A Nonlinear Controller Based on the Convolutional Neural Networks

Nobahari, Hadi; Seifouripour, Yousef

doi:10.1109/icrom48714.2019.9071803

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Nobahari focuses on developing a nonlinear controller based on the convolutional neural networks to control different plants. It is assumed that prior knowledge of the plants is very limited and there are only sensory input-output data history of the plants [19]. Wang studied the hysteresis nonlinear characteristics of piezoelectric actuators, a novel hybrid modeling method based on long short-term memory (LSTM) and nonlinear autoregressive with external input (NARX) neural networks is proposed [20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Zhang

et al. 2021

Processes

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In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.

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Section: Introductionmentioning

confidence: 99%

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Zhang

et al. 2021

Processes

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Adaptive PID controller using deep deterministic policy gradient for a 6D hyperchaotic system

Labbaf Khaniki,

Samii,

Tavakoli-Kakhki

2024

Transactions of the Institute of Measurement and Control

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This article introduces a method for the adaptive control of a six-dimensional (6D) hyperchaotic system using a multi-input multi-output (MIMO) approach, leveraging the deep deterministic policy gradient (DDPG) algorithm. The states and tracking errors of the hyperchaotic system are amalgamated to form an input image signal. This signal is then processed by a deep convolutional neural network (CNN) to extract profound features. Subsequently, the DDPG determines the coefficients of the proportional–integral–derivative (PID) controller based on the features discerned from the CNN. The proposed approach exhibits robustness to uncertainties and varying initial conditions, attributed to the DDPG’s ability to learn from the input image signal and adaptively adjust control policies and PID coefficients. The results demonstrate that the proposed adaptive PID controller, integrated with DDPG and CNN, surpasses conventional controllers in terms of synchronization accuracy and response speed. The paper presents the following: a 6D hyperchaotic system’s dynamic model, a CNN-based DDPG’s structure, and how it performs and compares to traditional methods. Then, it summarizes the main findings.

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Model-free control of a fixed-wing aircraft based on convolutional neural networks

Seifouripour,

Nobahari

2024

JoC

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A Nonlinear Controller Based on the Convolutional Neural Networks

Cited by 3 publications

References 12 publications

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Adaptive PID controller using deep deterministic policy gradient for a 6D hyperchaotic system

Model-free control of a fixed-wing aircraft based on convolutional neural networks

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