Robust control of drug infusion with time delays using direct adaptive control: experimental results

Palerm, Cesar C.; Bequette, B. Wayne; Özçelik, Selahattin

doi:10.1109/acc.2000.879110

Cited by 17 publications

(9 citation statements)

References 12 publications

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“…1. The model is expressed in the s ‐frequency domain, using transfer function matrices

()(, \begin{matrix} 1em4pt \\ normalΔ CO false(s false) \\ normalΔ BP false(s false) \end{matrix}) = ()(, \begin{matrix} 1em4pt \\ \frac{K_{11} e^{- T_{11} s}}{τ_{11} s + 1} & \frac{K_{12} e^{- T_{12} s}}{τ_{12} s + 1} \\ \frac{K_{21} e^{- T_{21} s}}{τ_{21} s + 1} & \frac{K_{22} e^{- T_{22} s}}{τ_{22} s + 1} \end{matrix}) ()(, \begin{matrix} 1em4pt \\ DP false(s false) \\ SNP false(s false) \end{matrix})

Indicative values for the parameters of this model are [10–13]: K 11 :5 ml/μ, with variation ranges between 1.0 and 12 ml/μ, τ 11 :300 s, with variation ranges between 70 and 600 s, T 11 :60 s, with variation ranges between 15 and 60 s.…”

Section: Dynamic Model Of the Cardiac Functionmentioning

confidence: 99%

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Flatness‐based control approach to drug infusion for cardiac function regulation

2017

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A new control method based on differential flatness theory is developed in this study, aiming at solving the problem of regulation of haemodynamic parameters. Actually control of the cardiac output (volume of blood pumped out by heart per unit of time) and of the arterial blood pressure is achieved through the administered infusion of cardiovascular drugs such as dopamine and sodium nitroprusside. Time delays between the control inputs and the system's outputs are taken into account. Using the principle of dynamic extension, which means that by considering certain control inputs and their derivatives as additional state variables, a state-space description for the heart's function is obtained. It is proven that the dynamic model of the heart is a differentially flat one. This enables its transformation into a linear canonical and decoupled form, for which the design of a stabilising feedback controller becomes possible. The proposed feedback controller is of proven stability and assures fast and accurate tracking of the reference setpoints by the outputs of the heart's dynamic model. Moreover, by using a Kalman filter-based disturbances' estimator, it becomes possible to estimate in real-time and compensate for the model uncertainty and external perturbation inputs that affect the heart's model.

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“…1. The model is expressed in the s ‐frequency domain, using transfer function matrices

()(, \begin{matrix} 1em4pt \\ normalΔ CO false(s false) \\ normalΔ BP false(s false) \end{matrix}) = ()(, \begin{matrix} 1em4pt \\ \frac{K_{11} e^{- T_{11} s}}{τ_{11} s + 1} & \frac{K_{12} e^{- T_{12} s}}{τ_{12} s + 1} \\ \frac{K_{21} e^{- T_{21} s}}{τ_{21} s + 1} & \frac{K_{22} e^{- T_{22} s}}{τ_{22} s + 1} \end{matrix}) ()(, \begin{matrix} 1em4pt \\ DP false(s false) \\ SNP false(s false) \end{matrix})

Section: Dynamic Model Of the Cardiac Functionmentioning

confidence: 99%

“…In [5][6][7][8][9], adaptive and robust control approaches have been proposed for models of heart function. In [10][11][12][13], the problem of control of haemodynamic models under time delays has been treated. Moreover, in [14][15][16][17] intelligent control methods for the regulation of cardiac function have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Flatness‐based control approach to drug infusion for cardiac function regulation

2017

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“…It is worth noting that, initially, SAC was meant to be a modest alternative to MRAC with apparently very modest aims and that also seemed to be very restricted by new conditions. Although at the time it probably was the only adaptive technique that could have been used in MIMO systems and with large systems, and therefore was quite immediately adopted by many researchers and practitioners in such diverse applications as flexible structures , flight control , power systems , robotics , motor control , drug infusion , and others , the SAC approach obtained a cold reception and for a long time has been largely ignored by the mainstream adaptive control.…”

Section: Simple Adaptive Control or The Simplified Approach To Modelmentioning

confidence: 99%

Simple adaptive control – a stable direct model reference adaptive control methodology – brief survey

Barkana¹

2013

Adaptive Control & Signal

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In spite of successful proofs of stability and even successful demonstrations of performance, the eventual use of model reference adaptive control methodologies in practical real-world systems has met a rather strong resistance from practitioners and has remained very limited. Apparently, the practitioners have a hard time understanding the conditions that can guarantee stable operations of adaptive control systems under realistic operational environments. Besides, it is difficult to measure the robustness of adaptive control system stability and allow it to be compared with the common and widely used measure of phase margin and gain margin that is utilized by present, mainly LTI, controllers. Furthermore, recent counterexamples seem to show that adaptive systems may diverge even when all required conditions are fulfilled. This paper attempts to revisit the fundamental qualities of the common direct model reference adaptive control methodology based on gradient and to show that some of its basic drawbacks have been addressed and eliminated within the so-called simple adaptive control methodology. The sufficient conditions that guarantee stability are clearly stated and lead to similarly clear proofs of stability. The main claim of the paper is that if sufficient information exists for a robust classical design control, same information can be used to implement robust simple adaptive controllers. As many real world applications show, the added value of using add-on adaptive control techniques, the use is pushing the desired performance beyond any previous limits. The paper also shows that the previous counterexamples to model reference adaptive control become just simple, successful, and stable applications of simple adaptive control.

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“…4,5 These techniques have also been extended by Wenn and Balas 6 and Balas 7 to infinite-dimensional systems. Those successful applications of low-order adaptive controllers to large-scale examples have led to successful implementations of SAC in such diverse applications as flexible structures, 8−15 flight control, 16,17 power systems, 18 robotics, 19 motor control, 20,21 drug infusion, 22,23 and other. 24 SAC methodology had started with an apparently restricted range of applications because it seemed to be feasible only for step input commands and required the controlled plant to be almost strictly passive (ASP) and its transfer function almost strictly positive real (ASPR).…”

Section: Introductionmentioning

confidence: 99%

Classical and Simple Adaptive Control for Nonminimum Phase Autopilot Design

Barkana

2005

Journal of Guidance, Control, and Dynamics

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A recent publication uses a difficult design example to show that fuzzy logic might have advantages when compared with classical compensators. Although in this particular case the application was shown to be successful, convergence of the fuzzy-logic algorithm, as well as other time-varying controllers, cannot not be guaranteed unless some preliminary conditions are satisfied. It will be shown that further exploitation of the classical design can improve robust performance. This result is then used to create sufficient conditions that guarantee convergence with time-varying controllers, and it is then shown that simple adaptive control methods can further improve performance and maintain it in changing environments.

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Robust control of drug infusion with time delays using direct adaptive control: experimental results

Cited by 17 publications

References 12 publications

Flatness‐based control approach to drug infusion for cardiac function regulation

Flatness‐based control approach to drug infusion for cardiac function regulation

Simple adaptive control – a stable direct model reference adaptive control methodology – brief survey

Classical and Simple Adaptive Control for Nonminimum Phase Autopilot Design

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