Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter

Wang, Ye; Ramirez-Jaime, A.; Xu, Feng; Puig, Vicenç

doi:10.1088/1742-6596/783/1/012025

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…MPC is an optimization-based technique for controlling the attitude of a quadcopter [45]. It requires the solution of a finite-horizon optimal control challenge subjected to the quadcopter rotational dynamics established in the previous section, reference inputs from BC, and trajectory and state limitations introduced on the quadcopter.…”

Section: Model Predictive Control Structure and Designmentioning

confidence: 99%

An optimal hybrid quadcopter control technique with MPC-based backstepping

Nwafor,

Eneh,

Ndefo

et al. 2024

Archives of Control Sciences

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Quadcopter unmanned aerial vehicle is a multivariable, coupled, unstable, and underactuated system with inherent nonlinearity. It is gaining popularity in various applications and has been the subject of numerous research studies. However, modelling and controlling a quadcopter to follow a trajectory is a challenging issue for which there is no unique solution. This study proposes an optimal hybrid quadcopter control with MPC-based backstepping control for following a reference trajectory. The outer-loop controller (backstepping controller) regulates the quadcopter’s position, whereas the inner-loop controller (Model Predictive Control) regulates its attitude. The translational and rotational dynamics of the quadcopter are analyzed utilizing the Newton-Euler method. After that, the backstepping controller (BC) is created, which is a recurrent control method according to Lyapunov’s theory that utilizes a genetic algorithm (GA) to choose the controller parameters automatically. In order to apply a linear control technique in the presence of nonlinearities in the quadcopter dynamics, Linear Parameter Varying (LPV) Model Predictive Control (MPC) structure is developed. Simulation validated the dynamic performance of the proposed optimal hybrid MPC-based backstepping controller of the quadcopter in following a given reference trajectory. The simulations demonstrate the fact that using a command control input in trajectory tracking, the proposed control algorithm offers suitable tracking over the assigned position references with maximum appropriate tracking errors of 0.1 m for the �� and �� positions and 0.15 m for the �� position.

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Section: Model Predictive Control Structure and Designmentioning

confidence: 99%

An optimal hybrid quadcopter control technique with MPC-based backstepping

Nwafor,

Eneh,

Ndefo

et al. 2024

Archives of Control Sciences

View full text Add to dashboard Cite

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“…Similarly to [18]- [21] that deal with related topics, we also take into account actuator limitations in our formulation, thus showing that the various sensitivity quantities and related gradients can also be used in a nonlinear optimization context (opening the door to consider even more complex constraints). As opposed to classical approaches to robust planning/control, such as belief space [22] or robust MPC [23], which need to evaluate the worst-case deviation caused by uncertainty by large-scale sampling, here we address the robustness by increasing the predictability of inputs and outputs over a trajectory by minimizing a (much simpler) single sensitivity index.…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Planning with Parametric Uncertainties

Brault

Delamare

Giordano

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper we extend the previously introduced notion of closed-loop state sensitivity by introducing the concept of input sensitivity and by showing how to exploit it in a trajectory optimization framework. This allows to generate an optimal reference trajectory for a robot that minimizes the state and input sensitivities against uncertainties in the model parameters, thus producing inherently robust motion plans. We parametrize the reference trajectories with Béziers curves and discuss how to consider linear and nonlinear constraints in the optimization process (e.g., input saturations). The whole machinery is validated via an extensive statistical campaign that clearly shows the interest of the proposed methodology.

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“…Also, it has been shown that the closed‐loop responses of the linear MPC and NMPC for trajectory tracking under external disturbances are comparable, while the NMPC has demonstrated slightly better disturbance rejection capability. A NMPC has been introduced in [9] for trajectory tracking control of a quadcopter unmanned aerial vehicle (UAV) considering the system constraints in the design steps. Both position and velocity components of the vehicle have been utilised in the optimisation problem of the MPC in order to provide a precise trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%