Robust Multivariable Microgrid Control Synthesis and Analysis

AbstractIn this paper, an islanded microgrid is modelled as a linear multivariable dynamic system. Then, the multivariable analysis tools are employed. The generalized Nyquist diagram and the relative gain array are used respectively for the stability assessment and solving the paring problem among the inputs and outputs. Droop control dependency on the X/R ratio of the microgrid DERs is recognized and its type is proposed using the relative gain array concept. Robust stability, nominal performance and robust performance requirements are evaluated in order to a better understanding of the system dynamics. Finally, three different controllers including H∞, H2 and sequential proportional-integral-derivative controls are designed and compared

Automatic blood glucose control for type 1 diabetes: A trade-off between postprandial hyperglycemia and hypoglycemia

Artificial pancreas (AP) systems perform automated insulin delivery to subjects with type 1 diabetes mellitus (T1DM). In this paper, a nonlinear suboptimal controller is designed to make a trade-off between the elimination of hypoglycemia events and the limitation of postprandial hyperglycemia. All the in silico simulations are performed using the distribution version of the UVA/Padova type 1 diabetes (T1D) simulator. The proposed nonlinear AP system is based on an individualized control law which is designed in three steps. At first, a nonlinear model of the glucose–insulin regulatory system is identified based on the data collected from some safe experiments. Then, using the personalized models for all the patients of the simulator and a nonlinear technique called state-dependent Riccati equation (SDRE), suboptimal controllers are designed in which a trade-off between the abilities to correct hyperglycemia and to minimize hypoglycemia is made by considering variable weighting matrices for the controller. Since the SDRE controller has a state-feedback structure, unscented Kalman filter (UKF) is employed to generate estimations for unmeasured state variables from the measured subcutaneous blood glucose level. To assess the performance of the proposed AP system, several scenarios are considered for 33 in silico patients (11 adults, 11 adolescents, and 11 children). The obtained results are analyzed and compared with two other AP systems. Patients’ blood glucose concentrations are maintained in safe levels in all the simulated scenarios and very limited hyperglycemia and no hypoglycemia are observed even in a challenging scenario. The promising results are so encouraging and the proposed AP system is worthy to be tested in vivo.Scopu

Nonlinear Suboptimal Tracking Controller Design Using State-Dependent Riccati Equation Technique

In this brief, a new technique for solving a suboptimal tracking problem for a class of nonlinear dynamical systems is presented. Toward this end, an optimal tracking problem using a discounted cost function is defined and a control law with a feedback-feedforward structure is designed. A state-dependent Riccati equation (SDRE) framework is used in order to find the gains of both the feedback and the feedforward parts, simultaneously. Due to the significant properties of the SDRE technique, the proposed method can handle the presence of input saturation and state constraint. It is also shown that the tracking error converges asymptotically to zero under mild conditions on the discount factor of the corresponding cost function and the desired trajectory. Two simulation and experimental case studies are also provided to illustrate and demonstrate the effectiveness of our proposed design methodology. 1 2017 IEEE.Scopu