Tube-based Robust MPC Processor-In-the-Loop Validation for Fixed-Wing UAVs

Real systems, as Unmanned Aerial Vehicles (UAVs), are usually subject to environmental disturbances, which could compromise the mission accomplishment. For this reason, the main idea proposed in this research is the design of a robust controller, as autopilot control system candidate for a fixedwing UAV. In detail, the inner loop of the autopilot system is designed with a tube-based robust model predictive control (TRMPC) scheme, able to handle additive noise. Moreover, the navigation outer loop is regulated by a proportional-integralderivative controller. The proposed TRMPC is composed of two parts: (i) a linear nominal dynamics, evaluated online with an optimization problem, and (ii) a linear error dynamics, which includes a feedback gain matrix, evaluated offline. The key aspects of the proposed methodology are: (i) offline evaluation of the feedback gain matrix, and (ii) robustness to random, bounded disturbances. Moreover, a path-following algorithm is designated for the guidance task, which provides the reference heading angle as input to the control algorithm. Software-in-theloop and processor-in-the-loop simulations have been performed to validate the proposed approach. The obtained performance have been evaluated in terms of tracking capabilities and computational load, assessing the real-time implementability compliance with the XMOS development board, selected as continuation of previous works

Single-state weighted particle filter with application to Earth Observation missions

To push the boundaries of autonomy in space, the spacecraft must rely on its own sensors to achieve positioning and environmental perception. In this context, the key problem of autonomous navigation is the nonlinear state estimation of the spacecraft in a dynamic 3D environment. In this paper, we propose a new approach based on a single-state sub-partitioning of the state vector and a partial updating of the vector of weights according to the specific information provided by each sensor. In this way, we avoid to lose information in the resampling phase thanks to a parallelization approach. The proposed method has been applied to an Earth observation mission and the efficacy of the proposed approach is demonstrated with a numerical example using a high-fidelity orbital simulator

Learning model predictive control for quadrotors minimum-time flight in autonomous racing scenarios

In this paper, we design a Learning Model Predictive Control (LMPC) algorithm for quadrotors autonomous racing. The proposed algorithm allows to define a highly customizable 3D race track, in which multiple types of obstacles can be inserted. The controller is then able to autonomously find the best trajectory minimizing the quadrotor lap time, by learning from data coming from previous flights within the track, ensuring also the avoidance of all the obstacles therein. We also present novel relaxation approaches for the LMPC optimization problem, that allow to reduce it from a mixed-integer nonlinear program to a quadratic program. The LMPC algorithm is tested via several software-in-the-loop simulations, showing that the algorithm has learned to fly the quadrotor aggressively and dexterously, managing to both find the minimum-time trajectory and avoid the obstacles inside the track

Computationally efficient stochastic MPC: A probabilistic scaling approach

In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, which has restricted their applicability to slow-dynamics and high-performing systems. To reduce the computational burden, in this paper we extend the probabilistic scaling approach to obtain low-complexity inner approximation of chance-constrained sets. This approach provides probabilistic guarantees at a lower computational cost than other schemes for which the sample complexity depends on the design space dimension. To design candidate simple approximating sets, which approximate the shape of the probabilistic set, we introduce two possibilities: i) fixed-complexity polytopes, and ii) `p-norm based sets. Once the candidate approximating set is obtained, it is scaled around its center so to enforce the expected probabilistic guarantees. The resulting scaled set is then exploited to enforce constraints in the classical SMPC framework. The computational gain obtained with the proposed approach with respect to the scenario one is demonstrated via simulations, where the objective is the control of a fixed-wing UAV performing a monitoring mission over a sloped vineyard

A probabilistic validation approach for penalty function design in stochastic model predictive control

Cuenta con otro ed. : IFAC-PapersOnLine Incluída en el vol. 53, Issue 2 Article number 145388In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive assumption on either independence nor Gaussianity. We revisit the rather classical approach based on penalty functions, with the aim of designing a control scheme that meets some given probabilistic specifications. The main difference with previous approaches is that we do not recur to the notion of probabilistic recursive feasibility, and hence we do not consider separately the unfeasible case. In particular, two probabilistic design problems are envisioned. The first randomization problem aims to design offline the constraint set tightening, following an approach inherited from tube-based MPC. For the second probabilistic scheme, a specific probabilistic validation approach is exploited for tuning the penalty parameter, to be selected offline among a finite-family of possible values. The simple algorithm here proposed allows designing a single controller, always guaranteeing feasibility of the online optimization problem. The proposed method is shown to be more computationally tractable than previous schemes. This is due to the fact that the sample complexity for both probabilistic design problems depends on the prediction horizon in a logarithmic way, unlike scenario-based approaches which exhibit linear dependence. The efficacy of the proposed approach is demonstrated with a numerical example.Ministerio de Economía y Competitividad ( España)Ministerio de Educación, Universidad e Investigación de Italia 2017 PRIN 2017S559B

A probabilistic validation approach for penalty function design in Stochastic Model Predictive Control

In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive assumption on either independence nor Gaussianity. We revisit the rather classical approach based on penalty functions, with the aim of designing a control scheme that meets some given probabilistic specifications. The main difference with previous approaches is that we do not recur to the notion of probabilistic recursive feasibility, and hence we do not consider separately the unfeasible case. In particular, two probabilistic design problems are envisioned. The first randomization problem aims to design \textit{offline} the constraint set tightening, following an approach inherited from tube-based MPC. For the second probabilistic scheme, a specific probabilistic validation approach is exploited for tuning the penalty parameter, to be selected \textit{offline} among a finite-family of possible values. The simple algorithm here proposed allows designing a \textit{single} controller, always guaranteeing feasibility of the online optimization problem. The proposed method is shown to be more computationally tractable than previous schemes. This is due to the fact that the sample complexity for both probabilistic design problems depends on the prediction horizon in a logarithmic way, unlike scenario-based approaches which exhibit linear dependence. The efficacy of the proposed approach is demonstrated with a numerical example.Comment: Submitted as Contributed Paper to IFAC2020 Congres

Prediction error quantification through probabilistic scaling

In this letter, we address the probabilistic error quantification of a general class of prediction methods. We consider a given prediction model and show how to obtain, through a sample-based approach, a probabilistic upper bound on the absolute value of the prediction error. The proposed scheme is based on a probabilistic scaling methodology in which the number of required randomized samples is independent of the complexity of the prediction model. The methodology is extended to address the case in which the probabilistic uncertain quantification is required to be valid for every member of a finite family of predictors. We illustrate the results of the paper by means of a numerical example