Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

Yuvaraja, T.; Ramya, K.

doi:10.1504/ijimr.2018.090941

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Type-1 fuzzy logic, IT2FLC, and adaptive neural fuzzy inference system (ANFIS) PID control strategies were applied to the triple inverted pendulum without testing system's robustness [14][15][16]. More recently published studies did not investigate the robustness of the proposed control system neither [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Balancing of Robustness and Performance for Triple Inverted Pendulum Using μ-Synthesis and Gazelle Optimization

Shafeek,

Ali

2024

JESA

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The control of systems like: bipedal locomotion robots, space launch vehicle, offshore wind turbines, and active vibration control systems in buildings and bridges, have to ensure, besides stability and accuracy, the system's insensitivity to parameters' uncertainties, unmodeled dynamics, external disturbances, and measurements noise. In such systems analysis and controller design, a triple inverted pendulum can be used as a benchmark to mimic systems characteristics and effect of different sources of uncertainty. μ-synthesis is a robust control method which seeks a controller that minimizes the robust H-infinity performance of the closed-loop system through D-K iteration. The D-K iteration is not guaranteed to converge to a global, or even local minimum. Hence this paper proposes the enhancement of controller design by applying gazelle optimization technique to shape the fictitious output by determining the parameters of the performance weighting matrix. The incorporation of optimization with controller design allows avoiding getting unnecessarily conservative system at the expense of performance. The developed control system is simulated using Matlab R2023b for different scenarios of system uncertainty. The results show that the requirements of robustness and performance can be balanced through the right choice of cost function. The robust performance measure obtained is 0.6432 which leads to good response for both stabilization and tracking in the presence of uncertainty. The results also show that even the baseline μ-synthesis design achieves higher robust stability margin about 2.818, the proposed optimized method stabilizes the system with overshoot been reduced by 67.65% and steady state error reduced by 5.69% without sacrificing robustness.

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