Functional Adaptive Control: An Intelligent Systems Approach

Fabri, Simon G.; Kadirkamanathan, Visakan

doi:10.1115/1.1508146

Cited by 58 publications

(105 citation statements)

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“…It is assumed in this section that the nonlinear functions f and g of model (2) are known and which, by virtue of the choice of coefficients i μ is asymptotically stable (Fabri & Kadrikamanathan, 2001;Tzirkel-Hancock & Fallside 1992). Since substitution in the system (2) the control law ) (…”

Section: The Case Of Exact Modelmentioning

confidence: 99%

“…some elementary knowledge of the model is assumed. The assumption above may, however, be substantially relaxed via applying, as basis functions, some sort of known approximators (Fabri & Kadrikamanathan, 2001;Tzirkel-Hancock & Fallside, 1992). As an example one may adopt a neuro-approximator with Gaussian radial basis functions (Sanner & Slotine, 1992).…”

Section: Introductionmentioning

confidence: 99%

“…Considering the system (1) it is possible (under certain conditions (Fabri & Kadrikamanathan, 2001;) to obtain a direct input-output relation between u and y, by successive differentiation y with respect of time having u g f y r ) ( ) ( ) (…”

Section: Introductionmentioning

confidence: 99%

“…As an example one may adopt a neuro-approximator with Gaussian radial basis functions (Sanner & Slotine, 1992). Systems of this sort are referred to as functional adaptive (Fabri & Kadrikamanathan, 2001) and represent a new branch of intelligent control systems. In the real-world applications, however, it seems purposeful to assume that we have at our disposal some (often very limited) knowledge, on the considered plant or process, that should be exploited in reasonable way.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nonlinear Adaptive Tracking-Control Synthesis for General Linearly Parametrized Systems

Zwierzewicz¹

2008

Automation and Robotics

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Section: The Case Of Exact Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear Adaptive Tracking-Control Synthesis for General Linearly Parametrized Systems

Zwierzewicz¹

2008

Automation and Robotics

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“…Therefore, predominantly linear stochastic systems with Gaussian random variables were studied and the performance objectives concerned were confined to be either mean value or variance of the stochastic output (Anderson and Moore, 1971;Niu et al, 2008;Solo, 1990;Everdij and Blom, 1996;Blackmore et al, 2010). For practical control systems where either the system variables or the noise are not Gaussian, the mean and variance of system outputs may not be sufficient to characterise the closed loop system behavior (Fabri and Kadirkamanathan, 2001;Wang and Afshar, 2009;Herzallah, 2012). As a result, several groups of control methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise

Herzallah

2015

Neural Networks

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Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained.

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Nonlinear adaptive tracking‐control synthesis for functionally uncertain systems

Zwierzewicz

2009

Adaptive Control & Signal

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The paper is concerned with the problem of adaptive tracking system control synthesis. It is assumed that a nonlinear, feedback linearizable object dynamics (model structure) is (partially) unknown and some of its nonlinear characteristics can be approximated by a sort of functional approximators. It has been proven that proportional state feedback plus parameter adaptation are able to assure its asymptotic stability. This form of controller permits online compensation of unknown model nonlinearities and exogenous disturbances, which results in satisfactory tracking performance. An interesting feature of the system is that the whole process control is performed without requisite asymptotic convergence of approximator parameters to the postulated 'true' values. It has been noticed that the parameters play rather a role of slack variables on which potential errors (that otherwise would affect the state variables) cumulate. The system's performance has been tested via Matlab/Simulink simulations via an example of ship path-following problem.

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Functional Adaptive Control: An Intelligent Systems Approach

Cited by 58 publications

References 0 publications

Nonlinear Adaptive Tracking-Control Synthesis for General Linearly Parametrized Systems

Nonlinear Adaptive Tracking-Control Synthesis for General Linearly Parametrized Systems

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise

Nonlinear adaptive tracking‐control synthesis for functionally uncertain systems

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