Predictive Second Order Sliding Control of Constrained Linear Systems with Application to Automotive Control Systems

This paper presents a new predictive second order sliding controller (PSSC) formulation for setpoint tracking of constrained linear systems. The PSSC scheme is developed by combining the concepts of model predictive control (MPC) and second order discrete sliding mode control. In order to guarantee the feasibility of the PSSC during setpoint changes, a virtual reference variable is added to the PSSC cost function to calculate the closest admissible set point. The states of the system are then driven asymptotically to this admissible setpoint by the control action of the PSSC. The performance of the proposed PSSC is evaluated for an advanced automotive engine case study, where a high fidelity physics-based model of a reactivity controlled compression ignition (RCCI) engine is utilized to serve as the virtual test-bed for the simulations. Considering the hard physical constraints on the RCCI engine states and control inputs, simultaneous tracking of engine load and optimal combustion phasing is a challenging objective to achieve. The simulation results of testing the proposed PSSC on the high fidelity RCCI model show that the developed predictive controller is able to track desired engine load and combustion phasing setpoints, with minimum steady state error, and no overshoot. Moreover, the simulation results confirm the robust tracking performance of the PSSC during transient operations, in the presence of engine cyclic variability.Comment: 6 pages, 5 figures, 2018 American Control Conferance (ACC), June 27-29, 2018, Milwaukee, WI, USA. [Accepted in Jan. 2018

Market Integration in the Golden Periphery - the Lisbon/London Exchange, 1854-1891

The existence of a self-regulating arbitrage mechanism under the gold standard has been traditionally considered as one of its main advantages, and attracted a corresponding research interest. This research is arguably relevant not only to test for the efficiency of the “gold points”, but also to study the evolution of financial integration during the so-called first era of globalization. Our first aim with this paper is to contribute to the enlargement of the scope of the literature by considering the case of Portugal that adhered to the system, in 1854, at a much earlier phase than the majority of countries, thus allowing for a broader perspective on the evolution of the efficiency of the foreign exchange market. As a typical “peripheral” country, Portugal can be used as the starting point for a study of the degree of integration of the periphery within the system. Furthermore, the Portuguese exchange also illustrates the role in practice of large players in sustaining currency stability, over and beyond the atomistic forces of arbitrage and speculation assumed in conventional theoretical frameworks. We also address the question of the credibility of the authorities’ commitment to the standard, through the perspective of the target zone literature.

Cooperative distributed MPC for tracking

This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

Extended MPC for Closed-Loop re-identification based on probabilistic invariant sets

Recently, a Model Predictive Control (MPC) scheme suitable for closedloop re-identification was proposed which solves, in a non-conservative form, the potential conflict between the persistent excitation of the system and the stabilization. The idea is to use the concept of probabilistic invariance to define a target set, and so to take advantage of the knowledge of the probabilistic distribution of the excitation signal to design a non-competitive two-objective MPC formulation. Although this proposal seems to work properly from an identification point of view (since uncorrelated output-input data are obtained), some theoretical properties of the formulation remains unexploited. In this work, new results are presented, focusing on the finite-time convergence to the target, which is necessary to start the second MPC objective of identification. Furthermore, several new simulation are developed to clearly show the new properties benefits

Model Predictive Control Suitable for closed-loop re-identification

The main problem of a closed-loop re-identification procedure is that, in general, the dynamic control and identification objectives are conflicting. In fact, to perform a suitable identification, a persistent excitation of the system is needed, while the control objective is to stabilize the system at a given equilibrium point. However, an abstraction or generalization of the concept of stability, from punctual stability to (invariant) set stability, allows a flexibility that can be used to avoid the conflict between these objectives. Taking into account that an invariant target set includes not only a stationary component, but also a transient one, the system could be excited without deteriorating the stability of the closed-loop. In this work, a MPC controller is proposed that assures the stability of invariant sets at the same time that a signal suitable for closed-loop re-identification is generated. Several simulation results show the propose controller formulation properties

Impacts of Quantifying Social Distancing Measures on Mpc Performance for Sir-Type Systems

Currently, there has been a sharp increase in epidemic control research as a result of recent epidemic outbreaks. Several strategies aiming to minimize the Epidemic Final Size and/or to keep the Infected Peak Prevalence under a specific value were proposed. However, not many strategies focused on analyzing the impact of applying quantified measures instead of continuous control action. This analysis is a crucial aspect since policymakers design their non-pharmaceutical intervention based on a discrete scale of intensity, from mask-wearing to hard lockdown. In this work, we present a quantized-input non-linear Model Predictive Control strategy to manage non-pharmaceutical interventions during an epidemic outbreak. The impact of quantifying the social distancing measure is computed through several simulations based on a COVID-19 epidemic model and considering different quantization levels of the non-pharmaceutical intervention. Finally, the control performance in each quantization level is evaluated with the computation of four epidemic indices

i-Steps Closed-Loop Sets for Constrained Linear Systems under Model Predictive Control

The understanding of invariant set theory is essential in the design of controllers for constrained systems. This paper presents some concepts related with the invariant set theory and Set-Based Model Predictive Control (set-based MPC). Precisely, introduces a new class of sets from where the closed-loop system reaches a target set before a pre-established number of steps. These novel concepts are based on several results presented in a former work [1]. The main results are exposed in a theoretical context, however several simulation examples show its potential and properties

Probabilistic Invariant Set for closed-loop re-identification

Recently, a Model Predictive Control (MPC) suitable for closed-loop re-identification was proposed, which solves the potential conflict between the persistent excitation of the system and the stabilization of the closed-loop by extending the equilibrium-point-stability to the invariant-setstability. The proposed objective set, however, derives in large regions that contain conservatively the excited system evolution. In this work, based on the concept of probabilistic invariant sets, the controller target sets are substantially reduced ensuring the invariance with a sufficiently large probability (instead of deterministically), giving the resulting MPC controller the necessary flexibility to be applied in a wide range of systems

Model Predictive Control Structures for Periodic ON–OFF Irrigation

Agriculture accounts for approximately 70% of the world’s freshwater consumption. Furthermore, traditional irrigation practices, which rely on empirical methods, result in excessive water usage. This, in turn, leads to increased working hours for irrigation pumps and higher electricity consumption. The main objective of this study is to develop and evaluate periodic model predictive control structures that explicitly account for on-off irrigation, a characteristic of drip irrigation systems where watering can be turned on and off, but flow cannot be regulated. While both proposed control structures incorporate an economic upper layer (Real Time Optimizer, RTO), they differ in the costs associated with the lower layer. The first structure, called Model Predictive Control for Tracking (MPCT), focuses on tracking effectiveness, while the second structure, called Economic Model Predictive Control for Tracking (EMPCT), incorporates the economic cost into the tracking term. These proposed structures are tested in a realistic case study, specifically in a strawberry greenhouse, and both show satisfactory performance. The choice of the best option will depend on specific conditions

Calcificazioni coronariche in una popolazione di donne in post-menopausa affette da sindrome metabolica

Background. The aim of this study was to evaluate the burden of coronary calcifications in a subgroup of post-menopausal women with metabolic syndrome (MS) in agreement with the National Cholesterol Educational Program-Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP-ATP III) definition. Methods. We studied 81 women (43 control subjects and 38 women with MS) in agreement with the NCEP-ATP III definition undergoing multislice computed tomography for evaluation of coronary calcifications. The patients were similar for Framingham risk score. Results. The severity and extent of coronary artery calcifications were higher in individuals with MS (10.8 \uc2\ub1 15.8 vs 3.02 \uc2\ub1 5.6; p = 0.006). In all patients total cholesterol, low-density lipoproteins and triglycerides were correlated with calcium score (p <0.05) while high-density lipoproteins were inversely correlated with coronary calcifications. In women with MS total cholesterol and low-density iipoprotein cholesterol were correlated with calcium score. Conclusions. Women with MS have a higher burden of subclinical coronary atherosclerosis. The correlation between MS and calcium score concerned more the presence rather than the severity of coronary calcifications. Moreover, no correlation was observed among single components of MS in agreement with the NCEP-ATP III definition. \uc2\ua9 2007 AIM Publishing Srl