Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Lu, Shumin; Li, Dong-Juan

doi:10.1155/2017/6893521

Cited by 14 publications

(9 citation statements)

References 55 publications

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“…Recently, many algorithms have been implemented on FPGA for real-time applications [14][15][16][17]. Emanuel et al [18] proposed a fuzzy logic edge detector based on the morphological gradient for pattern recognition and realized on FPGA.…”

Section: Related Workmentioning

confidence: 99%

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

et al. 2018

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This study used Xilinx Field Programmable Gate Arrays (FPGAs) to implement a functional neuro-fuzzy network (FNFN) for solving nonlinear control problems. A functional link neural network (FLNN) was used as the consequent part of the proposed FNFN model. This study adopted the linear independent functions and the orthogonal polynomials in a functional expansion of the FLNN. Thus, the design of the FNFN model could improve the control accuracy. The learning algorithm of the FNFN model was divided into structure learning and parameter learning. The entropy measurement was adopted in the structure learning to determine the generated new fuzzy rule, whereas the gradient descent method in the parameter learning was used to adjust the parameters of the membership functions and the weights of the FLNN. In order to obtain high speed operation and real-time application, a very high speed integrated circuit hardware description language (VHDL) was used to design the FNFN controller and was implemented on FPGA. Finally, the experimental results demonstrated that the proposed hardware implementation of the FNFN model confirmed the viability in the temperature control of a water bath and the backing control of a car.Electronics 2018, 7, 145 2 of 22 the accuracy of functional approximation. Corresponding to a FLNN, each fuzzy rule comprises a functional expansion of inputs. The linearly independent functions and orthogonal polynomials are used in FLNN. The learning algorithm was divided into structure learning and parameter learning and used for constructing the FNFN automatically. Initially, no rules existed in the FNFN model. In the structure learning algorithm, the entropy measure was used to determine a whether a new node needed to be added. In the parameter learning, the backpropagation leaning method was used to adjust the parameters of the FNFN model.Real-time control is very important in industrial process control system applications. For rapid computing hardware engineering, real-time control becomes more feasible [10]. Recently, there has been a focus on the hardware implementation [11] of artificial neural networks (ANNs). Furthermore, the realization that a hybrid of neural networks and fuzzy systems presents an even more powerful form of computational intelligence [12] provides additional motivation to complete hardware implementation. The main reason for hardware implementation is that it has high speed processing and real-time operating capability. In many applications, hardware implementation requires larger arrays and has resorted to digital simulation, which are usually built using digital integrated circuits. Development of digital integrated circuits such as FPGA [13] makes the hardware implementation process programmable and flexible. Recently, the hardware implementation of neural networks has been successfully implemented. Li et al.[10] discussed various aspects of the hardware implementation of an artificial neural network (ANN), e.g., generic architecture, back propagation, precision, etc. One of the b...

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Section: Related Workmentioning

confidence: 99%

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

et al. 2018

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“…and are, respectively, the pressure and flow of the pipe, and they can be described as in (3). , , and are expressed as in (4).…”

Section: Nonlinear Hydropower Generation Systemmentioning

confidence: 99%

“…Hydropower generation system (HGS) coupling with hydraulic-mechanical-electrical-magnetic nonlinear structures acts as a core part of a hydropower station, which is connected with the stability of the station [3][4][5][6]. Many safety accidents of the HGS occurred in the last thirty years all over the world [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Safety Assessment in Nonlinear Hydropower Generation Systems

Yang

Xia

2018

Complexity

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This paper focuses on the stability problems in a hydropower station. To enable this study, we consider a nonlinear hydropower generation system for the load rejection transient process based on an existing hydropower station. Herein we identify four critical variables of the generation system. Then, we carry out the dynamic safety assessment based on the Fisher discriminant method. The dynamic safety level of the system is determined, and the evolution behavior in the transient process is also performed. The result demonstrates that the hydropower generation system in this study case can operate safely, which is in a good agreement with the corresponding theory and actual engineering. Thus, the framework of dynamic safety assessment aiming at transient processes will not only provide the guidance for safe operation, but also supply the design standard for hydropower stations.

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“…In every subsystem, neural networks approximate the unknown nonlinear systems with adaptive law, based on Lyapunov stability theory, then the systems achieve the asymptotic stability or uniformly ultimately bounded stable. In order to better exploit the application of adaptive neural networks [27,28], finite time stable results are more meaningful for uncertain system [29,30], high order stochastic nonlinear system [31], and interconnected nonlinear system [32].…”

Section: Introductionmentioning

confidence: 99%

Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure‐Feedback Nonlinear Systems

Zhang

Zhu

et al. 2019

Complexity

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This paper proposes a new scheme for solving finite time neural networks adaptive tracking control issue for the nonaffine pure-feedback nonlinear system. The procedure, based on homeomorphism mapping and backstepping, effectively deals with constraint control and design difficulty induced by pure-feedback structure. The most outstanding novelty is that finite time adaptive law is proposed for training weights of neural networks. Furthermore, by combining finite time adaptive law and Lyapunov-based arguments, a valid finite time adaptive neural networks controller design algorithm is presented to ensure that system is practical finite stable (PFS) rather than uniformly ultimately bounded (UUB). Because of using the finite time adaptive law to training weights of neural networks, the closed-loop error system signals are in assurance of bounded in finite time. Benchmark simulations have well demonstrated effectiveness and efficiency of the proposed approach.

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Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Cited by 14 publications

References 55 publications

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

Dynamic Safety Assessment in Nonlinear Hydropower Generation Systems

Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure‐Feedback Nonlinear Systems

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