Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool

Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation

Predictive Control in the Era of Networked Control and Communication – a Perspective

Advances in communication, information technology, and computation have led to a rapid change of today’s world. Actuation, communication, sensing, and control are becoming ubiquitous. While this offers many possibilities - Smart cities, Smart buildings, Smart devices, Smart factories, Smart health monitoring a smarter world - there are also several challenges which need to be tackled. How can one handle the increasing complexity? Can one guarantee safety and performance of such networked control systems subject to erroneous communication, delays, and failures of sensors and actuators? Is it possible to design control systems with plug and play capacity? How can one guarantee privacy of the controlled subsystems while exchanging information? Predictive control is a well suited control approach to tackle some of these challenges, since its allows to directly take constraints, preview information, as well as models of the physical world into account.We limit our attention to three areas we believe predictive control methods can have a significant impact: the efficient and easy implementation of predictive control on the omnipresent embedded computation hardware, the control under resource limitations and network effects, and the control on the network level, outlining a contract based control approach which allows a structured, yet flexible hierarchical design.We briefly review results from these fields and outline some solutions related to our work, that provide possible solutions to the considered challenges

Predictive control, embedded cyberphysical systems and systems of systems: A perspective

Today’s world is changing rapidly due to advancements in information technology, computation and communication. Actuation, communication, sensing, and control are becoming ubiquitous. These technological advancements have led to the widespread availability of information and the possibility to connect systems in unforeseen manner. There is a strong desire for smart(er) cities, buildings, devices, factories, health monitoring – a smarter world. However, designing such a smarter world requires addressing also many challenges resulting from the emerging complex interactions and interoperation of systems. How is it possible to handle the increasing complexity during design and maintenance of such systems? How can one guarantee safety and performance of systems operating over networks which are subject to erroneous communication, delays, and failures of sensors and actuators? Is it possible to design control systems which allow for easy reconfiguration or even self-organization, for example by letting subsystems join and leave larger systems via plug and play strategies? Can one guarantee privacy of the controlled subsystems while exchanging information, which is necessary for maintaining overall system performance? We believe that predictive control is a well suited control approach to tackle some of these challenges due to its flexibility with respect to the formulation of the problem and the possibility to directly take constraints, preview information, as well as models of different complexity of the physical world into account. In this perspective we limit our attention to three areas we believe predictive control methods can provide a basis to tackle the appearing challenges: the efficient and easy implementation of predictive control on omnipresent embedded computation hardware, the question of resource and network aware control, as well as control on the network level of systems of systems. We briefly summarize results from these fields and outline some ideas on challenges, which arise