States and unknown input estimation via non-linear sliding mode high-gain observers for a glucose-insulin system

A meal-estimation algorithm is developed based on an extended mathematical model of the glucose-insulin system. The proposed model describes the dynamics of glucose levels in blood and in subcutaneous layer, as well as the meal intake which is considered an unknown input of the system. This model seeks to represent in a more realistic manner, the pancreas malfunction in patients with Type 1 Diabetes Mellitus. Based on model, a non-linear high gain observer (NHGO) with a sliding mode is designed in order to estimate the unmeasured states and the external disturbances of the system. This scheme is useful to maintain frequent monitoring of glucose levels and any changes in its behaviour. The unknown input or disturbance is estimated through the sliding mode based only the estimation error. Data from a real patient is used to evaluate the effectiveness of the proposed estimation scheme

Ecological Advanced Driver Assistance System for Optimal Energy Management in Electric Vehicles

Battery Electric Vehicles have a high potential in modern transportation, however, they are facing limited cruising range. The driving style, the road geometries including slopes, curves, the static and dynamic traffic conditions such as speed limits and preceding vehicles have their share of energy consumption in the host electric vehicle. Optimal energy management based on a semi-autonomous ecological advanced driver assistance system can improve the longitudinal velocity regulation in a safe and energy-efficient driving strategy. The main contribution of this paper is the design of a real-time risk-sensitive nonlinear model predictive controller to plan the online cost-effective cruising velocity in a stochastic traffic environment. The basic idea is to measure the relevant states of the electric vehicle at runtime, and account for the road slopes, the upcoming curves, and the speed limit zones, as well as uncertainty in the preceding vehicle behavior to determine the energy-efficient velocity profile. Closed-loop Entropic Value-at-Risk as a coherent risk measure is introduced to quantify the risk involved in the system constraints violation. The obtained simulation and field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of safe and energy-efficient states regulation and constraints satisfaction

Fast Stochastic Non-linear Model Predictive Control for Electric Vehicle Advanced Driver Assistance Systems

Semi-autonomous driving assistance systems have a high potential to improve the safety and efficiency of the battery electric vehicles that are enduring limited cruising range. This paper presents an ecologically advanced driver assistance system to extend the functionality of the adaptive cruise control system. A real-time stochastic non-linear model predictive controller with probabilistic constraints is presented to compute on-line the safe and energy-efficient cruising velocity profile. The individual chance-constraint is reformulated into a convex second-order cone constraint which is robust for a general class of probability distributions. Finally, the performance of proposed approach in terms of states regulation, constraints fulfilment, and energy efficiency is evaluated on a battery electric vehicle

Risk-averse Stochastic Nonlinear Model Predictive Control for Real-time Safety-critical Systems

Stochastic nonlinear model predictive control has been developed to systematically find an optimal decision with the aim of performance improvement in dynamical systems that involve uncertainties. However, most of the current methods are risk-neutral for safety-critical systems and depend on computationally expensive algorithms. This paper investigates on the risk-averse optimal stochastic nonlinear control subject to real-time safety-critical systems. In order to achieve a computationally tractable design and integrate knowledge about the uncertainties, bounded trajectories generated to quantify the uncertainties. The proposed controller considers these scenarios in a risk-sensitive manner. A certainty equivalent nonlinear model predictive control based on minimum principle is reformulated to optimise nominal cost and expected value of future recourse actions. The capability of proposed method in terms of states regulations, constraints fulfilment, and real-time implementation is demonstrated for a semi-autonomous ecological advanced driver assistance system specified for battery electric vehicles. This system plans for a safe and energy-efficient cruising velocity profile autonomously

Stochastic Optimum Energy Management for Advanced Transportation Network

Smart and optimal energy consumption in electric vehicles has high potential to improve the limited cruising range on a single battery charge. The proposed concept is a semi-autonomous ecological advanced driver assistance system which predictively plans for a safe and energy-efficient cruising velocity profile autonomously for battery electric vehicles. However, high entropy in transportation network leads to a challenging task to derive a computationally efficient and tractable model to predict the traffic flow. Stochastic optimal control has been developed to systematically find an optimal decision with the aim of performance improvement. However, most of the developed methods are not real-time algorithms. Moreover, they are mainly risk-neutral for safety-critical systems. This paper investigates on the real-time risk-sensitive nonlinear optimal control design subject to safety and ecological constraints. This system improves the efficiency of the transportation network at the microscopic level. Obtained results demonstrate the effectiveness of the proposed method in terms of states regulation and constraints satisfaction

On the simultaneous stabilization of three or more plants

International audienceIn this paper, the problem of the simultaneous stabilization of three multivariable plants is addressed. We consider the general case where the existence of a unit controller cannot be used as a sufficient condition to guarantee the existence of a simultaneous controller for three multivariable plants. The sufficient conditions given in this paper lead to a constructive controller design to stabilize simultaneously three multivariable plants. A generalization is proposed for stabilizing simultaneously n multivariable plants

Generalized H∞ observers design for systems with unknown inputs

Abstract-A generalized H ∞ observers design is proposed for linear systems with unknown inputs. It generalizes the existing results concerning the proportional observer (PO) design and the proportional integral observer (PIO) design. The approach is based on the solutions of the algebraic constraints obtained from the unbiasedness conditions of the estimation error. The observer design is obtained from the solutions of linear matrix inequalities (LMIs). A numerical example is given to illustrate our approach

Adaptive control for a lightweight robotic arm actuated by a Shape Memory Allow wire

This paper presents the design, model and closed-loop control of a single degree-of-freedom (DOF) lightweight robotic arm actuated by a biased Shape Memory Alloy (SMA) wire. The highly non-linear dynamics of SMAs represent a challenge for control tasks, due to phenomena as hysteresis or parameters uncertainty. With this in mind, we propose a control capable to adapt itself to the hysteretic behavior and update its behavior to deal with the changing parameters of the material over time. An adaptive control for position regulation is presented. This control includes a set of techniques, providing a systematic way to adjust the control parameters in real time, so maintaining the stability of the system and a desired performance, while dealing with parameter and model uncertainties. The closed-loop approach is tested in experimentally showing its effectiveness to deal with the highly non-linear dynamics of the SMA wire

Observer design for Lightweight Robotic arm actuated by Shape Memory Alloy (SMA) wire

International audienceThis paper presents the design of an observer for a lightweight robotic arm actuated by a single biased Shape Memory Alloy (SMA) wire. The internal states of the system are estimated via an Extended Kalman Filter (EKF) with sliding modeunknown input and states estimation. This observer allows estimating the temperature, stress and martensite fraction rate of the SMA wire. This approach avoids the use of switching dynamics in the observer's model, due to the martensite fraction being considered as the unknown input. The discretized model of the robotic arm and observer development is presented. Finally the effectiveness of the proposed observer is tested in simulation