A Sliding Mode Strategy for Adaptive Learning in Adalines

Sira‐Ramírez, Hebertt; Colina-Morles, Eliezer

doi:10.1016/s1474-6670(17)58510-9

Cited by 15 publications

(23 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we consider a robust adaptive sliding mode controller for the same truck-trailer model as the one [6], but there is no state delays in delta operator system (23), so that we cannot compare our results with the one in [6]. However, it is well known that adaptive sliding mode controller has better control effect than the state feedback controller.…”

Section: Remarkmentioning

confidence: 99%

“…The SMC problem was considered for a class of delta operator system with time delays [22]. Adaptive control law could lead to a stable closed-loop system and the deviation from the sliding surface is bounded [23]. Adaptive output control of linear time-varying systems and adaptive control for linear nonstationary objects output were considered in [24,25], respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust adaptive sliding mode control for uncertain delta operator systems

Yang¹,

Xia²,

Fu³

et al. 2010

Adaptive Control & Signal

View full text Add to dashboard Cite

In this paper, a robust adaptive sliding mode controller is presented for delta operator systems with mismatched uncertainties and exogenous disturbances. The parameters of the delta operator system are taken for norm-bounded uncertainties. The exogenous disturbance is also assumed to be bounded. After the statement of a sufficient condition for the existence of linear sliding surface based on linear matrix inequality technique, a robust reaching motion control method for delta operator systems is presented. Afterwards, an adaptive sliding mode controller for delta operator systems is designed. A bridge between the robust adaptive sliding mode control and the delta operator system framework is made. Numerical example is given to illustrate the effectiveness of the developed techniques.

show abstract

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust adaptive sliding mode control for uncertain delta operator systems

Yang¹,

Xia²,

Fu³

et al. 2010

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…When the system of (13) and the reference dynamics of (14) are discretized with first order Euler approximation, one ends up with the generic representation of (3), and the approach discussed becomes applicable. The response of the uncontrolled system for several different initial conditions is illustrated together with the desired periodic orbit in Figure 2.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Some examples utilizing VSS theory have successfully demonstrated that the approach can be utilized for tracking control of uncertain systems and identification purposes [14,15]. The underlying idea is to integrate the robustness and invariance properties of SMC technique with the power of knowledge based systems like neural networks and fuzzy inference systems.…”

Section: Introductionmentioning

confidence: 99%

A robust on‐line learning algorithm for intelligent control systems

Efe

Kaynak

Wilamowski

et al. 2003

Adaptive Control & Signal

View full text Add to dashboard Cite

This paper describes a novel error extraction approach for exploiting the strength of LevenbergMarquardt (LM) optimization technique in intelligent control systems. Since the target value of the control signal is unknown, tuning of the controller parameters becomes a tedious task if the knowledge about the system and the environment is limited. The suggested methodology utilizes the sliding model control (SMC) technique. The error extraction scheme postulates the form of error on the applied control signal using the discrepancy from the prescribed reaching dynamics. The devised approach has been tested on the non-linear Duffing oscillator, which has been forced to follow a periodic orbit radically different from the natural one. The results obtained through a series of simulations have confirmed the high precision and robustness advantages without knowing the analytical details of the system under investigation. The issues of observation noise and the stability in the parametric space have approximately been addressed from the point of SMC perspective.

show abstract

“…An unfortunate choice of any of these parameters can result in very slow convergence, or even network paralysis where learning virtually stops. Some drawbacks of this algorithm are the tendency of the steepest descent technique, which is used in the training process, to get stuck at local minima, the need for a derivational function, and the long learning time [1][2][3]. A number of research studies have attempted to overcome these problems during recent years.…”

Section: Introductionmentioning

confidence: 99%

Learning method of the ADALINE using the fuzzy logic system

Eom

Kang

2005

Neural Comput & Applic

View full text Add to dashboard Cite

We propose a learning method for the ADA-LINE. The proposed method exploits fuzzy logic system for automatic tuning of the weights of the ADALINE. The inputs of the fuzzy logic system are error and change of error, and the output is the weight variation. We used same membership functions and different scaling factor for each weights. In order to verify the effectiveness of the proposed method, we performed the simulation and experimentation for the cases of the noise cancellation and the inverted pendulum control. The results show that the proposed method does not need the learning rate and the derivative, and improves the performance compared to the Widrow-Hoff delta rule for ADALINE.

show abstract

A Sliding Mode Strategy for Adaptive Learning in Adalines

Cited by 15 publications

References 7 publications

Robust adaptive sliding mode control for uncertain delta operator systems

Robust adaptive sliding mode control for uncertain delta operator systems

A robust on‐line learning algorithm for intelligent control systems

Learning method of the ADALINE using the fuzzy logic system

Contact Info

Product

Resources

About