scholarly journals HIV–TB co-infection treatment control using multi-objective optimized sliding mode

2020 ◽  
Vol 19 ◽  
pp. 100316
Author(s):  
S. Hadipour Lakmesari ◽  
M.J. Mahmoodabadi ◽  
S. Hadipour
Keyword(s):  
Sliding Mode ◽  
Treatment Control ◽  
Multi Objective ◽  
Infection Treatment

Optimal Power Tracking of DFIG-Based Wind Turbine Using MOGWO-Based Fractional-Order Sliding Mode Controller

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
Ali Darvish Falehi
Keyword(s):  
Wind Turbine ◽  
Fractional Order ◽  
High Performance ◽  
Reactive Power ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Gray Wolf ◽  
Multi Objective ◽  
Active And Reactive Power ◽  
Doubly Fed

Abstract The doubly fed induction generator (DFIG)-based wind turbine as a nonlinear, compound, and multivariable time-varying system encompasses several uncertainties especially unfamiliar disturbances and unmodeled dynamics. The design of a high-performance and reliable controller for this system is regarded as a complex task. In this paper, an effective and roust fractional-order sliding mode controller (FOSMC) has been designed to accurately regulate the active and reactive power of DFIG. FOSMC has overcome the system uncertainties and abated the chattering amplitude. Since tuning the FOSMC is a challenging assignment, the application of a multi-objective optimization algorithm can efficiently and precisely solve the design problem. In this regard, non-dominated sorting multi-objective gray wolf optimizer (MOGWO) is taken into account to optimally adjust the FOSMC. In a word, the simulation results have definitively validated robustness of MOGWO-based FOSMC in order to accurately track DFIG's active and reactive power.

Hybrid Fuzzy Skyhook Surface Control Using Multi-Objective Microgenetic Algorithm for Semi-Active Vehicle Suspension System Ride Comfort Stability Analysis

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Yi Chen ◽  
Zhong-Lai Wang ◽  
Jing Qiu ◽  
Hong-Zhong Huang
Keyword(s):  
Stability Analysis ◽  
Real Time ◽  
Ride Comfort ◽  
Polynomial Function ◽  
Sliding Mode ◽  
Initial Conditions ◽  
Active Suspension ◽  
Suspension System ◽  
Multi Objective ◽  
Microgenetic Algorithm

A polynomial function supervising fuzzy sliding mode control (PSFαSMC), which embedded with skyhook surface method, is proposed for the ride comfort of a vehicle semi-active suspension. The multi-objective microgenetic algorithm (MOμGA) has been utilized to determine the PSFαSMC controller’s parameter alignment in a training process with three ride comfort objectives for the vehicle semi-active suspension, which is called the “offline” step. Then, the optimized parameters are applied to the real-time control process by the polynomial function supervising controller, which is named “online” step. A two-degree-of-freedom dynamic model of the vehicle semi-active suspension systems with the stability analysis is given for passenger’s ride comfort enhancement studies, and a simulation with the given initial conditions has been devised in MATLAB. The numerical results have shown that this hybrid control method is able to provide real-time enhanced level of reliable ride comfort performance for the semi-active suspension system.

Training neural networks with a multi-objective sliding mode control algorithm

2003 ◽  
Vol 51 ◽  
pp. 467-473 ◽  
Author(s):  
Marcelo A. Costa ◽  
Antônio P. Braga ◽  
Benjamin R. Menezes ◽  
Roselito A. Teixeira ◽  
Gustavo G. Parma
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Multi Objective ◽  
Mode Control

An Experimental Study of Robustness of Multi-Objective Optimal Sliding Mode Control

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Zhi-Chang Qin ◽  
Fu-Rui Xiong ◽  
Jian-Qiao Sun
Keyword(s):  
Experimental Study ◽  
Sliding Mode Control ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Estimation Error ◽  
Multi Objective ◽  
Mode Control ◽  
System A ◽  
Performance Variation ◽  
The Stability

This paper presents an experimental study of robustness of multi-objective optimal sliding mode control, which is designed in a previous study. Inertial and stiffness uncertainties are introduced to a two degrees-of-freedom (DOF) under-actuated rotary flexible joint system. A randomly selected design from the Pareto set of multi-objective optimal sliding mode controls is used in the experiments. Three indices are introduced to evaluate the performance variation of the tracking control in the presence of uncertainties. We have found that the multi-objective optimal sliding mode control is quite robust against the inertial and stiffness uncertainties in terms of maintaining the stability and delivering satisfactory tracking performance as compared to the control of the nominal system, even when the uncertainty is not a small quantity. Furthermore, we have studied the effect of upper bounds of the model estimation error on the stability of the closed-loop system.

scholarly journals Multi-objective optimal design of sliding mode control with parallel simple cell mapping method

2016 ◽  
Vol 23 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Zhi-Chang Qin ◽  
Fu-Rui Xiong ◽  
Qian Ding ◽  
Carlos Hernández ◽  
Jesús Fernandez ◽  
...  
Keyword(s):  
Optimal Design ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Mapping Method ◽  
Simple Cell ◽  
Design Parameters ◽  
Cell Mapping ◽  
Multi Objective ◽  
Mode Control ◽  
Simple Cell Mapping

This paper presents a study of the multi-objective optimal design of a sliding mode control for an under-actuated nonlinear system with the parallel simple cell mapping method. The multi-objective optimal design of the sliding mode control involves six design parameters and five objective functions. The parallel simple cell mapping method finds the Pareto set and Pareto front efficiently. The parallel computing is done on a graphics processing unit. Numerical simulations and experiments are done on a rotary flexible arm system. The results show that the proposed multi-objective designs are quite effective.

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