# Rien QuirynenMitsubishi Electric Research Laboratories

Rien Quirynen

PhD

## About

92

Publications

24,489

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1,323

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Introduction

Additional affiliations

October 2012 - present

## Publications

Publications (92)

The branch-and-bound optimization algorithm for mixed-integer model predictive control (MI-MPC) solves several convex quadratic program relaxations, but often the solutions are discarded based on already known integer feasible solutions. This letter presents a projection and early termination strategy for infeasible interior point methods to reduce...

This paper presents the acados software package, a collection of solvers for fast embedded optimization intended for fast embedded applications. Its interfaces to higher-level languages make it useful for quickly designing an optimization-based control algorithm by putting together different algorithmic components that can be readily connected and...

A predictive controller controls a vehicle subject to equality and inequality constraints on state and control variables of the vehicle and solves a matrix equation of necessary optimality conditions to produce control inputs to control the vehicle. The controller represents the state variables as a function of the control variables using discrete-...

Stochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering to perform high-accuracy propagation of mean and covariance information for the nonlinear system dynamics in a tractable approximati...

We propose a centralized multi-robot motion planning approach that leverages machine learning and mixed-integer programming (MIP). We train a neural network to imitate optimal MIP solutions and, during execution, the trajectories predicted by the network are used to fix most of the integer variables, resulting in a significantly reduced MIP or even...

A predictive controller controls a vehicle subject to equality and inequality constraints on state and control variables of the vehicle and solves a matrix equation of necessary optimality conditions to produce control inputs to control the vehicle. The controller represents the state variables as a function of the control variables using discrete-...

Model predictive control (MPC) for linear dynamical systems requires solving an optimal control structured quadratic program (QP) at each sampling instant. This article proposes a primal active‐set strategy, called PRESAS, for the efficient solution of such block‐sparse QPs, based on a preconditioned iterative solver to compute the search direction...

This paper presents a real-time algorithm for stochastic nonlinear model predictive control (NMPC). The optimal control problem (OCP) involves a linearization based covariance matrix propagation to formulate the probabilistic chance constraints. Our proposed solution approach uses a tailored Jacobian approximation in combination with an adjoint-bas...

Interior point methods are applicable to a large class of problems and can be very reliable for convex optimization, even without a good initial guess for the optimal solution. Active-set methods, on the other hand, are often restricted to linear or quadratic programming but they have a lower computational cost per iteration and superior warm start...

Model predictive control (MPC) for linear dynamical systems requires solving an optimal control structured quadratic program (QP) at each sampling instant. This paper proposes a primal active-set strategy (PRESAS) for the efficient solution of such block-sparse QPs, based on a preconditioned iterative solver to compute the search direction in each...

We propose an adaptive nonlinear model predictive control (NMPC) for vehicle tracking control. The controller learns in real time a tyre force model to adapt to a varying road surface that is only indirectly observed from the effects of the tyre forces determining the vehicle dynamics. Learning the entire tyre model from data would require driving...

The acados software package is a collection of solvers for fast embedded optimization, intended for fast embedded applications. Its interfaces to higher-level languages make it useful for quickly designing an optimization-based control algorithm by putting together different algorithmic components that can be readily connected and interchanged. How...

Nonlinear model predictive control (NMPC) generally requires the solution of a non-convex dynamic optimization problem at each sampling instant under strict timing constraints, based on a set of differential equations that can often be stiff and/or that may include implicit algebraic equations. This paper provides a local convergence analysis for t...

Enforcing state and input constraints during reinforcement learning (RL) in continuous state spaces is an open but crucial problem which remains a roadblock to using RL in safety-critical applications. This paper leverages invariant sets to update control policies within an approximate dynamic programming (ADP) framework that guarantees constraint...

This paper discusses systems theoretic and computational aspects of a feasible, but suboptimal, nonlinear model predictive control scheme based on fixed sensitivities of the functions representing the constraints and cost of the underlying nonlinear programs. In particular, it will be shown how, by freezing the sensitivities computed at the desired...

Mixed-integer model predictive control (MI-MPC) requires the solution of a mixed-integer quadratic program (MIQP) at each sampling instant under strict timing constraints, where part of the state and control variables can only assume a discrete set of values. Several applications in automotive, aerospace and hybrid systems are practical examples of...

Nonlinear model predictive control~(NMPC) generally requires the solution of a non-convex optimization problem at each sampling instant under strict timing constraints, based on a set of differential equations that can often be stiff and/or that may include implicit algebraic equations. This paper provides a local convergence analysis for the recen...

This paper discusses a real-time implementation of tube model predictive controllers for nonlinear input-affine systems. This is achieved by combining recent theoretical and practical advances on the construction of forward invariant tubes with state-of-the-art algorithms for nonlinear MPC, such as the real-time iteration scheme. The focus of the p...

We investigate using Krylov subspace iterative methods in model predictive control (MPC), where the prediction model is given by linear or linearized systems with linear inequality constraints on the state and the input, and the performance index is quadratic. The inequality constraints are treated by the primal-dual interior point method. We indic...

Model predictive control (MPC) often requires solving an optimal control structured quadratic program (QP), possibly based on an online linearization at each sampling instant. Block-tridiagonal preconditioners have been proposed, combined with the minimal residual (MINRES) method, to result in a simple but efficient implementation of a sparse activ...

Pseudospectral and collocation methods form a popular direct approach to solving continuous-time optimal control problems. Lifted Newton-type algorithms have been proposed as a computationally efficient way to implement online pseudospectral methods for nonlinear model predictive control (NMPC). The present paper extends this work based on a rank-o...

There are several efficient direct solvers for structured systems of linear equations defining search directions in primal-dual interior point methods applied to constrained model predictive control problems. We propose reusing matrix decompositions of direct solvers as preconditioners in Krylov-subspace methods applied to subsequent iterations of...

In this paper we present acados, a new software package for model predictive control. It provides a collection of embedded optimization algorithms written in C, with a strong focus on computational efficiency. Its modular structure makes it useful for rapid prototyping, i.e. designing a control algorithm by putting together different algorithmic co...

In this paper, a strategy is proposed to reduce the computational burden associated with the solution of problems arising in nonlinear model predictive control. The prediction horizon is split into two sections and the constraints associated with the terminal one are tightened using a barrier formulation. In this way, when using the Real-Time Itera...

Moving horizon estimation (MHE) solves a constrained dynamic optimization problem at each sampling instant. Including nonlinear dynamics into an optimal estimation problem generally comes at the cost of tackling a non-convex optimization problem. In this article, a particular model formulation is proposed in order to convexify a class of nonlinear...

Innovative charging concepts, such as two-stage turbocharging for gasoline engines, cause high demands on the process control. The open-loop process is characterized by a complex, nonlinear system behavior. In addition, the requirements on the closed-loop system are challenging: fast reference tracking has to be achieved without overshoots while re...

This paper presents and analyzes an Inexact Newton-type optimization method based on Iterated Sensitivities (INIS). A particular class of Nonlinear Programming (NLP) problems is considered, where a subset of the variables is defined by nonlinear equality constraints. The proposed algorithm considers any problem-specific approximation for the Jacobi...

Nonlinear Model Predictive Control (NMPC) requires the efficient treatment of the dynamic model in the form of a system of continuous-time differential equations. Newton-type optimization relies on a numerical simulation method in addition to the propagation of first or higher order derivatives. In the case of stiff or implicitly defined dynamics,...

This paper introduces a homotopy-based nonlinear interior-point method that can exploit warm-starts for an efficient real-time implementation of nonlinear model predictive control (NMPC). The algorithm performs a homotopy on a tightened problem with a fixed value of the barrier parameter during which the initial state is changed gradually. Once an...

Nonlinear Model Predictive Control (NMPC) requires the efficient treatment of the dynamic model in the form of a system of continuous-time differential equations. Newton-type optimization relies on a numerical simulation method in addition to the propagation of first or higher order derivatives. In the case of stiff or implicitly defined dynamics,...

Efficient integration schemes with sensitivity propagation are crucial for deploying real-time Nonlinear Model Predictive Control on systems described by continuous time dynamics. Implicit integration schemes are preferred when stiff modes are present in the model equations, or when the equations are implicit. We consider here a class of models, wh...

This paper introduces a homotopy-based nonlinear interior-point method that can exploit warm-starts for an efficient real-time implementation of nonlinear model predictive control (NMPC). The algorithm performs a homotopy on a tightened problem with a fixed value of the barrier parameter during which the initial state is changed gradually. Once an...

Quadratic programs (QP) with an indefinite Hessian matrix arise naturally in some direct optimal control methods, e.g., as subproblems in a sequential quadratic programming scheme. Typically, the Hessian is approximated with a positive definite matrix to ensure having a unique solution; such a procedure is called regularization. We present a novel...

This paper presents a class of efficient Newton-type algorithms for solving the nonlinear programs (NLPs) arising from applying a direct collo-cation approach to continuous time optimal control. The idea is based on an implicit lifting technique including a condensing and expansion step, such that the structure of each subproblem corresponds to tha...

Dynamic optimization based control and estimation techniques have gained increasing popularity, because of their ability to treat a wide range of problems and applications. They rely on the explicit formulation of a cost function, which needs to be minimized given the constraints of the problem and the system dynamics. Especially in the context of...

Direct optimal control algorithms first discretize the continuous-time optimal control problem and then solve the resulting finite dimensional optimization problem. If Newton type optimization algorithms are used for solving the discretized problem, accurate first as well as second order sensitivity information needs to be computed. This article de...

This paper is concerned with tube-based model predictive control (MPC) for both linear and nonlinear, input-affine continuous-time dynamic systems that are affected by time-varying disturbances. We derive a min-max differential inequality describing the support function of positive robust forward invariant tubes, which can be used to construct a va...

Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to recent progress in algorithms for solving online the underlying structured quadratic programs. In contrast, nonlinear MPC (NMPC) requires the deployment of more elaborate algorithms, which require longer computation times than linear MPC. Nonetheless, c...

Moving Horizon Estimation (MHE) is a powerful, yet computationally expensive approach for state and parameter estimation that is based on online optimization. In applications with multi-rate measurements that may include outliers, the Huber penalty is often a better candidate for the MHE objective than the commonly used Euclidean norm. Treating thi...

Nonlinear Model Predictive Control (NMPC) schemes offer the possibility to handle complex system dynamics and advanced requirements on control, such as the consideration of constraints for a multiple input multiple output system. The major bottleneck for these algorithms is the computation time. In this paper it is shown how the different ingredien...

In this paper, an inexact nonlinear model predictive control scheme with reduced computational complexity is proposed. The presented approach exploits fixed sensitivity information precomputed offline at a reference value. This allows one to avoid the online computational effort resulting from the propagation of sensitivities and possibly the corre...

Direct optimal control first discretizes the continuous time Optimal Control Problem (OCP) and then solves the resulting Nonlinear Program (NLP). Implicit integration schemes are used for the numerical simulation of stiff or implicitly defined dynamics. The propagation of sensitivities is often computationally demanding, especially when second orde...

This work presents an embedded nonlinear model predictive control (NMPC) strategy for autonomous vehicles under a minimum time objective. The time-optimal control problem is stated in a path-parametric formulation such that existing reliable numerical methods for real-time nonlinear MPC can be used. Building on previous work on time-optimal driving...

Advanced driver assistant systems (ADAS) are primarily introduced to increase safety in every day trafic situations. Adaptive cruise control (ACC) systems represent an important example for such ADAS. The worldwide increasing trafic volume and the demand for the reduction of overall emissions call for the development of ADAS which concern not only...

Nonlinear Model Predictive Control (NMPC) relies on solving an Optimal Control Problem (OCP) online at every sampling time. The discretization of the continuous time dynamics requires the deployment of some numerical integration method. To that end, implicit integrators are often preferred when stiff or implicitly defined dynamics are present in th...

Innovative charging concepts such as two-stage turbocharging for gasoline engines, cause high demands on the process control due to the complex, nonlinear system behavior. For complex, nonlinear systems Nonlinear Model-based Predictive Controllers (NMPC) offer a high potential. They are capable of handling coupled multiple-input systems while achie...

Nonlinear Model Predictive Control (NMPC) requires the online solution of an Optimal Control Problem (OCP) at every sampling instant. In the context of multiple shooting, a numerical integration is needed to discretize the continuous time dynamics. For stiff, implicitly defined or differential-algebraic systems, implicit schemes are preferred to ca...

A recently proposed quadratic programming (QP) solver dedicated to band-structured optimal control problems as they may arise in the field of model predictive control (MPC) is the dual Newton strategy, as implemented in the publicly available software qpDUNES. Such a structure-exploiting solver is often preferable over the use of condensing for pro...

Model Predictive Control (MPC) requires the online solution of an Optimal Control Problem (OCP) at each sampling time. Efficient online algorithms such as the Real-Time Iteration (RTI) scheme have been developed for real-time MPC implementations even for fast nonlinear dynamic systems. The RTI framework is based on direct Multiple Shooting (MS) for...

Nonlinear Model Predictive Control (NMPC) is a feedback control technique that uses the most current state estimate of a nonlinear system to compute an optimal plan for the future system behavior. This plan is recomputed at every sampling time, creating feedback. Thus, NMPC needs to repeatedly solve a nonlinear optimal control problem (OCP). Direct...

Nonlinear Model Predictive Control (NMPC) is a strong candidate for the control of large Multi-Mega Watt Wind Turbine Generators (WTG), especially when reliable Light Detection And Ranging (LIDAR) systems are available. Recently, a real-time NMPC for WTG control has been proposed, but had a limited reliability if deployed over the full WTG operatin...

Economic Model Predictive Control (EMPC) is an advanced receding horizon based control technique which optimizes an economic objective subject to potentially nonlinear dynamic equations as well as control and state constraints. The main contribution of this paper is an algorithmic differentiation (AD) based real-time EMPC algorithm including a soft...