Real-Time Nonlinear Model Predictive Control for Fast Mechatronic Systems

This thesis presents an efficient and extensible numerical software framework for real-time model-based control. We are motivated by complex and challenging mechatronic applications spanning from flight control of fixed-wing aircraft and thrust vector control drones to autonomous driving.In the first part, we present PolyMPC, a novel C++ software framework for real-time embedded nonlinear optimal control and optimisation. A key feature of the package is a highly optimised implementation of the pseudospectral collocation method that exploits instruction set parallelism available on many modern computer architectures. Polynomial representation of the state and control trajectories allows the tool to be used as a standalone controller and as an efficient solver for low-level tracking controllers in hierarchical schemes. Algorithmically, the choice is made towards computational speed. For nonlinear problems, we combine a sequential quadratic programming (SQP) strategy with the alternating direction method of multipliers (ADMM) for quadratic programs (QP), which is especially favourable for embedded applications thanks to the low computational cost per iteration.In the second part, the developed numerical methods and software are used to experimentally study optimisation-based control of airborne wind energy (AWE) systems. For this purpose, we designed and built a small-scale prototype of a single-line rigid-wing AWE kite which comprises an aircraft fitted with necessary sensors and computers and a fully autonomous ground station for tether control. The prototype serves as a research platform for studying flight navigation and control systems thanks to very flexible custom mission management and control software. We further develop a dynamic optimisation based methodology for parameter identification and provide a validated flight simulator that matches well the real behaviour of the system. Finally, a model-predictive path following flight controller is designed and tested in real-world experiments.The third part of the thesis is concerned with the application of real-time nonlinear model predictive control (NMPC) to autonomous driving at the limits of handling, which requires high sampling rates and robustness of the motion control system. We propose a dynamic optimization-based hierarchical framework for the local refinement of the racing lines that takes into account the nonlinear vehicle and actuator dynamics, adaptive tyre constraints, and the safety corridor around the initial path. The top layer receives a discrete obstacle-free local path computed by a coarse planner and transforms it into auto-differentiable look-up tables (LUT) for efficient continuous sampling.Separately, we investigated the problem of safe trajectory planning under parametric model uncertainties motivated by automotive applications. We use generalised polynomial chaos expansions for efficient nonlinear uncertainty propagation and distributionally robust inequalities for chance constraint approximation. Inspired by tube-based model predictive control, an ancillary feedback controller is used to control the deviations of stochastic modes from the nominal solution, and therefore, decrease the variance. Our approach reduces conservatism related to nonlinear uncertainty propagation while guaranteeing constraint satisfaction with a high probability.

Performance Comparison of Alternating Direction Optimization Methods for Linear-OPF based Real-time Predictive Control

Rahul Kumar Gupta, Mario Paolone, Fabrizio Sossan, Vladimir Sovljanski

The paper contributes to improving the computational performance of controls of distributed energy resources (DERs) in distribution grids for efficient real-time control and short-term scheduling. The considered setting is a distribution grid with heterogeneous DERs controlled with model predictive control (MPC) to track a dispatch plan at its grid connection point (GCP) subject to DERs’ and grid’s constraints. The MPC control is first expressed as a quadratic programming (using linearized grid models) and, then, solved with several state-of-the-art alternating direction optimization methods: AMA, ADMM, AADMM (ADMM with adaptive penalty parameter) and their accelerated variants: FAMA, FAADMM, and FAADMM with restart rule. Performance is tested in terms of computational performance, constraints satisfaction, and optimality against the centralized MPC. The case studies are the CIGRE and IEEE benchmark grid for low- and medium voltage systems hosting different numbers of controllable DERs.

IEEE2021

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Achille Nicoletti

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs for both linear-time-invariant (LTI) single-input-single-output (SISO) systems and certain classes of nonlinear systems. In this dissertation, a data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance. Necessary and sufficient conditions are derived for obtaining H∞ performance while guaranteeing the closed-loop stability of a system. A convex optimization algorithm is implemented to obtain the controller parameters which ensure system robustness; the controller is robust with respect to the frequency-dependent uncertainties of the frequency response function. For a certain class of nonlinearities, the proposed method can be used to obtain a best-linear-approximation with an associated frequency dependent uncertainty to guarantee the stability and performance for the underlying linear system that is subject to nonlinear distortions. The concepts behind these design methods are then used to devise necessary and sufficient conditions for ensuring the closed-loop stability of systems with sector-bounded nonlinearities. The conditions are simple convex feasibility constraints which can be used to stabilize systems with multi-model uncertainty. Additionally, a method is proposed for obtaining H∞ performance for an approximate model (i.e., describing function) of a sector-bounded nonlinearity. This work also proposes several data-driven methods for designing robust fixed-structure controllers with H∞ performance. One method considers the solution to a non-convex problem, while another method convexifies the problem and implements an iterative algorithm to obtain the local solution (which can also consider H2 performance). The effectiveness of the proposed method(s) is illustrated by considering several case studies that require robust controllers for achieving the desired performance. The main applicative work in this dissertation is with respect to a power converter control system at the European Organization for Nuclear Research (CERN) (which is used to control the current in a magnet to produce the desired field in controlling particle trajectories in accelerators). The proposed design methods are implemented in order to satisfy the challenging performance specifications set by the application while guaranteeing the system stability and robustness using data-driven design strategies.

EPFL2018

Architecture Exploration and Optimization of Heterogeneous Many-Core Compute and Memory Architectures with Architectural Extensions

Yasir Mahmood Qureshi

The expeditious proliferation of Internet connectivity and the growing adoption of digital products have transformed various spheres of our everyday lives. This increased digitization of society has led to the emergence of new applications, which are deployed all the way from High Performance Computing (HPC) systems and cloud servers to mobile devices. These new emerging applications like video analytics, autonomous driving, natural language processing, content recommendation, bioinformatics, and genome sequencing, have different performance/energy requirements, and are deployed on a variety of different platforms. To meet the performance and Quality-of-Service (QoS)constraints, cloud servers, and HPC platforms are comprised of many-core processors. Even the processors in today's mobile and edge devices are multi-core. To optimize energy efficiency and performance, a system-level simulator is required. This simulator must be capable of simultaneously executing multi-threaded applications in a full-system environment with a complete operating system (OS), on a heterogeneous many-core system.In this thesis, I present gem5-X, a system-level simulation platform to optimize many-core heterogeneous compute and memory architectures. Gem5-X exploration and optimization methodology is also proposed to optimize both the compute and memory sub-system for multi-thread applications. Gem5-X extends gem5 with architectural extensions for the compute and memory sub-systems, including in-caching computing accelerator, clustered heterogeneous cores, in-memory computing engine, and 3D stacked High Bandwidth Memory v2 (HBM2). It also adds support enhancements to gem5, including, Virtual Machine (VM) support, profiling support in the simulator using gperf profiler, enhanced checkpointing, and file sharing between the simulated and the host system. Finally, all the extensions and enhancements are fully supported in Full System (FS)mode of gem5-X, enabling booting and running a full Linux stack on top of the simulator, allowing almost any application running in a Linux system to be executed using gem5-X.To demonstrate the capabilities of gem5-X, various compute-dominated and memory-dominated applications are used as case studies. These state-of-the-art applications include video encoding, video analytics, VMs in the cloud, Binary Neural Networks (BNNs) and Recurrent Neural Networks (RNNs) as compute-intensive workloads. In addition to compute-dominated workloads, memory-dominated applications, including genome sequence alignment based on Next Generation Sequencing (NGS) techniques, and Convolutional Neural Networks (CNNs) are presented as case studies to demonstrate the memory sub-system extensions of gem5-X. Gem5-X exploration methodology is used to optimize architectures for these applications, achieving both performance and energy benefits. All the applications and workloads used in this thesis are case studies to showcase the capabilities of gem5-X. Gem5-X is generic and can be used to optimize and explore architectures for any multi-threaded (or single-threaded) application that can run on a Linux-based OS.