Darine Zambrano

Internal model sliding mode control approach for glucose regulation in type 1 diabetes

Abstract Patients with type 1 diabetes require insulin therapy to maintain blood glucose levels within safe ranges since their pancreas is unable to complete its function. The development of a closed-loop artificial pancreas capable of maintaining normoglycemia during daily life will dramatically improve the quality of life for insulin-dependent diabetic patients. In this work, a closed-loop control strategy for blood glucose level regulation in type 1 diabetic patients is presented. A robust controller is designed using a combination of internal model and sliding mode control techniques. Also, the controller is provided with a feedforward loop to improve meal compensation. A simulation environment designed for testing the artificial pancreas control algorithms has been used to evaluate the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model–patient mismatch and meal estimation errors.

A robust sliding mode controller with internal model for closed-loop artificial pancreas

The study presents a robust closed-loop sliding mode controller with internal model for blood glucose control in type-1 diabetes. Type-1 diabetic patients depend on external insulin delivery to keep their blood glucose within near-normal ranges. Closed-loop artificial pancreas is developed to help avoid dangerous, potentially life-threatening hypoglycemia, as well as to prevent complication-inducing hyperglycemia. The proposed controller is designed using a combination of sliding mode and internal model control techniques. To enhance postprandial performance, a feedforward controller is added to inject insulin bolus. Simulation studies have been performed to test the controller, which revealed that the proposed control strategy is able to control the blood glucose well within the safe limits in the presence of meals and measurements errors. The controller shows acceptable robustness against changes in insulin sensitivity, model–patient mismatch, and errors in estimating meal’s contents.

A solar cooling plant: A benchmark for hybrid systems control

Abstract This paper describes the hybrid model of a solar cooling plant. This model considers all possible operating modes of the process, which are modelled as a finite state machine whose transition conditions are given by the discrete variables. The discrete variables are the electrovalves and pumps. The model has been written as a mixed logical dynamical system and is simulated using Stateflow/Simulink Matlab. The model has been validated using real data from the plant. This plant is being used as a benchmark for hybrid control experiences by many European researchers in the framework of the HYCON Network of Excellence.

Application of MPC with multiple objective for a solar refrigeration plant

This paper presents a multiobjective model predictive control algorithm which allows the specification of different control goals at different operating points. The algorithm is illustrated with an application to a very detailed model of a solar cooling plant. The plant is used to cool down an office building with varying operating conditions. The plant is a nonlinear multivariable process subject to high disturbances and changing operating loads.

Hierarchical Control of a Hybrid Solar Air Conditioning Plant

This paper describes the development and practical application of a hierarchical scheme for the global control of a solar air conditioning plant. The plant is a variable configuration hybrid process characterised by nonlinearities and time delays, which uses two energy sources for its daily operation, namely solar energy and gas. A control algorithm able to handle these characteristics ensures that the plant gets constantly reconfigured for the most suitable operating mode. The envisaged global control encompasses the starting and stopping phases, as well as operating the plant with and without cooling demand. The hierarchical structure proposed is composed of two main levels, namely the configuration and the regulatory control levels. The configuration level selects the operating mode by means of minimizing a linear function with variable weights. The weights assigned depend on the current state of the plant and on the weather conditions, since the main energy source is solar radiation and it directly influences the selected operating mode. The regulatory control level instead adjusts the variables of the process related to each operating mode by using model predictive control in several structures of control loops. We show the results of the implementation of the hierarchical scheme over the real plant. The scheme exhibits a satisfactory behaviour even in presence of adverse weather conditions, thus proving able to satisfy the cooling demand throughout the day.

Identification of a discrete-time nonlinear Hammerstein-Wiener model for a selective catalytic reduction system

This paper deals with the identification of the nitrogen oxide emissions (NOx) from vehicles using the selective catalyst as an after treatment system for its reduction. The process is nonlinear, since the chemical reactions involved are highly depending on the operating point. The operating point is defined by the driving profile of the vehicle, which includes for example, cold and hot engine starts, highway, and urban driving. The experimental data used in this paper are based on a standard transient test developed for Euro VI testing. Real measurements of NOx inlet concentration, injected urea, inlet temperature and exhaust flow are used as inputs to a detailed simulator. NOx output concentration from the simulator is used as output, so there is no interference from the ammonia concentration in the NOχ output concentration due to cross-sensitivity. Experimental data are properly divided into identification and validation data sets. A Hammerstein-Wiener model is identified and it represents the dynamics very well. The best fits achieved with this model are 78.64% and 68.05% for the identification and validation data, respectively. Nonlinear static functions are selected from the knowledge and analysis of a selective catalytic reduction first principles based model. Identified linear models are able to represent the NOx emission with a fit of 68.93% and 38.92% for the identification and validation data, respectively.

SLIDING MODE PREDICTIVE CONTROL FOR CHEMICAL PROCESS WITH TIME DELAY

Abstract A design of a novel model predictive controller is presented. The proposed Sliding Mode Predictive Control (SMPC) algorithm combines the design technique of Sliding-Mode Control (SMC) with Model based Predictive Control (MPC). The SMPC showed a considerable robustness improvement with respect to MPC in the presence of time delay, and showed an enhanced ability to handle set point changes in a nonlinear process. Its robustness was evaluated using a robustness plot, its performance was judged using a single input single output nonlinear mixing tank process with variable time delay.

Nonlinear black box identification of a selective catalytic reduction system

Abstract This paper discusses the identification of linear and non-linear black-box models for describing a diesel engine selective catalytic reduction (SCR) system. SCR aftertreatment systems form an important technology for reducing the NO x produced by diesel engines, and therefore good models are essential for the control of these systems. This paper compares a linear and a non-linear model for identification of the system. The output signals of the SCR were generated from 4 measured input signals, using a simulated 18 state model. The experiments with a recursive prediction error method, RPEM, with only 2 states show that the system can be accurately approximated with a much simpler model. The RPEM estimates 16 unknown parameters while the linear model uses 9 parameters. The results were compared based on the model fit and it was clear from the validation data set that the non-linear model gives better results and captures more of the system dynamics as compared to the linear model. A comparison of the RPEM using the midpoint integration method and the Euler method for discretisation was also made for the models. The results clearly show that the more accurate discretisation algorithm results in a better model fit.

Application of a Transition Graph-Based Predictive Algorithm to a Solar Air Conditioning Plant

This brief presents the development of a transition graph-based predictive algorithm and its application to a solar air conditioning plant. The process considered is a hybrid system of variable configuration subject to disturbances in its main energy source, namely solar radiation. The predictive algorithm switches between the operating modes of the plant on the basis of minimizing the performance cost function associated to each operating mode and the switching cost function associated to the transition between operating modes. The cost functions depend on the predicted states of the plant. A transition graph is used to reduce the computational cost of the optimization problem; the graph is plotted using the concept of reachability. The main objective of the predictive algorithm is to find the optimal solution to the configuration problem and the optimal control signal of the plant throughout the day. Experimental results show that solar energy was used during most of the test and cooling demand was satisfied.

Multivariable model predictive control of processes with unstable transmission zeros

This paper shows how a model predictive controller has instability problems for multivariable processes with unstable transmission zeros. The instability problems are produced by the cancellation of unstable transmission zeros, which leads to a loss of internal stability of the feedback system.