Automatic Weight Selection in H¿¿ Loop Shaping Using Genetic Algorithm

Muhammad, E.; Arbab, Muhammad Naeem

doi:10.1109/icet.2006.335971

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Some researchers have proposed a technique for simplifying this process [26]. c. In wide operation range applications, it is well known that the robust performance obtained from a single robust controller is not good enough.…”

Section: Pso Based Robust Loop Shaping-fuzzy Gain Scheduled Pid Controlmentioning

confidence: 99%

Robust loop shaping–fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

Kaitwanidvilai

Olranthichachat

2011

Mechatronics

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Section: Pso Based Robust Loop Shaping-fuzzy Gain Scheduled Pid Controlmentioning

confidence: 99%

Robust loop shaping–fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

Kaitwanidvilai

Olranthichachat

2011

Mechatronics

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“…This example is taken from reference which provides the nonlinear aerodynamics for single plane motion of a missile. This model gives a tail-controlled missile traveling between Mach 2 and Mach 4, at altitudes ranging from 3050 meters (10000 feet) to 18290 meters (60000 feet), and with typical angles of attack in the range of ±20 degrees [13] [14]. The transfer function of the Missile Airframe is as follows: - Figure-3 and Figure-4 respectively, which show that the plant is highly unstable.…”

Section: Design Example -Missile Systemmentioning

confidence: 99%

Application of Genetic Algorithm for Tuning of Reduced Ordered Robust PID Controller and Computer Simulations

Muhammad¹

2012

IJCA

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Robust controllers designed via H ∞ Loop Shaping are complicated and are of order higher than that of the plant. It is very difficult to implement these controllers in practical engineering applications. To overcome this problem, Genetic Algorithm is used to approximate a robust PID controller from H ∞ Loop Shaping controller so that the difficulties in implementation of higher order controller can be avoided.

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“…The algorithm is combined with a non-Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator linear model predictive controller process to obtain the exact model of a non-linear system [26]. The authors in [27] propose an automatic loop shaping method with GAs. The weighting functions of the aforementioned control approaches consist of only scalar values.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

Keskin

Aliskan

2023

ELEKTRON ELEKTROTECH

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In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequency-dependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right half-plane zeros and limited bandwidth constraints. A trade-off design flowchart is proposed, providing Pareto optimal solutions to choose the optimal configuration of the robust feedback controller. The control method is structured by combining the robust H∞ optimal technique and the multi-objective algorithm. The effectiveness of the approach is compared with the non-convex single-objective heuristic solutions like the multi-verse optimisation algorithm (MVO), whale optimisation algorithm (WOA), and grey wolf optimisation algorithm (GWO). The focus of this design is to track and stabilise the output voltage of the DC-DC converter in the presence of external disturbances and parameter uncertainties. The optimised controllers are implemented using a digital signal processor (DSP) on a 200 W interleaved boost converter. The simulation results and experimental findings show that the proposed control method provides supreme disturbance rejection along with maintaining the stability of the system.

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Automatic Weight Selection in H¿¿ Loop Shaping Using Genetic Algorithm

Cited by 3 publications

References 2 publications

Robust loop shaping–fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

Robust loop shaping–fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

Application of Genetic Algorithm for Tuning of Reduced Ordered Robust PID Controller and Computer Simulations

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

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