Improved NN-PID control of MIMO systems with PSO-based initialisation of weights

Varshney, Tarun; Sheel, Satya

doi:10.1504/ijaac.2014.063362

Cited by 5 publications

(2 citation statements)

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“…A Back propagation neural network can be modified as a PID like structure and provide with the advantages of both BP neural network and PID control. This technique has been utilized and being applied to several nonlinear problems [6], [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study

Chaturvedi,

Kumar,

Kumar

2023

Soft Comput

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This paper proposes a Particle Swarm Optimization (PSO) tuned novel Proportional Integral Derivative (PID) like neural network (PID-NN). The structure of proposed PID-NN is very simple having only 3 neurons in the hidden layer and a single output neuron. The proportional, integral, and derivative gains of the PID controller are represented by the three weights in the neural network's output layer, respectively. The suggested approach uses the PSO method to optimize the output layer weights, which correspond to PID gains. The non-linear Continuous Stirred Tank Reactor (CSTR) plant, one of the most popular chemical industry processes, is utilized to test the suggested approach. A jacketed CSTR's temperature is controlled via a Particle Swarm Optimization tuned PID like neural network (PSO-NN-PID) controller. In terms of time domain specifications, the performance of the PSO-based NN-PID controller, the back propagation-based NN-PID controller (BP-NN-PID), and the conventionally tuned PID controller are compared. Mean square error is the objective function used in PSO-NN-PID and BP-NN-PID to optimize PID settings. The results show that the overshoot decreases from 44.13% in case of Zeigler-Nichols tuned PID controller to 26.33% in case of BPNN-PID controller, and further reduces to 23.13% in case of proposed PSO-based NN-PID controller. The decrease in rise time is observed from 0.2727 seconds in case of BPNN tuned PID to 0.1283 seconds in case of proposed PSO-NN-PID controller.

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Section: Introductionmentioning

confidence: 99%

A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study

Chaturvedi,

Kumar,

Kumar

2023

Soft Comput

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“…The completely dynamic model of the practical systems can't be obtained. Therefore, the model-based controllers cannot achieve good performance for the non-linear MIMO systems [5][6].…”

Section: Introductionmentioning

confidence: 99%

Robust Recurrent Cerebellar Model Articulation Controller for Non-Linear MIMO Systems

Dang¹,

Ta²

2019

ijacsa

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This research proposes a H  robust recurrent cerebellar model articulation control system (RRCMACS) for MIMO non-linear systems to achieve the robustness of the system during operation. In this system, the superior properties of the recurrent cerebellar model articulation controller (RCMAC) are incorporated to imitate an ideal sliding mode controller. The H  robust controller efficiently attenuates the effects of uncertainties, external disturbances, and noises to maintain the robustness of the system. The parameters of the controller were updated in the sense of the Lyapunov-like Lemma theory. Therefore, the stability and robustness of the system were guaranteed. The simulation results for the micromotion stage system are given to prove the effectiveness and applicability of the proposed control system for model-free nonlinear systems.

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An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition

Chen

Zeng

et al. 2020

Neurocomputing

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Improved NN-PID control of MIMO systems with PSO-based initialisation of weights

Cited by 5 publications

References 0 publications

A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study

A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study

Robust Recurrent Cerebellar Model Articulation Controller for Non-Linear MIMO Systems

An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition

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