Composite single neural PID controller based on fuzzy self-tuning gain and RBF network identification

Xing, Boyang; Yu, Liye; Zhou, Zhongkai

doi:10.1109/ccdc.2014.6852238

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…To date, the work in this field [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] is primarily divided into four categories. All are computationally intensive, and none offer a unified approach to complete motion control.…”

Section: Related Workmentioning

confidence: 99%

“…All are computationally intensive, and none offer a unified approach to complete motion control. The four categories are: Single Neuron [ 3 , 4 , 5 ]; Fuzzy Logic [ 6 , 7 , 8 , 9 , 10 , 11 ]; Self-Tuning [ 12 , 13 ]; Model Reference (based on the Laplace Transform) [ 13 ]. …”

Section: Related Workmentioning

confidence: 99%

“…The work, however, focuses on specifically the noted application. The authors in [ 4 ] proposed to improve the Single Neuron PID approach using Fuzzy Logic gain control, focusing on nonlinear and time delay control. While there is no doubt a need for nonlinear and/or time delayed control, it is noteworthy to mention that 90% of motion control is with linear systems that do not have any delays in the control feedback [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

“…This paper, however, aims to tackle this problem in more detail. The proposed algorithmic approach in this paper is the simplest and most dynamically adaptive technique for PID self-tuning, providing a complete motion control solution, unlike the prior related work [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] and other algorithms [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ], for generalized humanoid motion control.…”

Section: Related Workmentioning

confidence: 99%

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PID++: A Computationally Lightweight Humanoid Motion Control Algorithm

Arciuolo

Faezipour

2021

Sensors

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Currently robotic motion control algorithms are tedious at best to implement, are lacking in automatic situational adaptability, and tend to be static in nature. Humanoid (human-like) control is little more than a dream, for all, but the fastest computers. The main idea of the work presented in this paper is to define a radically new, simple, and computationally lightweight approach to humanoid motion control. A new Proportional-Integral-Derivative (PID) controller algorithm called PID++ is proposed in this work that uses minor adjustments with basic arithmetic, based on the real-time encoder position input, to achieve a stable, precise, controlled, dynamic, adaptive control system, for linear motion control, in any direction regardless of load. With no PID coefficients initially specified, the proposed PID++ algorithm dynamically adjusts and updates the PID coefficients Kp, Ki and Kd periodically. No database of values is required to be stored as only the current and previous values of the sensed position with an accurate time base are used in the computations and overwritten in each read interval, eliminating the need of deploying much memory for storing and using vectors or matrices. Complete in its implementation, and truly dynamic and adaptive by design, engineers will be able to use this algorithm in commercial, industrial, biomedical, and space applications alike. With characteristics that are unmistakably human, motion control can be feasibly implemented on even the smallest microcontrollers (MCU) using a single command and without the need of reprogramming or reconfiguration.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

PID++: A Computationally Lightweight Humanoid Motion Control Algorithm

Arciuolo

Faezipour

2021

Sensors

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“…A nonlinear controller is suggested by Su in 2005 which enhances the performance of the standard linear PID controller [8]. The PID controllers are also combined with fuzzy logic [9][10][11][12], neural networks [13][14][15][16], or implemented based on fractional order [17][18][19]. This paper proposes a nonlinear controller known as Improved nonlinear state error feedback controller.…”

Section: Introductionmentioning

confidence: 99%

From PID to Nonlinear State Error Feedback Controller

Abdul-Adheem¹,

Ibraheem²

2017

ijacsa

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Abstract-In this paper an improved nonlinear state error feedback controller (INLSEF) has been proposed for perfect reference tracking and minimum control energy. It consists of a nonlinear tracking differentiator together with nonlinear combinations of the error signal. The tracking differentiator generates a set of reference profile for the input signal, which is the signal itself in addition to its derivatives. On the other hand, the 12-parameters nonlinear combinations of the error signal make the INLSEF controller can handles with time-varying and system's nonlinearity. Digital simulation studies have been conducted for the proposed controller and compared with several works from literature survey on two case studies, mass-springdamper which is a very nonlinear system and nonlinear ball and beam systems. The parameters of the nonlinear combination of the error signal are tuned to satisfy the optimality condition by minimizing the OPI performance index defined in this work. From the simulations results one can conclude that the proposed INLSEF controller performance is better than that of its counterpart in terms of speed and control energy and minimum error. Also, the results showed that the proposed controller is effectively enhancing the stability and performance of the closed loop system.

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Adaptive backstepping tracking control of a car with n trailers based on RBF neural network

Jin

Liang

Guo

et al. 2019

Asian Journal of Control

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An adaptive control scheme based on a radial basis function neural network (RBFNN) is proposed for the kinematic model of a car with n trailers. Firstly, a chained system for a kinematic model of a car with n trailers is introduced, then the tracking error system is given. Secondly, a RBFNN controller is designed using the backstepping method and adaptive method, in which neural networks (NNs) are used to approximate the unknown functions in the error system. By using Lyapunov stability theory, the adaptive law of network weights is designed. This method ensures the stability of the error system. Simulation results are provided to verify the effectiveness of the approach.

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Composite single neural PID controller based on fuzzy self-tuning gain and RBF network identification

Cited by 8 publications

References 4 publications

PID++: A Computationally Lightweight Humanoid Motion Control Algorithm

PID++: A Computationally Lightweight Humanoid Motion Control Algorithm

From PID to Nonlinear State Error Feedback Controller

Adaptive backstepping tracking control of a car with n trailers based on RBF neural network

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