MPC Based Feedback-Linearization Strategy of a Fixed-Wing UAV

2020 ◽  
Author(s):  
Leonardo A. A. Pereira ◽  
Luciano C. A. Pimenta ◽  
Guilherme V. Raffo
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Feedback Linearization ◽  
Degrees Of Freedom ◽  
Vehicle Control ◽  
Control Allocation ◽  
Linearization Technique ◽  
Aerial Vehicle ◽  
The Relationship ◽  
Curve Tracking

This work proposes a xed-wing UAV (Ummaned Aerial Vehicle) control strategy based on feedback-linearization and model predictive control (MPC). The strategy makes use of the relationship between the applied control inputs of the UAV and the generalized forces and moments actuating on it. A linear model is obtained by the exact feedback-linearization technique, followed by the use of MPC to solve the trajectory tracking and the control allocation problems. The proposed controller is capable of actuating on the 6 DOF (Degrees of Freedom) of the UAV, avoiding inherited restrictions when the model is decoupled. The proposed strategy is applied in a curve tracking task. Simulations are performed using MATLAB software, and the results show the eciency of the proposed control strategy.

Nonlinear system controllability analysis and autopilot design for bank-to-turn aircraft with two flaps

2018 ◽  
Vol 233 (5) ◽  
pp. 1772-1783 ◽  
Author(s):  
Jinlu Dong ◽  
Di Zhou ◽  
Chuntao Shao ◽  
Shikai Wu
Keyword(s):  
Numerical Simulation ◽  
Control System ◽  
Nonlinear System ◽  
Feedback Linearization ◽  
Degrees Of Freedom ◽  
Lyapunov Stability Theory ◽  
Zero Dynamics ◽  
Controlled System ◽  
Linearization Technique ◽  
Six Degrees Of Freedom

In this study, the six-degrees-of-freedom flight motion of a tail-controlled bank-to-turn aircraft with two flaps is described as a nonlinear control system. The controllability of this flap-controlled system is analyzed based on nonlinear controllability theory and the system is proved to be weakly controllable. By choosing the angle-of-attack and roll angle as the outputs of this control system, the zero dynamics of the system are analyzed using Lyapunov stability theory, and are proved to be stable under some conditions given by an inequality. Then an autopilot is designed for this system using the feedback linearization technique. Results of the numerical simulation for this control system show the effectiveness of the controllability analysis and autopilot design.

Robust generalized predictive control of the Orthoglide robot

2014 ◽  
Vol 41 (3) ◽  
pp. 275-285 ◽  
Author(s):  
Fabian Andres Lara-Molina ◽  
João Maurício Rosário ◽  
Didier Dumur ◽  
Philippe Wenger
Keyword(s):  
Computational Complexity ◽  
Predictive Control ◽  
Control Strategy ◽  
Feedback Linearization ◽  
Generalized Predictive Control ◽  
Robot Dynamics ◽  
Tracking Accuracy ◽  
Content Type ◽  
Control Loops ◽  
Youla Parameterization

Purpose – The purpose of this paper is to address the synthesis and experimental application of a generalized predictive control (GPC) technique on an Orthoglide robot. Design/methodology/approach – The control strategy is composed of two control loops. The inner loop aims at linearizing the nonlinear robot dynamics using feedback linearization. The outer loop tracks the desired trajectory based on GPC strategy, which is robustified against measurement noise and neglected dynamics using Youla parameterization. Findings – The experimental results show the benefits of the robustified predictive control strategy on the dynamical performance of the Orthoglide robot in terms of tracking accuracy, disturbance rejection, attenuation of noise acting on the control signal and parameter variation without increasing the computational complexity. Originality/value – The paper shows the implementation of the robustified predictive control strategy in real time with low computational complexity on the Orthoglide robot.

Unified NMPC framework for attitude and position control for a VTOL UAV

2019 ◽  
Vol 233 (7) ◽  
pp. 889-903 ◽  
Author(s):  
André Murilo ◽  
Renato Vilela Lopes
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicle ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Strategy ◽  
Position Control ◽  
Nonlinear Model Predictive Control ◽  
Decision Variables ◽  
Vtol Uav ◽  
Aerial Vehicle

In this article, a parameterized nonlinear model predictive control strategy is developed for trajectory tracking and stabilization of a quadrotor unmanned aerial vehicle. The control strategy handles structurally with constraints on commands and input derivatives and deals with model uncertainties and white noise on whole state vector. Moreover, the resulting parametrization decreases the number of decision variables related to the nonlinear optimization problem substantially, which highly facilitates the attainment of the optimal control sequence. The proposed nonlinear model predictive control framework also enables the use of different nonlinear formulations of the unmanned aerial vehicle whatever the complexity of the model being used. Simulation results for different flight conditions are presented in order to show the efficiency of the tracking performance and highlight the advantages of the proposed control scheme.

scholarly journals A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1822
Author(s):  
Norberto Urbina-Brito ◽  
María-Eusebia Guerrero-Sánchez ◽  
Guillermo Valencia-Palomo ◽  
Omar Hernández-González ◽  
Francisco-Ronay López-Estrada ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Predictive Control ◽  
Control Strategy ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Lagrange Formulation ◽  
Aerial Vehicle ◽  
Load Swing ◽  
The Mathematical Model ◽  
Three Dimensional Space

This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of ±10∘. Numerical simulations were carried out to validate the control strategy.

scholarly journals Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2293
Author(s):  
Yu-Chen Lin ◽  
Chun-Liang Lin ◽  
Shih-Ting Huang ◽  
Cheng-Hsuan Kuo
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Detection System ◽  
Vehicle Control ◽  
Lane Detection ◽  
Driving Safety ◽  
Area Network ◽  
Sensors And Actuators ◽  
Lateral Velocity

According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity.

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