A toolkit for nonlinear model predictive control using gradient projection and code generation

Kalmari, Jouko; Backman, Juha; Visala, Arto

doi:10.1016/j.conengprac.2015.01.002

Cited by 32 publications

(23 citation statements)

References 17 publications

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“…Optimising w.r.t the decision variables, (δÛ) and (Û), results in the modified Hessian (22) for both modified cost functions, (20) and (21), respectively.…”

Section: Blocked Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Shifting strategy for efficient block‐based non‐linear model predictive control using real‐time iterations

Villarreal

Rossiter

2020

IET Control Theory & Applications

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Nonlinear Model Predictive Control (NMPC) requires the use of efficient solutions and strategies for its implementation in fast/real-time systems. A popular approach for this is the Real Time Iteration (RTI) Scheme which uses a shifting strategy, namely the Initial Value Embedding (IVE), that shifts the solution from one sampling time to the next. However, this strategy together with other efficient strategies such as Move Blocking, present a recursive feasibility problem. This paper proposes a novel modified shifting strategy which preserve both recursive feasibility and stability properties, as well as achieves a significant reduction in the computational burden associated with the optimisation. The proposed approach is validated through a simulation of an inverted pendulum where it clearly outperforms other standard solutions in terms of performance and recursive feasibility properties. Additionally, the approach was tested on two computing platforms: a laptop with an i7 processor and a Beaglebone Blue Linux-based computer for robotic systems, where computational gains compared to existing approaches are shown to be as high as 100 times faster.

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“…Optimising w.r.t the decision variables, (δÛ) and (Û), results in the modified Hessian (22) for both modified cost functions, (20) and (21), respectively.…”

Section: Blocked Solutionsmentioning

confidence: 99%

“…In [21], a toolkit named VIA-TOC that also exports automatically generated code is presented. Other toolkits such as CasADI and GRAMPC are also discussed in [21]. Another important area of research is the development of efficient QP solvers.…”

Section: Introductionmentioning

confidence: 99%

Shifting strategy for efficient block‐based non‐linear model predictive control using real‐time iterations

Villarreal

Rossiter

2020

IET Control Theory & Applications

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“…Table 1 gives an overview of the considered MPC benchmark problems in terms of the system dimension, the type of constraints (control/state/general nonlinear constraints), the dynamics (linear/nonlinear and explicit/semi-implicit) as well as the respective references. The MPC examples are evaluated with GRAMPC as well as with ACADO Toolkit [30] and VIATOC [33].…”

Section: General Mpc Evaluationmentioning

confidence: 99%

“…The well-known ACADO Toolkit [30] uses the above-mentioned active set strategy in combination with a real-time iteration scheme to efficiently solve nonlinear MPC problems. Another recently presented MPC toolkit is VIATOC [33] that employs a projected gradient method to solve the time-discretized, linearized MPC problem.…”

Section: Introductionmentioning

confidence: 99%

A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC)

et al. 2019

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A nonlinear MPC framework is presented that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. The algorithm is implemented in the software framework GRAMPC and is a fundamental revision of an earlier version. Detailed performance results are presented for a test set of benchmark problems and in comparison to other nonlinear MPC packages. In addition, runtime results and memory requirements for GRAMPC on ECU level demonstrate its applicability on embedded hardware.

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“…The MPC-based control scheme is developed for hydraulic forestry crane [32,33], boom crane [34], and laboratory models of a gantry crane [35] and overhead crane [36,37]. The feasibility and efficiency of GPC algorithm are also verified in numerical experiments carried out on a model of an overhead crane [38].…”

Section: Introductionmentioning

confidence: 99%

Generalized Predictive Control of an overhead crane

Smoczek

Szpytko

2015

2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR)

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The adaptive control of an overhead crane is developed based on Generalized Predictive Control (GPC) technique. The one-step ahead predictive control scheme is proposed for reducing the residual and transient oscillations of a payload suspended on a rope and transferred by a crane, with respect to the variation of rope length and mass of a payload. The recursive least square (RLS) algorithm is applied to real-time estimate parameters of discrete-time model of a crane dynamic system. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on the laboratory stand. The results of experiments are presented and compared with performances of the linear controllers-based scheme designed using pole placement method. Keywords-overhead crane, generalized predictive control, recursive least squareI.

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A toolkit for nonlinear model predictive control using gradient projection and code generation

Cited by 32 publications

References 17 publications

Shifting strategy for efficient block‐based non‐linear model predictive control using real‐time iterations

Shifting strategy for efficient block‐based non‐linear model predictive control using real‐time iterations

A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC)

Generalized Predictive Control of an overhead crane

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