transforms the decoupling problem into a compensator parameter optimization problem
iii.Analyzes CDM effectiveness in processing measurement noise
iv.Provides step-by-step control design procedure for MIMO systems
v.Demonstrates effectiveness through simulation experiments on four typical MIMO systems

How they did it

The decoupling problem is transformed into a compensator parameter optimization problem, and PSO optimizes the compensator parameters to reduce the coupling effect in the MIMO systems. For the MIMO system with measurement noise, the effectiveness of CDM in processing measurement noise is analyzed. Simulation experiments of four typical MIMO systems demonstrate the effectiveness of the proposed method.

What they found

PSO achieved the best objective function value (Fmax = -0.1908) and the lowest standard deviation (0.0039) among GA, SFLA, and CS (Table 2).
Evaluation value improved from 0.048587 (reference) to 0.0000067 using the proposed method (Table 5).
Decoupling interaction metric reduced to 0.000000057, indicating minimal interaction (example 3).
Settling time reduced from 43 s (Masaya) to 41 s (CDM) for y1 and from 172 s to 26 s for y2 (Table 6).

Caveats

✕An ideal compensator that diagonalizes Q(s) at all frequencies is difficult to obtain.
✕The decoupling system Q(s) may not meet design requirements for all frequencies.
✕Interaction measurement based on steady‑state assumptions cannot judge decoupling at other frequencies.

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