Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama, Sabrine; Errachdi, Ayachi; Benrejeb, Mohamed

doi:10.1155/2019/7340392

Cited by 19 publications

(11 citation statements)

References 24 publications

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“…Gaussian function usually called Gaussian and used to arbitrary real constant and non-zero variable; it is simple and using it with the RBF network does not increase the computational complexity when using it with a significant variable or large dataset. where better analysis results can be obtained when executing the data set [22], [23]. The Gaussian function expressed as (14):…”

Section: Radial Basis Function (Rbf) With Gradient Descent Pid Tuningmentioning

confidence: 99%

An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach

Abdalhadi

Wahid

Burhanuddin

2022

IJEECS

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<span>A magnetic levitation system (MLS) is a complex nonlinear system that requires an electromagnetic force to levitate an object in the air. The electromagnetic field is extremely sensitive to noise which can cause the acceleration on the spherical object, leading it to move into the unbalanced region. This paper presents a comparative assessment of controllers for the magnetic levitation system using proportional integral derivative (PID) controller based optimal tuning. The analysis was started by deriving the mathematical model followed by the implementation of radial basis function neural network (<a name="_Hlk93327650"></a>RBFNN) based metamodel. The optimal tuning of the PID controller has offered better transient responses with the improvement of overshoot and the rise time as compared to the standard optimization methods. It is more robust and tolerant as compared to gradient descent method. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are better than the gradient descent (GD) and conventional PID methods. For the verification purpose, a Simscape model has been developed which mimic the real model. It was found that the model has produced about similar performance as what has been obtained from the MATLAB simulation.</span>

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Section: Radial Basis Function (Rbf) With Gradient Descent Pid Tuningmentioning

confidence: 99%

An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach

Abdalhadi

Wahid

Burhanuddin

2022

IJEECS

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“…Generally, the fixed parameters reduce the control performance [28]. In order to overcome this problem, many researchers focusing on the inventing of new modified PID, like integrating the neural network with traditional PID controller (NN-PID) in references [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

Euldji¹,

Batel²,

Rebhi³

et al. 2022

JESA

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The present investigation suggests a novel control technique that merge the advantage of the adaptive Neuro-Fuzzy inference system (ANFIS) with the proportional integral derivative (PID) controller, abbreviated as (ANFIS-PID), for dealing with the dynamics of the of wheeled mobile robot (WMR). A comparative study of various meta-heuristic optimization algorithms is made. The results revealed the highest efficiency of the suggested ANFIS-PID technique compared to seven designed PID controllers, in terms of settling and rise time, overshoot, and the integral square error (ISE) as a cost function. Various cases, study (circular path, diamond path, zigzag path) have highlighted the over performance of mentioned controller. Moreover, this hybrid technique is fused with backstepping approach for the kinematic control. A lemniscate and a square trajectory were performed to clarify the capability of the mentioned controller.

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“…Above all these classical trajectory tracking methods, many researchers start introducing neural network into the UAV control system, especially in recent years. In reference [20], an adaptive switch controller (ASC) is proposed for the nonlinear multi-input multi-output system (MIMO). In this paper, the ASC is an online switch between the neural network adaptive PID (APID) controller and the neural network indirect adaptive controller (IAC).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network

et al. 2022

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To improve the trajectory tracking accuracy, the anti-jamming performance, and the environment adaptability of a quadrotor, the paper proposes a new adaptive trajectory tracking algorithm with multilayer neural network and sliding mode control method. The major difference between other related approaches is that the paper uses the multilayer neural network in the system and the neural network is online computing in the whole process. Firstly, the paper establishes the quadrotor dynamic model and introduces the conception of Sigma-Pi neural network. Then, the paper adds the neural network to the attitude and trajectory tracking control loop. Moreover, the paper designs the adaptive neural network control law. At last, to illustrate the stability of the adaptive control law, the paper gives the Lyapunov stability analysis. Finally, to demonstrate the effectiveness of the method, the paper gives different types of simulation. Comparing with different cases, when increasing the layer of the neural network, the trajectory tracking performance becomes better. In addition, introducing multilayer neural network into the system could improve the anti-interference ability of the system and has a high-precision in tracking the desire trajectory.

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Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Cited by 19 publications

References 24 publications

An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach

An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach

Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network

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