We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last 7 years.

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CasADi: a software framework for nonlinear optimization and optimal control

This paper recalls a few past achievements in Model Predictive Control, gives an overview of some current developments and suggests a few avenues for future research.

Model Predictive Control

This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

/pdf/an-overview-of-recent-progress-in-the-study-of-distributed-1xnb6e12se.pdf

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

System Identification I

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac11-proceedings/data/html/papers/2848.pdf

Is suboptimal nonlinear MPC inherently robust

Optimal modified models for robust predictive controllers

This paper presents a study of identification and validation of data-driven models for the description of the acid gas treatment process, a key step of flue gas cleaning in waste-to-energy plants. The acid gas removal line of an Italian plant, based on the injection of hydrated lime, Ca(OH)2, for the abatement of hydrogen chloride, HCl, is investigated. The final goal is to minimize the feed rate of reactant needed to achieve the required HCl removal performance, also reducing as a consequence the production of solid process residues. Process data are collected during dedicated plant tests carried out by imposing Generalized Binary Noise (GBN) sequences to the flow rate of Ca(OH)2. Various input-output and state-space models are identified with success, and related model orders are optimized. The models are then validated on different datasets of routine plant operation. The proposed modeling approach appears reliable and promising for control purposes, once implemented into advanced model-based control structures. 

https://doi.org/10.1016/j.ifacol.2022.07.554

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

The problem of designing robust inverse-based controllers for multivariable ill-conditioned processes is addressed in this paper. A methodology is developed aimed at finding modified models that, used in inverse-based controllers, are able to make the overall control structure robust. The models are obtained through the solution of an optimization problem which contains in the objective function terms related both to nominal performance and to robustness to uncertainties. A basic advantage of the procedure is that its results can be used in any kind of inverse-based controller. The application to the design of a decoupler and of an internal model controller for a structurally ill-conditioned distillation column is presented to show the effectiveness of the method.

Robust Multivariable Inverse-Based Controllers: Theory and Application

The economic performance of distillation processes has been always addressed by techniques supporting and completing Model Predictive Control (MPC), which are known as Real Time Optimization (RTO). RTO, via simulation model, has been deeply studied and applied to distillation processes, nevertheless, the cost associated with the development of such online simulation models is often an obstacle for industries to change their operations. Hence, this work presents an unconventional RTO approach, named “analytical RTO”, which calculates the setpoint of manipulated variables according to operating strategy exploiting an analytical generalized formulation. The proposed RTO is built for a specific unit, that is a Propane-Propylene super-fractionator producing polymer grade Propylene (99.5%), and it is based on an offline simulation model developed in UniSim Design. Sensitivities analysis of the revenue with the manipulated variables is obtained with the simulation model and used to derive a simple, yet quite effective analytical RTO formulation, taking into account the fluidynamic limit of the unit studied. Comparing such an approach with the simulations, the results show a satisfactory match of the analytical estimations with the optimal points calculated by the rigorous model. 

https://doi.org/10.1016/j.ifacol.2022.07.435

Gabriele Pannocchia

Papers

Is suboptimal nonlinear MPC inherently robust

Optimal modified models for robust predictive controllers

Data-driven Models for Advanced Control of Acid Gas Treatment in Waste-to-energy Plants

Robust Multivariable Inverse-Based Controllers: Theory and Application

Analytical RTO for a critical distillation process based on offline rigorous simulation