PID Based on a Single Artificial Neural Network Algorithm for  Intelligent Sensors

Rivera-Mejia, Jose; Léon-Rubio, A.G.; Arzabala-Contreras, E.

doi:10.22201/icat.16656423.2012.10.2.417

Cited by 14 publications

(10 citation statements)

References 42 publications

(34 reference statements)

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“…In Section 4.1 results of both simulation and experimental tests of the proposed adaptive single neuron PID controller trained with extended Kalman filter (EKF-SNPID) compared to conventional PID, backpropagation trained adaptive neural PID controller (BP-PIDNN) [4], and an adaptive neuron PID controller trained with Hebbian learning rule (HR-PIDNN) [19]. Then, in Section 4.1.1, the proposed EKF single neuron PID controller is compared with a conventional PID controller with a back-calculation anti-windup method [29].…”

Section: Resultsmentioning

confidence: 99%

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Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

et al. 2020

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In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

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Section: Resultsmentioning

confidence: 99%

“…• Single neuron PID controllers Examples of this group are works [6,[18][19][20][21]. These adaptive controllers are base on a single neuron whose inputs are the proportional error (P), integral of the error (I), and derivative of the error (D) (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

et al. 2020

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“…Artificial neural networks (ANNs) have been widely studied and adopted in many tasks, for example, pattern recognition (Bishop, 1996), text classification (Apte et al, 1994), optimization (Cochocki & Unbehauan, 1993), and data clustering (Herrero et al, 2001), to name a few. Recently, applications of ANNs even extend to workflow scheduling in critical infrastructure systems (Vukmirović et al, 2012) and intelligent control (Rivera-Mejía et al, 2012). Since the widespread use of ANN, various types of neural networks have been proposed.…”

Section: Related Workmentioning

confidence: 99%

A constructive algorithm for unsupervised learning with incremental neural network

Wang

Chen

et al. 2015

Journal of Applied Research and Technology

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Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neural network is incrementally constructed by the corresponding subnets with individual instances. First, a subnet maps the relation between inputs and outputs for an observed instance. Then, when combining multiple subnets, the neural network keeps the corresponding abilities to generate the same outputs with the same inputs. This makes the learning process unsupervised and inherent in this framework. In our experiment, Reuters-21578 was used as the dataset to show the effectiveness of the proposed method on text classification. The experimental results showed that our method can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. This framework also validates scalability in terms of the network size, in which the training and testing times both showed a constant trend. This also validates the feasibility of the method for practical uses.

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“…In [ 20 ], the authors propose a visual servoing algorithm combined with a proportional derivative (PD) controller. However, PID approaches are not effective on highly nonlinear systems with model uncertainties such as the hexarotor [ 21 , 22 ]. According to this, another approach is required.…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

López-Franco

Gómez-Avila

Alanis

et al. 2017

Sensors

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In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results.

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PID Based on a Single Artificial Neural Network Algorithm for Intelligent Sensors

Cited by 14 publications

References 42 publications

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

A constructive algorithm for unsupervised learning with incremental neural network

Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

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