Intelligent optimal control with dynamic neural networks

Becerikli, Yaşar; Konar, A. F.; Samad, Tariq

doi:10.1016/s0893-6080(02)00232-0

Cited by 77 publications

(37 citation statements)

References 10 publications

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“…Psaltis et al (1988) employed a multi-layer neural network processor and used different learning architectures to train the neural controller for a given plant. Lietzau and Kreiner ( (2001), (2004) Development of an intelligent optimal control system with learning generalization capabilities was explored by Becerikli et al (2003). They used a DNN as a control trajectory priming system to overcome the non-dynamic nature of popular ANN architectures.…”

Section: Black-box Modelsmentioning

confidence: 99%

Design of conventional and neural network based controllers for a single-shaft gas turbine

Asgari

Jegarkandi

Chen

et al. 2017

AEAT

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Purpose -The purpose of this paper is to develop and compare conventional and neural network based controllers for gas turbines.Design/methodology/approach -Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an ANN-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine.They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network based modelling is employed. Performances of the controllers are explored and compared on the base of design criteria and performance indices.Findings -It is shown that NARMA-L2, as a neural network based controller, has a superior performance to the PID controller.Practical implications -Using artificial intelligence in gas turbine control systems.Originality/value -Providing a novel methodology for control of gas turbines.

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Section: Black-box Modelsmentioning

confidence: 99%

Design of conventional and neural network based controllers for a single-shaft gas turbine

Asgari

Jegarkandi

Chen

et al. 2017

AEAT

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“…Besides all of these networks are not being trained with rigor methods. Moreover in [5,6] an approach to consider the neural network's teaching as an optimal control problem is presented. Hence in this paper authors are going to present an attempt to set the problem of rational control of the special neural network which solves rail scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

One approach to control of a neural network with variable signal conductivity

Olshansky¹,

Ignatenkov²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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The article focuses on a new extent to synthesize a control strategy of learning of a special artificial neural network with variable signal conductivity and on features of neural networks with variable signal conductivity. The purpose of the research is to create a new control strategy based on analysis of the neural network error signal. Also the article contains an approach how to compute the control strategy. The main result is contained in the necessity to realize the control strategy on the basis of preliminary training of the neural network with specific techniques. Authors also suggest a technique of computing the crucial trajectory of the network's error decreasing. The trajectory may be computed using signal processing methods.

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“…Various intelligent control techniques have been developed for the control of these non-linear systems [4,5,6]. The recent development in areas of soft-computing techniques like ANFIS [7], fuzzy logic [8], Neural networks (NNs) [9], Genetic algorithm (GA) [10], Particle swarm optimisation (PSO) [11], Linear quadratic regulator (LQR) [12], Proportional-integral-derivative (PID) [13] etc have given novel solution to control of IP systems. The PID control provides the simplest and efficient control of IP systems.…”

Section: Introductionmentioning

confidence: 99%

A Pid Based Anfis & Fuzzy Control Of Inverted Pendulum On Inclined Plane (Ipip)

Kharola

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-The objective of this study is to present an offline control of highly non-linear inverted pendulum system moving on a plane inclined at an angle of 10° from horizontal. The stabilisation was achieved using three different soft-computing control techniques i.e. Proportional-integral-derivative (PID), Fuzzy logic and Adaptive neuro fuzzy inference system (ANFIS). A Matlab-Simulink model of the proposed system was initially developed which was further simulated using PID controllers based on trial and error method. The ANFIS controller were trained using data sets generated from simulation results of PID controller. The ANFIS controllers were designed using only three membership functions. A fuzzy logic control of the proposed system is also shown using nine membership functions. The study compares the three techniques in terms of settling time, maximum overshoot and steady state error. The simulation results are shown with the help of graphs and tables which validates the effectiveness of proposed techniques.

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Intelligent optimal control with dynamic neural networks

Cited by 77 publications

References 10 publications

Design of conventional and neural network based controllers for a single-shaft gas turbine

Design of conventional and neural network based controllers for a single-shaft gas turbine

One approach to control of a neural network with variable signal conductivity

A Pid Based Anfis & Fuzzy Control Of Inverted Pendulum On Inclined Plane (Ipip)

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