Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

Na, Jing; Ren, Xuemei; Shang, Cong; Guo, Yu

doi:10.1016/j.jprocont.2011.09.003

Cited by 87 publications

(48 citation statements)

References 35 publications

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“…For a class of unknown nonlinear affine time-delay systems, an adaptive control scheme was proposed by constructing two high-order NNs for identifying system uncertainties [47]. This idea has been further extended to affine nonlinear systems with input time-delay in [48]. It should be noticed that, piecewise continuous functions such as frictions, backlash, and dead-zone are widely existed in industrial plants.…”

Section: Cerebellar Model Articulation Controller (Cmac) Nnmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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Section: Cerebellar Model Articulation Controller (Cmac) Nnmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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“…Adaptive systems are also used to compensate input delay, as proposed in Na et al [4] where an adaptive NN observer was designed for nonlinear systems. The generic model control (GMC) of Lee and Sullivan [5] is probably one of the simplest nonlinear control techniques to install and maintain among nonlinear model-based controllers.…”

Section: Introductionmentioning

confidence: 99%

A Gain-Scheduling PI Control Based on Neural Networks

Tronci¹,

Baratti²

2017

Complexity

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This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC) formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR), considering both single-input single-output (SISO) and multi-input multi-output (MIMO) control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

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“…Many researchers implemented the adaptive forms of neural networks to forecast complex benchmarks (see, for example [3,5,[7][8][9][10][11][12][13]). An adaptive network must selfadapt its structure and self-adjust its parameters over time as changes are sensed.…”

Section: Introduction and Orientationmentioning

confidence: 99%

A Feedback-oriented Data Delay Modeling in a Dynamic Neural Network for Time Series Forecasting

2016

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Abstract. In this study, we develop a neural network with a time shifting approach to forecast time series patterns. We investigate the impact of di erent layer-weight con gurations to capture the trends in seasonal, chaotic, etc. forms. We also hypothesize the combined e ect of the delayed inputs and the forward connections to introduce a dynamical structure. The e ect of over tting issue is procedurally monitored to gain the resistance property from the early stoppage of training process and to reduce the error of predictions. Finally, the performance of the proposed network is challenged by six well-known deterministic and non-deterministic time series and compared by the autoregression (AR), Arti cial Neural Network (ANN), Adaptive K-nearest Neighbors (AKN), and adaptive neural network (ADNN) models. The results show that the proposed network outperforms the conventional models, particularly in forecasting the chaotic and seasonal time series.

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Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

Cited by 87 publications

References 35 publications

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

A Gain-Scheduling PI Control Based on Neural Networks

A Feedback-oriented Data Delay Modeling in a Dynamic Neural Network for Time Series Forecasting

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