Optimal Thrust Vector Control of an Electric Small-Scale Rocket Prototype

Colin Neil Jones, Petr Listov, Roland Schwan
2021
Report or working paper

Recent advances in Model Predictive Control (MPC) algorithms and methodologies, combined with the surge of computational power of available embedded platforms, allows the use of real-time optimization-based control of fast mechatronic systems. This paper presents an implementation of an optimal guidance, navigation and control (GNC) system for the motion control of a small-scale electric prototype of a thrust-vectored rocket. The aim of this prototype is to provide an inexpensive platform to explore GNC algorithms for automatic landing of sounding rockets. The guidance and trajectory tracking are formulated as continuous-time optimal control problems and are solved in real-time on embedded hardware using the PolyMPC library. An Extended Kalman Filter (EKF) is designed to estimate external disturbances and actuators offsets. Finally, indoor and outdoor flight experiments are performed to validate the architecture.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts

Related publications

Colin Neil Jones, Petr Listov, Roland Schwan
2021
Report or working paper

Abstract

Official source

https://infoscience.epfl.ch/record/288898?ln=en

About this result

Related concepts (8)

Related concepts

Related publications (2)

Related publications

Model predictive control

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemica

Mechatronics

Mechatronics engineering also called mechatronics, is an interdisciplinary branch of engineering that focuses on the integration of mechanical, electrical and electronic engineering systems, and also

Motion control

Motion control is a sub-field of automation, encompassing the systems or sub-systems involved in moving parts of machines in a controlled manner. Motion control systems are extensively used in a vari

Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving

Jean Pierre Allamaa, Colin Neil Jones, Petr Listov

In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle. Non-linear model predictive control (NMPC) is designed and deployed on a system with hard real-time constraints. We show the properties of sequential quadratic programming (SQP) optimization solvers that are suitable for driving tasks. Importantly, the designed algorithms are validated based on a standard automotive development cycle: model-in-the-loop (MiL) with high fidelity vehicle dynamics, hardware-in-theloop (HiL) with vehicle actuation and embedded platform, and vehicle-hardware-in-the-loop (VeHiL) testing using a full vehicle. The autonomous driving environment contains both virtual simulation and physical proving ground tracks. Throughout the process, NMPC algorithms and optimal control problem (OCP) formulation are fine-tuned using a deployable C code via code generation compatible with the target embedded toolchains. Finally, the developed systems are applied to autonomous collision avoidance, trajectory tracking and lane change at high speed on city/highway and low speed at a parking environment.

2021

Two-Layered Real-Time Optimization of a Solid Oxide Fuel Cell Stack

Dominique Bonvin, Gene Bunin, Grégory François

The optimal operation of a solid oxide fuel cell stack is addressed in this paper. Real-time optimization, performed at a slow time scale via constraint adaptation, is used to account for uncertainty and degradation eﬀects, while model-predictive control is performed at a faster time scale to reject process disturbances and to safely adapt the system to the specified output constraints following changes in cell power demand. To ensure that these constraints are strictly honored, a novel adaptation algorithm that uses the built-in constraint handling of quadratic programming is implemented within the model-predictive controller. An additional feature of this algorithm - its ability to adapt the feasibility region in view of uncertainty - is shown as well. Simulation results illustrate the efficacy of this approach in the solid oxide fuel cell system.

2010