On-line adaptive control for inverted pendulum balancing based on feedback-error-learning

Ruan, Xiaogang; Ding, Mingxiao; Gong, Daoxiong; Qiao, Junfei

doi:10.1016/j.neucom.2006.10.012

Cited by 42 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The purpose of the proportional feedback controller is to cancel the effect of unmeasured disturbances and to make the adaptive controller more robust during the initial training phase. 27 As the controller is initialized with no prior knowledge and the feedforward controller is unable to control the plant, the proportional controller offers acceptable performance during the initial phase. The use of a proportional controller with a very small gain reduces the effects of unmeasured disturbances and improves the control performance during the initial training phase but it is not essential.…”

Section: The Ffrm-based Direct Adaptive Controllermentioning

confidence: 99%

Fuzzy Relational Control of an Uncertain System

Kadri

Dexter

2014

Int. J. Unc. Fuzz. Knowl. Based Syst.

View full text Add to dashboard Cite

The paper considers the usefulness of a control strategy based on a fuzzy relational model of the controller to counteract uncertainties caused by measurement noise and unmeasured disturbances. The fuzzy relational model is identified using a combination of feedback error learning and fuzzy identification. An important feature of the resulting fuzzy relational model is that it will generate a fuzzy output in the presence of uncertainties. Two causes of uncertainty are considered separately, the first cause of uncertainty is due to the noise on the sensor measuring the controlled variable and the second one is an unmeasured input disturbance. Results are presented that show that the fuzzy control signal is representative of the uncertainties and that conditional defuzzification can then be used to improve the control performance by reducing the control activity. Keywords: Fuzzy control; direct adaptive control; fuzzy relational model. Int. J. Unc. Fuzz. Knowl. Based Syst. 2014.22:243-261. Downloaded from www.worldscientific.com by UNIVERSITY OF WATERLOO on 02/08/15. For personal use only. Int. J. Unc. Fuzz. Knowl. Based Syst. 2014.22:243-261. Downloaded from www.worldscientific.com by UNIVERSITY OF WATERLOO on 02/08/15. For personal use only.

show abstract

Section: The Ffrm-based Direct Adaptive Controllermentioning

confidence: 99%

Fuzzy Relational Control of an Uncertain System

Kadri

Dexter

2014

Int. J. Unc. Fuzz. Knowl. Based Syst.

View full text Add to dashboard Cite

show abstract

“…Figure 5 shows a schematic of an ANN model reference control system, where e c is the control error, and r is the reference. Occasionally, ANN model reference controllers are used together with feedback controllers [63] or as feedback controllers (with control error input) [64].…”

Section: Ann Model Reference Control Systemsmentioning

confidence: 99%

A critical review of the most popular types of neuro control

Mohammadzaheri

Chen

Grainger

2011

Asian Journal of Control

View full text Add to dashboard Cite

In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well-known control technique. This attitude towards the extension of the application of well-known control methods using ANNs was followed by the development of ANN model-predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well-known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed.Key Words: Control, neural network, adaptive, feedback linearization, predictive, model reference, perceptron, radial basis. I. ARTIFICIAL NEURAL NETWORKS USABLE IN CONTROLArtificial neural networks (ANNs) are special mathematical models inspired by human neural networks. An ANN usually includes neurons, connections and biases. Neurons are arranged in 'layers'. There are a variety of neural networks suitable for different purposes [1,2]. In neuro control, it is a difficult and unsolved problem to find the best ANN structure for each specific application; thus, a fairly large ANN is usually employed to deal with relatively complex approximation problems [3]. In this paper, the most common types of artificial neural networks in the area of control are introduced: multi layer perceptrons (MLPs) and radial basis function networks (RBFNs). Both of these are known as universal approximators of systems [4,5]. The advantage of RBFNs is their quick training process compared with MLPs; however, for complicated systems with many inputs, the number of neurons in RBFNs are usually considerably higher than MLPs [1,2]. Regardless of the main structure, if the output of an ANN depends not only on the current q

show abstract

“…For instance, combining a linear nominal controller with an adaptive compensator, Ruan (2007) and Hovakimyan (2006) realized the high performance stabilizing of inverted pendulum with un-modeling nonlinear dynamics. Since sliding mode control is robust to uncertainties of system structure and parameters, external disturbances and other unexpected factors, when the system lies on the sliding surface, it is obtained more and more attention in the control realm Young et al, 1999).…”

Section: Frontiers In Adaptive Control 132mentioning

confidence: 99%