Fast Predictive Control: Real-time Computation and Certification

Jones, Colin N.; Domahidi, Alexander; Morari, Manfred; Richter, Stefan; Ullmann, Fabian; Zeilinger, Melanie N.

doi:10.3182/20120823-5-nl-3013.00075

Cited by 27 publications

(24 citation statements)

References 6 publications

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“…The ideal The formulation (10) and (11) is a standard quadratic program and it has been implemented using the QP solver qpOASES [46]. Extensive research has been performed to develop fast, real-time solvers for quadratic programs [47][48][49] enabling real-time implementation in aircraft computer systems. Additionally if we take a closer look at the problem (10) and (11) we can see that it in fact only has the two free variables, µ and ν, to optimize.…”

Section: Simulation Resultsmentioning

confidence: 99%

Command Governor Approach to Maneuver Limiting in Fighter Aircraft

Simon

Härkegård²,

Löfberg

2017

Journal of Guidance, Control, and Dynamics

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Section: Simulation Resultsmentioning

confidence: 99%

Command Governor Approach to Maneuver Limiting in Fighter Aircraft

Simon

Härkegård²,

Löfberg

2017

Journal of Guidance, Control, and Dynamics

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“…Fast gradient methods are first order optimization methods that perform weighted/filtered gradient descent steps and constraint projections, usually taking advantage of the very simple projection needed to solve the dual problem (i.e. positive Lagrange multipliers) for constraint fulfillment, [33], [34], [4], [35]. In addition to very simple code that leads to a small footprint and may allow formal verification of the code, one can bound the number iterations needed to achieve a given accuracy.…”

Section: Embedded Mpc Algorithms and Softwarementioning

confidence: 99%

Toward Dependable Embedded Model Predictive Control

Johansen

2017

IEEE Systems Journal

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Abstract-While Model Predictive Control (MPC) is the industrially preferred method for advanced control in the process industries, it has not found much use in consumer products and safety-critical embedded systems applications in industries such as automotive, aerospace, medical and robotics. The main barriers are implementability and dependability, where important factors are implementation of advanced numerical optimization algorithms on resource-limited embedded computing platforms and the associated complexity of verification. This challenge comes from a requirement of the use of ultra-reliable hardware and software architectures in safety-critical applications, low-cost hardware in consumer products, or both. This paper surveys the state-of-the-art in the emerging field of dependable embedded MPC, and discusses some key challenges related to its design, implementation and verification. A novel result is the study of a simulator-based performance monitoring and control selection method that monitors and predicts MPC performance and switches to a highly reliable backup controller in cases when the MPC experiences performance issues.

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“…Note however, that a thorough benchmark is not the aim of the paper and hence the results intend to demonstrate roughly where GRAMPC stands in terms of performance. As regards the comparison, the code generation tool included in the ACADO Toolkit 3 [6], AutoGenU 4 [7], the FMPC code 5 [3] as well as FORCES and FiOrdOs 6 [4], [5] were applied for stabilizing the origin in an MPC fashion. All MPC solvers were also implemented within an s-function for use in MATLAB/SIMULINK where the computation times were measured using the SIMULINK profiler for multiple MPC runs.…”

Section: B Quadrotormentioning

confidence: 99%

“…The presented methods in [3], [4], [5] are suited for linear, time discrete systems and use efficient solution strategies, interior point approaches tailored to convex multistage problems and first order methods. The software environment ACADO Toolkit [6] provides a module to generate optimized C code for real-time MPC which can be used to control nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

The gradient based nonlinear model predictive control software GRAMPC

Käpernick

Graichen

2014

2014 European Control Conference (ECC)

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Abstract-This paper presents the nonlinear model predictive control (MPC) software GRAMPC (GRAdient based MPC -[graemp si:]) which is suited for controlling nonlinear systems with input constraints in the (sub)millisecond range. GRAMPC is based on a real-time solution strategy in combination with a (projected) gradient method. It is written in plain C with an interface to MATLAB/SIMULINK and also provides a graphical user interface (GUI) in MATLAB for a convenient MPC design and tuning. The performance of GRAMPC is demonstrated by two examples from different technical fields. Additionally, some comparison results with established MPC software are provided. I. INTRODUCTIONModel predictive control (MPC) has become a popular and powerful control methodology due to its ability to handle nonlinear multiple input systems as well as constraints [1], [2]. It is based on the solution of an underlying optimal control problem (OCP) which is solved online at each sampling instance and typically requires a considerable computational effort. This drawback usually limits the applicability of model predictive control to sufficiently slow or lowdimensional processes.There exist several MPC approaches and software packages to cope with the problem of real-time applicability for linear and nonlinear systems. The presented methods in [3], [4], [5] are suited for linear, time discrete systems and use efficient solution strategies, interior point approaches tailored to convex multistage problems and first order methods. The software environment ACADO Toolkit [6] provides a module to generate optimized C code for real-time MPC which can be used to control nonlinear systems. An alternative nonlinear MPC approach developed in [7] employs a continuation method to trace the solution of the optimality conditions over a single MPC step based on a generalized minimum residual (GMRES) method. This paper presents the portable and user-friendly nonlinear MPC software GRAMPC (GRAdient based MPC -[graemp si:]). It is based on a (projected) gradient method from optimal control [8], [9] and includes a real-time solution strategy for controlling nonlinear systems with input constraints and real-time demands. GRAMPC is written in plain C without the use of external libraries resulting in a small code size and a high level of portability in view of different operating systems and platforms. It is well suited to handle fast and high-dimensional systems and also provides a user-friendly integration to MATLAB/SIMULINK with an additional graphical user interface (GUI) in MATLAB

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Fast Predictive Control: Real-time Computation and Certification

Cited by 27 publications

References 6 publications

Command Governor Approach to Maneuver Limiting in Fighter Aircraft

Command Governor Approach to Maneuver Limiting in Fighter Aircraft

Toward Dependable Embedded Model Predictive Control

The gradient based nonlinear model predictive control software GRAMPC

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