Algorithms for real-time, embedded optimization need to run within tight computational times, and... more Algorithms for real-time, embedded optimization need to run within tight computational times, and preferably on embedded control hardware for which only limited computational power and memory is available. A computationally demanding step of these algorithms is the model simulation with sensitivity generation. This paper presents an implementation of code generation for Implicit Runge-Kutta (IRK) methods with efficient sensitivity generation, which outperforms other solvers for the targeted applications. The focus of this paper will be on the extension of the proposed tool to the integration of index-1 Differential Algebraic Equations (DAE), and continuous output functions, which are crucial for e.g. performing sensor fusion with measurements provided at very high sampling rates. The new tool is provided with a powerful MATLAB interface. It is illustrated in simulation for the trajectory estimation of a mechanical system modeled by complex Differential-Algebraic equations, using sensor information provided at fast, multi-rate sampling frequencies.
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
ABSTRACT For extreme maneuvers, feasible flight trajectories can be difficult to compute. If the ... more ABSTRACT For extreme maneuvers, feasible flight trajectories can be difficult to compute. If the aircraft is controlled based on linear approximations of the system dynamics that are computed along infeasible trajectories, poor control actions and violations of the flight envelope constraints can result. This paper proposes a Non-linear Model Predictive Control (NMPC) approach, to handle extreme maneuvers, respect the flight envelope, handle actuator failure, and perform emergency obstacle avoidance with a single, non-hierarchical controller that can be implemented in real time.
Algorithms for real-time, embedded optimization need to run within tight computational times, and... more Algorithms for real-time, embedded optimization need to run within tight computational times, and preferably on embedded control hardware for which only limited computational power and memory is available. A computationally demanding step of these algorithms is the model simulation with sensitivity generation. This paper presents an implementation of code generation for Implicit Runge-Kutta (IRK) methods with efficient sensitivity generation, which outperforms other solvers for the targeted applications. The focus of this paper will be on the extension of the proposed tool to the integration of index-1 Differential Algebraic Equations (DAE), and continuous output functions, which are crucial for e.g. performing sensor fusion with measurements provided at very high sampling rates. The new tool is provided with a powerful MATLAB interface. It is illustrated in simulation for the trajectory estimation of a mechanical system modeled by complex Differential-Algebraic equations, using sensor information provided at fast, multi-rate sampling frequencies.
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
ABSTRACT For extreme maneuvers, feasible flight trajectories can be difficult to compute. If the ... more ABSTRACT For extreme maneuvers, feasible flight trajectories can be difficult to compute. If the aircraft is controlled based on linear approximations of the system dynamics that are computed along infeasible trajectories, poor control actions and violations of the flight envelope constraints can result. This paper proposes a Non-linear Model Predictive Control (NMPC) approach, to handle extreme maneuvers, respect the flight envelope, handle actuator failure, and perform emergency obstacle avoidance with a single, non-hierarchical controller that can be implemented in real time.
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Papers by Rien Quirynen