Indirect adaptive dual control for Hammerstein systems using ANN

Knohl, Torsten; Xu, Wenxia; Unbehauen, H.

doi:10.1016/s0967-0661(02)00117-x

Cited by 18 publications

(12 citation statements)

References 12 publications

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“…Two types of multivariable models are discussed. Although Hammerstein models are frequently used for control [1,7,13,17,24,26,37,39], an inverse model is usually necessary. In the described algorithm the neural Hammerstein model is used on-line to find its local linearization and a nonlinear free trajectory.…”

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confidence: 99%

Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

Ławryńczuk

2010

Appl Intell

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This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which the neural Hammerstein model is used. The MultipleInput Multiple-Output (MIMO) dynamic model contains a neural steady-state nonlinear part in series with a linear dynamic part. The model is linearized on-line, as a result the MPC algorithm requires solving a quadratic programming problem, the necessity of nonlinear optimization is avoided. A neutralization process is considered to discuss properties of neural Hammerstein models and to show advantages of the described MPC algorithm. In practice, the algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimization.

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confidence: 99%

Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

Ławryńczuk

2010

Appl Intell

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“…If D > 0, only one of the roots is real (the first equation of (15)) and the other two are complex. The required solution to this real minimum is thus given by u 1 (k) in (15).…”

Section: The Control Lawmentioning

confidence: 99%

“…Generally speaking, dual adaptive approaches for extremum control have not been intensively investigated and the scope of related works published in this area is typically much narrower than the one presented here. For example [15] only addresses dual adaptive control of Hammerstein systems, with no reference to the extremum control problem. On the other hand [16] does consider extremum control but it only deals with static nonlinearities that are not of the Hammerstein type.…”

Section: Introductionmentioning

confidence: 99%

Dual adaptive extremum control of a Hammerstein system

Fabri

Wittenmark

Bugeja

2014

22nd Mediterranean Conference on Control and Automation

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This paper proposes a dual adaptive control methodology for extremum control of stochastic Hammerstein systems that are subject to uncertainty and characterised by a nonlinear second-order polynomial function. The design is based on an explicit-type innovations dual cost function that leads to a control law which balances out the need for caution when using uncertain parameter estimates to effect control actions, with the conflicting need to probe the system input so as to reduce parameter uncertainty. This extremum control problem is more challenging than conventional adaptive systems because the reference input is itself a non-linear function of the unknown system parameters. The controller's performance is analysed through extensive Monte Carlo simulations and shown to be superior to other types of adaptive control systems that use a certainty equivalence assumption.

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“…Knohl, Xu, and Unbehauen (2003) for example, consider dual adaptive control of Hammerstein systems but only with externally predefined reference inputs rather than extremum control. On the other hand, the work of Egardt and Larsson (2005) does consider adaptive extremum control but it is limited to a static nonlinear plant that lacks the dynamic element of Hammerstein models.…”

Section: Introductionmentioning

confidence: 99%

Dual adaptive extremum control of Hammerstein systems

Fabri

Wittenmark

Bugeja

2015

International Journal of Control

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A novel dual adaptive controller for extremum control of stochastic and uncertain nonlinear Hammerstein systems is proposed. The design is based on the innovations dual control cost function originally developed for conventional adaptive control of linear systems. However, the design process is extensively modified and developed so as to cater for the extremum control scenario. This is a more challenging problem because the reference input is itself a nonlinear function of the unknown system parameters, rather than an independent and predefined external reference signal. As in all dual adaptive schemes, it leads to a control law that balances out the need for caution, due to parameter uncertainty, with the conflicting requirement of probing that acts to quickly reduce parameter uncertainty. The proposed controller's performance is analysed through extensive Monte Carlo simulation trials and compared with several other non-dual adaptive extremum controllers. It is shown that the novel extremum innovations dual controller is superior to other types of adaptive control systems that are based on a certainty equivalence assumption. In addition, a novel statistical measure is introduced that yields a more objective evaluation of the Monte Carlo simulation results.

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Indirect adaptive dual control for Hammerstein systems using ANN

Cited by 18 publications

References 12 publications

Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models

Dual adaptive extremum control of a Hammerstein system

Dual adaptive extremum control of Hammerstein systems

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