PID Control, Implementation and Tuning 2011
DOI: 10.5772/15964
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A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Abstract: This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain S… Show more

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Cited by 3 publications
(2 citation statements)
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References 14 publications
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“…As the value range of the sigmoid function is (0, 1), the state of the gate is always half-open and half-closed. The input gate and forget gate in the gating system can be represented by Equations ( 34) and (35), respectively.…”
Section: The Forward Propagation Of Lstmmentioning
confidence: 99%
See 1 more Smart Citation
“…As the value range of the sigmoid function is (0, 1), the state of the gate is always half-open and half-closed. The input gate and forget gate in the gating system can be represented by Equations ( 34) and (35), respectively.…”
Section: The Forward Propagation Of Lstmmentioning
confidence: 99%
“…It has been utilized to control camera stabilizers, achieving the precise control of the stable position of the camera. The Dynamic Neural Network (DNN) can be used to tune the gain of the PID controller online [35], which is applied to control pneumatic artificial muscles. This improves the speed and performance of the control process, and features a dynamically self-organizing structure.…”
Section: Introductionmentioning
confidence: 99%