Feedback linearization using neural networks

Yeşildirek, Aydın; Lewis, Frank L.

doi:10.1109/icnn.1994.374620

Cited by 70 publications

(99 citation statements)

References 10 publications

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“…4. The criteria of s Z ; s f 5ðg u À g m Þ=2g m and (11) together imply that s f =d; s Z =d5g=g m : Therefore, the control effort here is a g fraction of that in Reference [3]. 5.…”

Section: Proofmentioning

confidence: 99%

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A smooth switching adaptive controller for linearizable systems with improved transient performance

Huang

Chen

2006

Adaptive Control & Signal

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The certainty equivalent control has achieved asymptotic tracking stability of linearizable systems in the presence of parametric uncertainty. However, two major drawbacks remain to be tackled, namely, the risk of running into singularity for the calculated control input and the poor transient behaviour arising frequently in a general adaptive system. For the first problem, a high gain control is activated in place of the certainty equivalent control until the risk is bypassed. Among others, it requires less control effort by taking advantages of the bounds for the input vector field. Moreover, the switching mechanism is smooth and hence avoids possible chattering behaviour. Next, to solve the second problem, a new type of update algorithm guaranteeing the exponential stability of the overall closed-loop system, on a weaker persistent excitation (PE) condition, is proposed. In particular, it requires no filtering of the regressor and hence is easier to implement. Simulation results demonstrating the validity of the proposed design are given in the final.

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“…4. The criteria of s Z ; s f 5ðg u À g m Þ=2g m and (11) together imply that s f =d; s Z =d5g=g m : Therefore, the control effort here is a g fraction of that in Reference [3]. 5.…”

Section: Proofmentioning

confidence: 99%

“…However, the discontinuous nature of switching, which might cause chattering behaviour, should be avoided at the same time. Regarding this, a purely robust control with smooth transition [3] or a hysteresis-like switching adaptive control [4], etc. have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A smooth switching adaptive controller for linearizable systems with improved transient performance

Huang

Chen

2006

Adaptive Control & Signal

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“…If system uncertainty and external disturbances exist, then the results may be unstable. Therefore, a neural network is used to approximate the unknown functions (or uncertainty) to solve this problem [5,8,9,14,17,18,27,28]. Herein, we propose a wavelet-based neural network (WNN) system to enhance the approximation of the unknown functions.…”

Section: Introductionmentioning

confidence: 99%

Experimental implementation of nonlinear TORA system and adaptive backstepping controller design

Lee

Chang

2011

Neural Comput & Applic

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The purpose of this paper is to design an adaptive controller and system experimental implementation for nonlinear translational oscillations with a rotational actuator (TORA) system. A wavelet-based neural network (WNN) is proposed to develop an adaptive backstepping control scheme, called ABC WNN for TORA system. To ensure the stability of the controlled system, a compensated controller is designed to enhance the control performance. Based on its universal approximation ability, we use a WNN to estimate the system uncertainty including frictional forces, external disturbance, and parameter variance. According to the estimations of the WNNs, the ABC WNN control is developed via a backstepping design procedure such that the system outputs follow the desired trajectories. For system development, the effects of frictional forces are discussed and solved using the estimation of the WNN. The effectiveness of the proposed control scheme for TORA system is verified by numerical simulation and experimental results.

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“…The use of neural networks' learning ability avoids complex mathematical analysis in solving control problems when plant dynamics are complex and highly nonlinear, which is a distinct advantage over traditional control methods. As an alternative, intensive research has been carried out on neural networks control of unknown nonlinear systems [11][12][13][14][15][16][17][18]. This motivates some researches on combining neural networks with adaptive control techniques to develop decentralized control approaches for uncertain nonlinear systems with restrictions on interconnections.…”

Section: Introductionmentioning

confidence: 99%

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

Zhao

2007

Neural Comput & Applic

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For a class of large-scale decentralized nonlinear systems with strong interconnections, a radial basis function neural network (RBFN) adaptive control scheme is proposed. The system is composed of a class of nonaffine nonlinear subsystems, which are implicit function and smooth with respect to control input. Based on implicit function theorem, inverse function theorem and the design idea of pseudo-control, a novel control algorithm is proposed. Two neural networks are used to approximate unknown nonlinearities in the subsystem and unknown interconnection function, respectively. The stability is proved rigidly. The result of simulation validates the effectiveness of the proposed scheme.

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Feedback linearization using neural networks

Cited by 70 publications

References 10 publications

A smooth switching adaptive controller for linearizable systems with improved transient performance

A smooth switching adaptive controller for linearizable systems with improved transient performance

Experimental implementation of nonlinear TORA system and adaptive backstepping controller design

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

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