Alberto Leva

We present an anti-windup technique, independent of the controller structure, targeted at optimizing the transients that the controlled variable and the control input undergo when the latter exits saturation, which is an aspect where the available techniques offer hardly any control. We formulate the problem as a constrained quadratic optimization one, and obtain for it an analytic solution. This solution provides a value to which we reset the controller state on exiting saturation. The resulting implementation is computationally lightweight, hence readily applicable to industrial controllers. The reported simulation and experimental results evidence its ease of use, together with the yielded advantages.

This paper highlights some still-open issues in theModelica representation of industrial controllers,particularly when simulation efficiency must balance withfidelity to their real-world implementation as digitalcomponents of cyber-physical systems. The treatise focuseson PID controllers due to their predominant role, thoughthe ideas readily extend to virtually any other controlstructure. The aim is to stimulate discussion around thisimportant yet often overlooked topic, while also suggestingdirections for future development.

Realistic industrial systems typically need to be modeled as hybrid systems consisting of hundreds (easily thousands) of nonlinear differential algebraic equations (DAEs). The size of such models is one of the major obstacles to overcome when developing automated design methods for industrial control systems. In this article, we present a scenario-based approach that, by exploiting the synergies among simulation, black-box optimization, and statistical model checking, allows us to automate the design of quality-guaranteed industry-size control systems, i.e., control systems for which a user-specified statistical guarantee on correctness holds over the possible operational scenarios. We show the effectiveness of our approach through a Modelica model consisting of a hybrid nonlinear DAE system with 1276 equations, 492 of which are nontrivial, containing 152 continuous state variables and 38 discrete ones, plus 7 algorithm blocks. Our experiments show that within a few hours of computation on an off-the-shelf workstation, we can find quality-guaranteed solutions (with very tight quality guarantees) to our design problem. We also compute an entire discretized Pareto front for such a large system over two conflicting key performance indicators.

This paper introduces MARCO, a research compiler aimed at the efficient generation of efficient simulation code from a large-scale Modelica model. MARCO’s design goals, requirements, and specifications are dis- cussed in the paper, as well as the software architec- ture, the current development status, and a future develop- ment roadmap. The results of two test cases demonstrate MARCO’s capability to handle non-trivial Modelica mod- els with over 10 million equations very efficiently.

Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative languages. When confronted with nowadays’ problems, however, well assessed model translation techniques exhibit scalability issues that are particularly severe when models contain very large arrays. In fact, such models can be made very compact by enclosing equations into looping constructs, but reflecting the same compactness into the translated imperative code is nontrivial. In this paper, we face this issue by concentrating on a key step of equations-to-code translation, the equation/variable matching. We first show that an efficient translation of models with (large) arrays needs awareness of their presence, by defining a figure of merit to measure how much the looping constructs are preserved along the translation. We then show that the said figure of merit allows to define an optimal array-aware matching, and as our main result, that the so stated optimal array-aware matching problem is NP-complete. As an additional result, we propose a heuristic algorithm capable of performing array-aware matching in polynomial time. The proposed algorithm can be proficiently used by model translator developers in the implementation of efficient tools for large-scale system simulation.

The power density in modern Integrated Circuits (ICs) is tremendous. For example, Multi-Processor Systems-on-Chip (MPSoCs) nowadays undergo temperature swings of 40 degrees in 100 milliseconds or less, with rapidly emerging and vanishing sub-millimeter hot spots. As such, not only a simulation-based cooling assessment is vital, but one has to simulate the on-chip thermal phenomena jointly with the heat dissipation system - historically, a challenge. In recent years, however, the idea of coupling traditional 3D chip simulators with heat dissipation models written in Equation-Based Modeling (EBM) languages has proven to be a game changer. EBM languages allow one to compose a model by assembling components described in terms of Differential and Algebraic Equations (DAE) and have the simulation code generated automatically. In this article, we take a tutorial viewpoint on the matter just sketched, to put the reader in the position of exploiting the above technology. We also present the first nucleus of a model library for cooling systems, that we release as free software for the scientific and engineering community.

Digital twins (DTs) are enablers for the fast optimization processes required in the Industry 4.0 context. Declarative equation-based modeling languages, in turn, enable the creation of large-scale simulation-based DTs, as they relieve the analyst from creating the solution code. However, most industrial assets are cyber-physical systems, the cyber part being their digital controls. With the available technology, a precise representation of modulating and logic controls conflicts with DT simulation performance. The result is a barrier to using DTs for system-level optimization. We analyze the problem, propose a modeling paradigm to solve it, and suggest how to integrate that paradigm into equation-based language compilers. We support our proposal by presenting a Modelica/C++ library, which we release as free software, built according to the said paradigm.

We propose a periodic event-based control scheme in which, when the sensor triggers an event, also past and subsequent values of the controlled variable are transmitted. We employ this idea to obtain a stability condition independent of the used event-triggering mechanism. In turn, we exploit this independence to devise a methodology for extending existing controller (auto)tuning rules to the event-based setting and provide an example with the internal model control PID (IMC-PID) rule. We then abstract the functionalities that a wireless sensor node has to offer for implementing our scheme and finally present such a node, physically realized in a completely hardware-based form to the advantage of both energy efficiency and security. Simulations results and experiments with the so-obtained control architecture (wireless sensor node plus event-based IMC-PID) are reported.