A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches

CORRESPONDENCE - Takagi-Sugeno Fuzzy-Model-Based Fault Detection for Networked Control Systems with Markov Delays

Multi-population based nature-inspired optimization algorithms have attracted wide research interests in the last decade, and become one of the frequently used methods to handle real-world optimization problems. Considering the importance and value of multi-population methods and its applications, we believe it is the right time to provide a comprehensive survey of the published work, and also to discuss several aspects for the future research. The purpose of this paper is to summarize the published techniques related to the multi-population methods in nature-inspired optimization algorithms. Beginning with the concept of multi-population optimization, we review basic and important issues in the multi-population methods and discuss their applications in science and engineering. Finally, this paper presents several interesting open problems with future research directions for multi-population optimization methods.

Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

PSO-MISMO Modeling Strategy for MultiStep-Ahead Time Series Prediction

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms.

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

Sensitivity analysis and identification of kinetic parameters in batch fermentation of glycerol

The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.

A discrete shuffled frog optimization algorithm

Simulated Annealing algorithm (SA) is a local search method invented to avoid local minima. The main shortcomings of this algorithm are its slowness and difficult tuning of its parameters. It is claimed that newer versions of fast SA (FSA) and very fast SA (VFSA) have resolved these problems, but these algorithms have some problems such as estimation of initial temperature and neighborhood generation. In this paper a modified form of VFSA is represented in which the standard deviation of the cost function is used for the estimation of initial temperature and also some modifications is done in neighborhood generation which made the algorithm more accurate. This algorithm is compared with some evolutionary-based algorithms and as simulation results show, it can optimize the complex problems with high accuracy in a feasible time.

A modified very fast Simulated Annealing algorithm

In this paper, a new design approach to construct a fault-tolerant controller (FTC) with fault estimation capability is proposed using a generalized Takagi-Sugeno (T-S) fuzzy model for a class of n...

Fault-tolerant controller design with fault estimation capability for a class of nonlinear systems using generalized Takagi-Sugeno fuzzy model:

This paper introduces three hybrid methods for the generation and optimization of rules and membership functions of a fuzzy logic controller for nonlinear systems. The proposed methods overcome the deficiency of a systematic approach for optimal design of fuzzy controllers. An optimally designed fuzzy logic controller should have the least number of fuzzy variables and fuzzy rules and the best possible configuration of fuzzy rules in the rule table. The first strategy of this paper is a two-phase optimization problem: in the first phase, the number of fuzzy variables and their arrangement in the rule table are optimized by a genetic algorithm; in the second phase, the parameters of the membership functions are optimized via extended Kalman filtering. The second strategy tries to achieve the goals of the first method all in one phase by modifying the chromosome structure of the genetic algorithm. Then in the next step, a local search algorithm is utilized to improve the obtained results. The third strategy...

Design of optimized fuzzy model-based controller for nonlinear systems using hybrid intelligent strategies

A novel design approach to construct a fault-tolerant control (FTC) system for a class of nonlinear systems based on a generalized Takagi-Sugeno (GT-S) fuzzy model is proposed. The local rules of the GT-S fuzzy model consist of some multiplicative nonlinear terms. The nonlinear system is affected by actuator faults and unknown disturbances. A state/fault observer is designed and then, a dynamic output feedback scheme is proposed based on the estimated fault and state information. The sufficient conditions for observer and controller design are separately given in terms of linear matrix inequalities (LMIs). It can be shown that the number of LMIs and the computational burden is less than that of similar methods and the effectiveness of the proposed dynamic output feedback FTC approach is verified by proposing simulation results applied to an inverted pendulum system.

https://tjee.tabrizu.ac.ir/article_13299_3082b24069ec0b88f6b8b492f06c2639.pdf

Alireza Navarbaf

Papers

A discrete shuffled frog optimization algorithm

A modified very fast Simulated Annealing algorithm

Fault-tolerant controller design with fault estimation capability for a class of nonlinear systems using generalized Takagi-Sugeno fuzzy model:

Design of optimized fuzzy model-based controller for nonlinear systems using hybrid intelligent strategies

Dynamic output feedback fault-tolerant controller design for a class of generalized Takagi-Sugeno fuzzy nonlinear systems