Identification and control of dynamical systems using different architectures of recurrent fuzzy system

Dass, Anuli; Srivastava, Smriti

doi:10.1016/j.isatra.2018.09.025

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…In the literature, benchmark problems of control engineering are analyzed with control schemes like linear quadratic regulators (LQR) [33], state-space control [24], sliding mode control (SMC) [36], neural networks (NN) [17,6], and fuzzy logic control (FLC) [47,43]. Many nature-inspired and evolutionary techniques have been discussed such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) [3], and they have shown improved performance of the system [9]. Generally speaking, these swarm-based algorithms have some limitations and take more computational effort [42].…”

Section: Related Workmentioning

confidence: 99%

“…Ant colony optimization (ACO) with fuzzy logic controller (FLC) was used to improve the performance of ball and beam system [5,11]. For another example, i.e., robotic arm manipulator, adaptive control and online tuning has been done to improve its performance [23,9]. Fuzzy logic controller in combination with PD controller has been used for the analysis of wheeled robot system [19,13].…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

Srivastava

Chaudhary

et al. 2021

Arab J Sci Eng

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This paper exploits various meta-heuristic optimization techniques to learn PID controller parameters for nonlinear systems. The nonlinear systems considered here are well known ball and beam, inverted pendulum, and robotic arm manipulator. The gain parameters of the controllers are optimized by using two categories of meta-heuristic optimization techniques—swarm-based grasshopper optimization algorithm and particle swarm optimization and human-based, i.e., teacher learning-based optimization. Mean square error has been used to measure the performance of various algorithms. Robustness of these algorithms is studied and compared using parameter perturbation and external disturbance. There are substantial improvements in the performance of these plants using the mentioned algorithms as shown in the simulation results. A detailed comparative analysis of these algorithms has also been done.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

Srivastava

Chaudhary

et al. 2021

Arab J Sci Eng

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“…Remark 5. The major drawback of the learning technique is that the computational burden is high due to the weights learning process [19,20,21,22]. This paper employed the TDE method to significantly reduce the computational burden of the system as it is simple in computing.…”

Section: Adaptive Pid-nftsmc and Tde For Robust Fault Tolerant Controlmentioning

confidence: 99%

“…Therefore, to reduce the chattering, the sliding gain needs to be reduced. To obtain this requirement, learning methods using neural network (NN) [19,20] or fuzzy logic [21,22] has been developed for approximating the uncertain nonlinear function. After obtaining the accurate model based on the learning methods, the sliding gain only needs to estimate the approxi-mation error, which is usually small, and consequently, the chattering is significantly reduced.…”

Section: Introductionmentioning

confidence: 99%

Self-tuning fuzzy PID-nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators

2020

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In this work, a new robust controller is developed for robot manipulator based on an integrating between a novel self-tuning fuzzy proportional-integral-derivative (PID)nonsingular fast terminal sliding mode control (STF-PID-NFTSM) and a time delay estimation (TDE). A sliding surface based on the PID-NFTSM is designed for robot manipulators to get multiple excited features such as faster transient response with finite time convergence, lower error at steady-state and chattering elimination. However, the system characteristics are hugely affected by the selection of the PID gains of the controller. In addition, the design of the controller requires an exact dynamics model of the robot manipulators. In order to obtain effective gains for the PID sliding surface, a fuzzy logic system is employed and in order to get an estimation of the unknown dynamics model, a TDE algorithm is developed. The innovative features of the proposed approach, i.e., STF-PID-NFTSM, is verified when comparing with other up-to-date advanced control techniques on a PUMA560 robot.

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“…In literature, controllers have been classified into three subgroups, that is, classical controllers, adaptive controllers, and artificial intelligence-based controllers. PID controllers and linear quadratic regulator (LQR) controllers are classical controllers, sliding mode and model predictive controllers are adaptive ones, and artificial intelligence-based controllers include fuzzy, neural, neuro-fuzzy (Dass & Srivastava, 2019) and nature-inspired algorithms tuned PID controllers. The PID controller is still used because of its simple structure, easy implementation, cost-effectiveness, and simple tuning methods (Shen, 2002).…”

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confidence: 99%

Performance improvement and Lyapunov stability analysis of nonlinear systems using hybrid optimization techniques

Srivastava

Chaudhary

et al. 2022

Expert Systems

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Using Hybrid optimization algorithms for nonlinear systems analysis is a novel approach. It is a powerful technique that uses the exploitation ability of one algorithm and the exploration ability of another algorithm, to find the best solution. Literature survey reveals that hybrid algorithms not only show quality response but also give faster convergence of error for nonlinear systems. In this paper, hybrid optimization techniques based proportional integral derivative (PID) controller is used for benchmark problems: Continuous stirred tank reactor (CSTR), Inverted pendulum and blood glucose system. Two recent hybrid algorithms: Particle swarm optimization‐Gravitational search algorithm (PSO‐GSA) and Particle swarm optimization‐Grey wolf algorithm (PSO‐GWO) are implemented to control the temperature and concentration of CSTR, pendulum angle of inverted pendulum, glucose concentration and insulin level of blood glucose system. In PID and PSOGWO algorithms, the exploration abilities of GSA and GWO combined with the exploitation ability of PSO have been used. The performance of these algorithms is then compared with individual PSO, GSA, and GWO algorithms proving their superiority. Stability is ensured using the Lyapunov approach while the robustness of the systems is checked using the parameter perturbation technique. Simulation results show substantial improvement in the performance of these systems by using these meta‐heuristic hybrid optimization techniques. A comparative analysis of these algorithms has also been done.

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Identification and control of dynamical systems using different architectures of recurrent fuzzy system

Cited by 13 publications

References 20 publications

RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

RETRACTED ARTICLE: On Comparing the Performance of Swarm-Based Algorithms with Human-Based Algorithm for Nonlinear Systems

Self-tuning fuzzy PID-nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators

Performance improvement and Lyapunov stability analysis of nonlinear systems using hybrid optimization techniques

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