HPIPM: a high-performance quadratic programming framework for model predictive control

Frison, Gianluca; Diehl, Moritz

doi:10.1016/j.ifacol.2020.12.073

Cited by 129 publications

(58 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q s,q = diag(0, 1000) and Q s,l = (0, 1000). We used HPIPM [27] solver integrated in acados [28] library to find the solutions of the problem (2). The problem (4), instead, is solved using eiquadprog [29].…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Bratta¹,

Focchi²,

Rathod³

et al. 2022

Preprint

View full text Add to dashboard Cite

Model Predictive Control (MPC) approaches are widely used in robotics, since they allow to compute updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of parameters of the cost function in order to obtain good performance. When for example, a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a certain goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work we propose a novel optimization-based Reference Generator, named Governor, which exploits a Linear Inverted Pendulum model to compute reference trajectories for the Center of Mass, while taking into account the possible under-actuation of a gait (e.g. in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work [1]. We also present a formulation that can guarantee a certain response time to reach a goal, without the need to tune the weights of the cost terms. In addition, foothold locations are corrected to drive the robot towards the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

show abstract

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Bratta¹,

Focchi²,

Rathod³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To this end, we have completely reimplemented our optimization problem using acados [23] as a code generation tool, in contrast to [6], where ACADO [24] was used. The main benefit of using acados is that they provide an interface to HPIPM (High Performance Interior Point Method) solver [25]. HPIPM solves optimization problems using BLASFEO [26], a linear algebra library specifically designed for embedded optimization.…”

Section: A Implementation Detailsmentioning

confidence: 99%

Time-Optimal Online Replanning for Agile Quadrotor Flight

Romero,

Penicka,

Scaramuzza

2022

Preprint

View full text Add to dashboard Cite

In this paper, we tackle the problem of flying a quadrotor using time-optimal control policies that can be replanned online when the environment changes or when encountering unknown disturbances. This problem is challenging as the time-optimal trajectories that consider the full quadrotor dynamics are computationally expensive to generate (order of minutes or even hours). We introduce a sampling-based method for efficient generation of time-optimal paths of a point-mass model. These paths are then tracked using a Model Predictive Contouring Control approach that considers the full quadrotor dynamics and the single rotor thrust limits. Our combined approach is able to run in real-time, being the first time-optimal method that is able to adapt to changes on-the-fly. We showcase our approach's adaption capabilities by flying a quadrotor at more than 60 km/h in a racing track where gates are moving. Additionally, we show that our online replanning approach can cope with strong disturbances caused by winds of up to 68 km/h.

show abstract

“…In this study, the MATLAB Toolbox utilizing the fmincon solver is used NMPC simulation. For comparison, state-of-theart commercial solver FORCES PRO by EMBOTECH [48,49] and open-source package acados [50,51] with the QP solver HPIPM (High-Performance Interior-Point Method) [52] are also used to solve the NMPC optimization. In NMPC, the OCP structured NLP is reformulated into a Sequential Quadratic Programming (SQP) problem by means of iterative quadratic approximations at the shooting nodes [13].…”

Section: Nonlinear Model Predictive Controller Designmentioning

confidence: 99%

Deep Learning based Model Predictive Control for Compression Ignition Engines

Norouzi¹,

Shahpouri²,

Gordon³

et al. 2022

Preprint

View full text Add to dashboard Cite

Machine learning (ML) and nonlinear model predictive control (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is used for two applications. In the first application, ML is to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of a model predictive controller where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emission and performance of a 4.5 liter 4-cylinder Cummins compression ignition engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the calibrated Cummins Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (NO x ) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with two orders of magnitude less computation time.

show abstract

HPIPM: a high-performance quadratic programming framework for model predictive control

Cited by 129 publications

References 22 publications

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Time-Optimal Online Replanning for Agile Quadrotor Flight

Deep Learning based Model Predictive Control for Compression Ignition Engines

Contact Info

Product

Resources

About