Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Model predictive control of a CSTR: A comparative study among linear and nonlinear model approaches

© 2017 IEEE. This paper presents a comparative study of two widely accepted model predictive control schemes based on mixed logical dynamical (MLD) and nonlinear modeling approaches with application to a continuous stirred tank reactor (CSTR) system. Specifically, we approximate the nonlinear behavior of a CSTR system with multiple local linear models in a MLD framework. The main benefit of such a scheme is the significant improvement in model accuracy when compared with a single linearized model. The benefits and trade-offs associated with predictive control laws synthesized using MLD and nonlinear modeling approaches are also compared.National Research Foundation, Singapore

Flight control of hybrid drones towards enabling parcel relay manoeuvres

This work addresses the modeling and controlling process of a hybrid UAV, aimed for parcel relay maneuvers. Hybrid UAVs bring big advantages with the capability of flying in two flight modes, rotary and fixed wing. But with them comes added complexity both in modeling and controlling. This work is based on a popular airframe, a tilt tri-rotor UAV, containing all the specific system dynamics such vehicle category provides. The model is then validated by designing two separate controllers for both flight modes, capable of trajectory tracking in eachmode,makinguseofacustomhybridcontrolallocationtechniquethatdifferentiates the control in three parts: vertical, horizontal, and transitional flight modes. Finally, a hybrid controller is proposed, using a finite state machine capable of handling logical events, with the aim to provide control logic to perform autonomous mid flight transitions. All the designs system are simulated using a mathematical framework and a power-full simulation tool.Este trabalho aborda o processo de modelação e controlo de um veículo aéreo não tripulado híbrido com o objetivo de proporcionar manobras de transição de carga. Drones híbridos trazem grandes vantagem com a sua capacidade de voar em dois modos de voo, de asa rotativa e asa fixa. Por outro lado, estas vantagens adicionam complexidade ao sistema dificultando o processo de modulação e controlo. Nestetrabalhoestápresenteummodelodeumdronetrirotortendodoisrotoresmovíveis. Este contém todas as dinâmicas especificas que um sistema deesta categoria de UAV obriga. O modelo é posteriormente validado com dois controladores separados em modo de voo, capazes de proporcionar medidas de seguimento de trajetória em cada modo, usando uma técnica de alocação de controlo personalizada que diferencia o controlo em três partes: vertical, horizontal e de transição. Por fim, é proposto um controlador híbrido contento uma máquina de estados capaz de tratar de eventos lógicos, de modo a proporcionar transições de modo de voo autónomas em pleno voo. Todos os sistemas propostos são devidamente simulados usando ferramentas matemáticas e também poderosos sistemas de simulação

A methodology and a tool for evaluating hybrid electric powertrain configurations

This paper describes a methodology for automatic optimisation of hybrid electric powertrains. This methodology is developed and implemented in a tool, CAPSimO, and the paper is written in the form of describing the tool. Given the user inputs, which are dynamic vehicle model, driving cycle and optimisation criterion, the tool first produces a simplified powertrain model in a form of static maps, before dynamic programming is used to find an optimal power split which minimises the chosen criterion. The tool does not require that the vehicle model is transparent, which makes it possible to work on models hidden for intellectual property reasons. The paper presents two examples of powertrain evaluation, in terms of fuel consumption, for a parallel and a parallel-series powertrain

model predictive control tools for evolutionary plants

The analysis and design of control system configurations for automated production systems is generally a challenging problem, in particular given the increasing number of automation devices and the amount of information to be managed. This problem becomes even more complex when the production system is characterized by a fast evolutionary behaviour in terms of tasks to be executed, production volumes, changing priorities, and available resources. Thus, the control solution needs to be optimized on the basis of key performance indicators like flow production, service level, job tardiness, peak of the absorbed electrical power and the total energy consumed by the plant. This paper proposes a prototype control platform based on Model Predictive Control (MPC) that is able to impress to the production system the desired functional behaviour. The platform is structured according to a two-level control architecture. At the lower layer, distributed MPC algorithms control the pieces of equipment in the production system. At the higher layer an MPC coordinator manages the lower level controllers, by taking full advantage of the most recent advances in hybrid control theory, dynamic programming, mixed‐integer optimization, and game theory. The MPC-based control platform will be presented and then applied to the case of a pilot production plant