Optimal and robust controllers design for a smartphone-based quadrotor

Astudillo, Alejandro; Bacca, Bladimir; Rosero, Esteban

doi:10.1109/ccac.2017.8276392

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…Subsequently, trajectory tracking tests were performed, where the performance of the non-linear MPC controller based on the model that includes the aerodynamic effects was compared with H ∞ and LQI controllers, which were designed based on the linearized model of the quadrotor, as shown in [18]. Tests were performed using Matlab and Simulink, and results are shown in Table 2.…”

Section: Implementation Resultsmentioning

confidence: 99%

“…A nonlinear MPC (NMPC) controller was compared with H ∞ and LQI controllers [18] in path tracking tests, where NMPC control reached 50% lower tracking error in square trajectory, 80% less for helical trajectory and 40 − 50% less for the crop trajectory. Estimated energy consumption was about 2% higher than H ∞ and LQI controllers in the square trajectory and about 1% higher in the helical and crop trajectory, due to the control effort.…”

Section: Discussionmentioning

confidence: 99%

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Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

Garcia Linares,

Rosero García

2023

JAMRIS

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In this paper, the design and implementation of a non-linear model-based predictive controller (NMPC) for predefined trajectory tracking and to minimize the control effort of a smartphone-based quadrotor is carried out. The optimal control actions are calculated in each iteration by means of an optimal control algorithm based on the non-linear model of the quadrotor, considering some aerodynamic effects. Control algorithm implementation and simulation tests are executed on a smartphone using the CasADi framework. In addition, a technique for estimating the energy consumed based on control signals is presented. NMPC controller performance is compared to an H-infinity controller and an LQI controller, using three predefined trajectories, where the NMPC average tracking error was around 50\% lower, and average estimated power and energy consumption slightly higher, with respect to the H-infinity and LQI controllers.

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Section: Implementation Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

Garcia Linares,

Rosero García

2023

JAMRIS

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“…After implementing a linear MPC in Android using the Java library JOptimizer [16], now we extend the possibilities of control algorithm implementation in smartphones with the use of CasADi code-generation capabilities and the Java Native Interface (JNI).…”

Section: Luis Garcia and Alejandro Astudillo Contributed Equally To This Workmentioning

confidence: 99%

“…The estimated state vector x est is delivered by a Kalman filter, which is executed in the smartphone. The reader is referred to [5] to extend the description of the implemented state estimator.…”

Section: B Convex Optimization Problemmentioning

confidence: 99%

“…In [4], a motion planning application and a quadrotor control, including visual odometry for mapping, was developed. Moreover, optimal and robust controllers, and a state estimator based on a Kalman filter for a smartphone-based quadrotor were described in [5], while in [6] an attitude and altitude cascade controller for a smartphone-based quadrotor was exposed.…”

Section: Introductionmentioning

confidence: 99%

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Fast Model Predictive Control on a Smartphone-based Flight Controller

García

Astudillo

Rosero

2019

2019 IEEE 4th Colombian Conference on Automatic Control (CCAC)

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Several attempts have been made to execute control of a system using only a smartphone's processor running computationally inexpensive algorithms such as PID, LQR or H∞ controllers. This paper presents design and implementation of model predictive controllers on a smartphone using the numerical optimization framework CasADi. To evaluate this framework's performance (and compare its results with those from a Java library JOptimizer's deployment) the implemented model predictive control algorithm was subjected to simulations of running a quadrotor control system on a smartphone. It attained a tracking error of 0.0693 m. These evaluation results open the possibility of implementing more computationally expensive algorithms on a smartphone's processor including online or real-time usage.

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Attitude and Altitude Linear Parameter-Varying Control System for a Smartphone-based Quadcopter

Muñoz,

Rosero

2023

2023 IEEE 6th Colombian Conference on Automatic Control (CCAC)

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Optimal and robust controllers design for a smartphone-based quadrotor

Cited by 7 publications

References 11 publications

Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

Fast Model Predictive Control on a Smartphone-based Flight Controller

Attitude and Altitude Linear Parameter-Varying Control System for a Smartphone-based Quadcopter

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