A two-layer recurrent neural network for real-time control of redundant manipulators with torque minimization

2002 ◽  
Author(s):  
Wai-Sum Tang ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Redundant Manipulators ◽  
Real Time Control ◽  
Time Control

Neural-Network Based Approach for Real-Time Control of BMEP and MFB50 in a Euro 6 Diesel Engine

2017 ◽  
Author(s):  
Roberto Finesso ◽  
Ezio Spessa ◽  
Yixin Yang ◽  
Giuseppe Conte ◽  
Gennaro Merlino
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Real Time ◽  
Real Time Control ◽  
Time Control

Multi-dimensional Taylor network modelling and optimal control of SISO nonlinear systems for tracking by output feedback

2019 ◽  
Vol 37 (3) ◽  
pp. 699-717 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Real Time Control ◽  
Network Modelling ◽  
Single Input Single Output ◽  
Time Control

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.

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

2001 ◽  
Vol 44 (1) ◽  
pp. 95-104 ◽  
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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