Real time control of robot manipulator using a neural network based learning controller

Abstract In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control.

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Enhanced precision of real-time control photothermal therapy using cost-effective infrared sensor array and artificial neural network

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.104960 ◽

2021 ◽

pp. 104960

Author(s):

Duc Tri Phan ◽

Van Nam Tran ◽

Le Hai Tran ◽

Sumin Park ◽

Jaeyeop Choi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Real Time ◽

Photothermal Therapy ◽

Sensor Array ◽

Cost Effective ◽

Infrared Sensor ◽

Real Time Control ◽

Time Control ◽

Artificial Neural

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Development of a real-time control strategy with artificial neural network for automatic control of a continuous-flow sequencing batch reactor

Water Science & Technology ◽

10.2166/wst.2001.0023 ◽

2001 ◽

Vol 44 (1) ◽

pp. 95-104 ◽

Cited By ~ 17

Author(s):

B. C. Cho ◽

S.-L. Liaw ◽

C.-N. Chang ◽

R.-F. Yu ◽

S.-J. Yang ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Real Time ◽

Control Strategy ◽

Continuous Flow ◽

Prediction Models ◽

Process Models ◽

Real Time Control ◽

Time Control ◽

Artificial Neural

The purpose of this study is to develop a reliable and effective real-time control strategy by integrating artificial neural network (ANN) process models to perform automatic operation of a dynamic continuous-flow SBR system. The ANN process models, including ORP/pH simulation models and water quality ([NH4+-N] and [NOx--N]) prediction models, can assist in real-time searching the ORP and pH control points and evaluating the operation performances of aerobic nitrification and anoxic denitrification operation phases. Since the major biological nitrogen removal mechanisms were controlled at nitritification (NH4+-N→NO2--N) and denitritification (NO2--N→N2) stages, as well as the phosphorus uptake and release could be completely controlled during aerobic and anoxic operation phases, the system operation performances under this ANN real-time control system revealed that both the aeration time and overall hydraulic retention time could be shortened to about 1.9-2.5 and 4.8-6.2 hrs/cycle respectively. The removal efficiencies of COD, ammonia nitrogen, total nitrogen, and phosphate were 98%, 98%, 97%, and 84% respectively, which were more effective and efficient than under conventional fixed-time control approach.

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