A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems

The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.

Derivative-Based Sampled Data Control for Continuous Linear Parameter Varying System With Unknown Parameters

This paper deals with the robust stabilization of a class of linear parameter varying (LPV) systems in the sampled data control case. Instead of using a state observer or searching for a dynamic output feedback, the considered controller is based on output derivatives estimation. This allows the stabilization of the plant with very large parameter variations or uncertainties. The proof of stability is based on the polytopic representation of the closed-loop under Lyapunov conditions and system transformations. The result is a control structure with only one parameter tuned via very simple conditions. Finally, the effectiveness of the proposed method is verified via a numerical example of a second-order LPV system.

The Rollover Risk in Delta Tricycles: A New Rollover Index and Its Robust Mitigation by Rear Differential Braking

Although there are efforts to electrify and diversify small vehicles, active safety on motorcycles and tricycles (also known as auto rickshaw, tuk-tuk, mototaxi, etc.) has been relegated until a few years ago. For instance, the electric tricycles (even the combustion ones) marketed today do not have an active safety system that prevents or mitigates the risk of rollover, despite how prone they are to such a situation. The concern for the increase in its marketing is latent and, unfortunately, there are very few related studies. In this article, we present the obtaining and validation of a new rollover index for tricycles demonstrating its effectiveness in predicting and detecting the risk, even statically, by means of a simple quantity. In addition, a controller for the mitigation of the risk of rollover is presented which, by means of a Lyapunov type analysis, it is shown to be robust to changes in parameters, such as the center of gravity height, using a polytopic representation of the system and a differential braking strategy on the rear wheels. Numerical simulations, including video simulation captures, of the operation of the rollover mitigation system using widely recognized commercial software, are also presented. This work can be extended to vehicles with a suspension system or for trikes without autocamber.

A Reconfigurable Buck, Boost, and Buck-Boost Converter: Unified Model and Robust Controller

The need for reconfigurable, high power density, and low-cost configurations of DC-DC power electronic converters (PEC) in areas such as the transport electrification and the use of renewable energy has spread out the requirement to incorporate in a single circuit several topologies, which generally result in an increment of complexity about the modeling, control, and stability analyses. In this paper, a reconfigurable topology is presented which can be applied in alterative/changing power conversion scenarios and consists of a reconfigurable Buck, Boost, and Buck-Boost DC-DC converter (RBBC). A unified averaged model of the RBBC is obtained, a robust controller is designed through a polytopic representation, and a Lyapunov based switched stability analysis of the closed-loop system is presented. The reported RBBC provides a wide range of voltage operation, theoretically from -∞ to ∞ volts with a single power source. Robust stability, even under arbitrarily fast (bounded) parameter variations and reconfiguration changes, is reported including numerical and experimental results. The main advantages of the converter and the robust controller proposed are simple design, robustness against abrupt changes in the parameters, and low cost.

Fault Reconstruction and Accommodation in Linear Parameter-Varying Systems via Learning Unknown-Input Observers

This paper addresses the problem of observer-based fault reconstruction and accommodation for polytopic linear parameter-varying (LPV) systems. A polytopic representation of an LPV system subject to actuator faults and external disturbances is first established; then, a novel polytopic learning unknown-input observer (LUIO) is constructed for simultaneous state estimation and robust fault reconstruction. The stability of the presented LUIO is proved using Lyapunov stability theory together with H∞ techniques. Further, using reconstructed fault information, a reconfigurable fault-tolerant controller is designed to compensate for the influence of actuator faults by stabilizing the closed-loop system. At last, an aircraft example is employed to illustrate the effectiveness and practicability of the proposed techniques.