Proceedings of the IFAC World Congress. International Federation of Automatic Control. World Congress最新文献

Single particle tracking (SPT) is a method to study the transport of biomolecules with nanometer resolution. Unfortunately, recent reports show that systematic errors in position localization and uncertainty in model parameter estimates limits the utility of these techniques in studying biological processes. There is a need for an experimental method with a known ground-truth that tests the total SPT system (sample, microscope, algorithm) on both localization and estimation of model parameters. Synthetic motion is a known ground-truth method that moves a particle along a trajectory. This trajectory is a realization of a Markovian stochastic process that represents models of biomolecular transport. Here we describe a platform for creating synthetic motion using common equipment and well-known, simple methods that can be easily adopted by the biophysics community. In this paper we describe the synthetic motion system and calibration to achieve nanometer accuracy and precision. Steady state input-output characteristics are analyzed with both line scans and grid scans. The resulting relationship is described by an affine transformation, which is inverted and used as a prefilter. Model inverse feed forward control is used to increase the system bandwidth. The system model was identified from frequency response function measurements using an integrated stepped-sine with coherent demodulation built into the FPGA controller. Zero magnitude error tracking controller method was used to invert non-minimum phase zeros to achieve a stable discrete time feed forward filter.

A fault-tolerant system is able to reach its goal even when some of its components are malfunctioning. This paper examines tolerance to a specific type of malfunction: the loss of control authority over actuators. Namely, we investigate whether the desired target set for a linear system remains reachable under any undesirable input. Contrary to robust control, we assume that the undesirable inputs can be observed in real time, and subsequently allow the control inputs to depend on these undesirable inputs. Building on previous work on reachability with undesirable inputs, this paper develops a reachability condition for linear systems, and obtains a formula that describes reachability of the goal set for driftless linear systems by computing the minimum of a concave-convex objective function. From this formulation we establish two novel sufficient conditions for resilient reachability.

Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady state response with respect to PID. The numerical analysis framework and findings reported here can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.

Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.