scholarly journals Adaptive control of two-wheeled mobile balance robot capable to adapt different surfaces using a novel artificial neural network–based real-time switching dynamic controller

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141770089 ◽  
Author(s):  
Ali Unluturk ◽  
Omer Aydogdu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Adaptive Controller ◽  
Linear Displacement ◽  
Real Time Control ◽  
Development Environment ◽  
Dynamic Controller ◽  
Switching Dynamic ◽  
Artificial Neural

In this article, a novel real-time artificial neural network–based adaptable switching dynamic controller is developed and practically implemented. It will be used for real-time control of two-wheeled balance robot which can balance itself upright position on different surfaces. In order to examine the efficiency of the proposed controller, a two-wheeled mobile balance robot is designed and a test platform for experimental setup is made for balance problem on different surfaces. In a developed adaptive controller algorithm which is capable to adapt different surfaces, mean absolute target angle deviation error, mean absolute target displacement deviation error and mean absolute controller output data are employed for surface estimation by using artificial neural network. In a designed two-wheeled mobile balance robot system, robot tilt angle is estimated via Kalman filter from accelerometer and gyroscope sensor signals. Furthermore, a visual robot control interface is developed in C++ software development environment so that robot controller parameters can be changed as desired. In addition, robot balance angle, linear displacement and controller output can be observed online on personal computer. According to the real-time experimental results, the proposed novel type controller gives more effective results than the classic ones.

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.

scholarly journals Predictive Neural Network in Multipurpose Self-Tuning Controller

2020 ◽  
Vol 14 (2) ◽  
pp. 114-120
Author(s):  
Oleksiy Bondar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Adaptive Controller ◽  
Development Environment ◽  
Wide Range ◽  
Artificial Neural ◽  
Arbitrary Precision ◽  
The Right

AbstractA very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.

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