Neuro-PID control for inverted single and double pendulums

Omatu, Sigeru; Fujinaka, Toru; Yoshioka, Michifumi

doi:10.1109/icsmc.2000.884401

Cited by 37 publications

(16 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A neural network controller has been known as a powerful nonlinear controller so it can be used as a nonlinear controller by itself [11][12][13][14]. Combing the neural network with the fuzzy controller is expected to yield the better performance since merits of two intelligent tools are used.…”

Section: Rct Control Schemementioning

confidence: 99%

See 1 more Smart Citation

Experimental Studies of Neural Compensation Technique for a Fuzzy Controlled Inverted Pendulum System

Lee¹,

Jung

2010

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

This article presents the experimental studies of controlling angle and position of the inverted pendulum system using neural network to compensate for errors caused due to fuzzy controller. Although fuzzy control method can deal with nonlinearities of the system, fixed fuzzy rules may not work and result in tracking errors in some cases. First, a nominal Takagi-Sugeno (TS) type fuzzy controller with fixed weights is used for controlling the inverted pendulum system. Then the neural network is added at the reference input to form the reference compensation technique (RCT)control structure. Neural network modifies the input trajectories to improve system performances by updating internal weights in on-line fashion. The back-propagation learning algorithm for neural network is derived and used to update weights. Control hardware of a DSP 6713 board to have real time control is implemented. Experimental results of controlling inverted pendulum system are conducted and performances are compared.

show abstract

Section: Rct Control Schemementioning

confidence: 99%

“…We add a separate neural network to change fuzzy rules by modifying reference input values [10]. This forms the reference compensation technique control method as one of neural network control methods that has been used for controlling the inverted pendulum system [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Experimental Studies of Neural Compensation Technique for a Fuzzy Controlled Inverted Pendulum System

Lee¹,

Jung

2010

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Among many advanced control methods, an intelligent control method is one of the promising tools for controlling complicated systems, including robots and mechatronics systems. Many successful control performances by intelligent control algorithms, including fuzzy logic and neural network control, have been reported in the literature [1,3,9,10,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Inverted pendulum systems with multiple serial linkage structures have been presented to solve challenging control performance by mimicking an acrobat system with two links [7][8][9][10][11][12][13]. Simulation studies of the swing up motion of an acrobat have been performed using the partial feedback linearization method [11] and the energy-based control method [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experimental Studies of Swing Up and Balancing Control of an Inverted Pendulum System Using Intelligent Algorithms Aimed at Advanced Control Education

Ahn¹,

Jung

2014

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

This paper presents the control of an inverted pendulum system using intelligent algorithms, such as fuzzy logic and neural networks, for advanced control education. The swing up balancing control of the inverted pendulum system was performed using fuzzy logic. Because the switching time from swing to standing motion is important for successful balancing, the fuzzy control method was employed to regulate the energy associated with the angular velocity required for the pendulum to be in an upright position. When the inverted pendulum arrived within a range of angles found experimentally, the control was switched from fuzzy to proportional-integral-derivative control to balance the inverted pendulum. When the pendulum was balancing, a joystick was used to command the desired position for the pendulum to follow. Experimental results demonstrated the performance of the two intelligent control methods.

show abstract

Neural network‐based PID gain tuning of chemical plant controller

Abe

Konishi

Imai

et al. 2010

Electrical Engineering Japan

View full text Add to dashboard Cite

Plant control systems are now highly automated and are used in many industries. The control performance changes with the passage of time because of deterioration of plant facilities. For this reason, human experts tune the control system to improve overall plant performance. In this study, a PID control system for the oil refining chemical plant process is discussed. In oil refining, thousands of control loops are used in plants in order to keep the product quality at the desired value and to assure the safety of plant operation. Due to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using a neuro emulator and a recurrent neural networks model (RNN model) for emulation and tuning of parameters, a PID gain tuning system for a chemical plant controller is constructed. Numerical experiments using actual plant data demonstrate the effect of the proposed method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(4): 28–36, 2010; Published online in Wiley InterScience (http://www.interscience.wiley.com). DOI 10.1002/eej.20973

show abstract

Neuro-PID control for inverted single and double pendulums

Cited by 37 publications

References 2 publications

Experimental Studies of Neural Compensation Technique for a Fuzzy Controlled Inverted Pendulum System

Experimental Studies of Neural Compensation Technique for a Fuzzy Controlled Inverted Pendulum System

Experimental Studies of Swing Up and Balancing Control of an Inverted Pendulum System Using Intelligent Algorithms Aimed at Advanced Control Education

Neural network‐based PID gain tuning of chemical plant controller

Contact Info

Product

Resources

About