Tube-based MPC with Nonlinear Control for Load Transportation using a UAV

Santos, Max André dos; Ferramosca, Antonio; Raffo, Guilherme V.

doi:10.1016/j.ifacol.2018.11.180

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…Soon after the original LTI TMPC papers, TMPC was extended to nonlinear cases, as in (Mayne et al, 2011;Santos et al, 2018), and to time-varying processes, as done in (Gonzalez et al, 2011). These developments opened doors to a LPV TMPC formulation.…”

Section: Tube-based Methodsmentioning

confidence: 99%

Model predictive control design for linear parameter varying systems: A survey

Morato

Normey‐Rico

Sename

2020

Annual Reviews in Control

140

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Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods.

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Section: Tube-based Methodsmentioning

confidence: 99%

Model predictive control design for linear parameter varying systems: A survey

Morato

Normey‐Rico

Sename

2020

Annual Reviews in Control

140

View full text Add to dashboard Cite

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“…Artificial trajectories have also been proposed in the literature; earlier works are available in the context of the control of periodic linear time-varying system [21]. A more recent formulation for trajectory tracking with obstacle avoidance, that is based on an economic NMPC strategy and makes use of auxiliary trajectories, can be seen in [36]. Following the ideas presented in [19] and further extended in [20], auxiliary variables are used as additional optimization variables.…”

Section: Artificial Variablesmentioning

confidence: 99%

“…Following the ideas presented in [19] and further extended in [20], auxiliary variables are used as additional optimization variables. Similarly to [36], a set of variables conforming an auxiliary reference trajectory is introduced in the optimization problem as decision variables. Moreover, these variables are required to satisfy the system dynamics, meaning that the artificial trajectory is a feasible trajectory for the system.…”

Section: Artificial Variablesmentioning

confidence: 99%

Nonlinear Model Predictive Path Following Controller with Obstacle Avoidance

Sánchez

D’Jorge

Raffo

et al. 2021

J Intell Robot Syst

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In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in this work simultaneously provides an optimizing solution for both, path-following and obstacle avoidance tasks in a single optimization problem, using Nonlinear Model Predictive Control (NMPC). The main idea consists in extending the existing NMPC controllers by the introduction of an additional auxiliary trajectory that maintains the feasibility of the successive optimization problems even when the reference curve is unfeasible, possibly discontinuous, relaxing assumptions required in previous works. The obstacle avoidance is fulfilled by introducing additional terms in the value functional, rather than imposing state space constraints, with the aim of maintaining the convexity of the state and output spaces. Simulations results considering an autonomous vehicle subject to input and state constraints are carried out to illustrate the performance of the proposed control strategy.

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“…The UAV's linearized translational dynamic was used for the latter to design a backstepping control approach. In [11], they assumed that the components of the thrust vector along the x and y axes were already known. In [12], an offset-free MPC controller was designed by considering three flight modes, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in [30], an LQR solution was used to calculate the terminal set for further stabilizing the MPC controller. Moreover, in [11] a tube-based MPC was designed for a tilt-rotor UAV with two rotors that guarantee the system's stability in the presence of external disturbances. In our proposed system, the rotational dynamic system is linearized around various operational points.…”

Section: Introductionmentioning

confidence: 99%

A constrained robust switching MPC structure for tilt-rotor UAV trajectory tracking problem

Eskandarpour,

Mehrandezh,

Gupta

et al. 2023

Nonlinear Dyn

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In tilt-rotor UAVs, both the fuselage and tilting rotors contribute to the vehicle's rotational motion. Consequently, the system's dynamics rise to a highly-nonlinear system, making it challenging to find feasible and desired control solutions. The common control practices devise a logic-based controller to switch between different flight modes or map the control inputs to the conventional helicopter-type control inputs. However, they fail to provide energy-efficient fast trajectory tracking, especially in the presence of external disturbances. This paper proposes a general-model dynamic formulation and a two-layered constrained Model Predictive Control (MPC) strategy to tackle the trajectory tracking problem for tilt-rotor UAVs. After splitting the vehicle's dynamics into translational and rotational parts, a constrained Linear MPC (LMPC) is designed for the translational dynamic to follow a reference trajectory. We formulate the LMPC as a Quadratically-Constrained Quadratic Problem (QCQP) that leads to a feasible set-point solution for the rotational control layer without violating the physical constraints. Also, an optimizer is designed to generate a thrust vector, which leverages the vehicle's

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Tube-based MPC with Nonlinear Control for Load Transportation using a UAV

Cited by 16 publications

References 14 publications

Model predictive control design for linear parameter varying systems: A survey

Model predictive control design for linear parameter varying systems: A survey

Nonlinear Model Predictive Path Following Controller with Obstacle Avoidance

A constrained robust switching MPC structure for tilt-rotor UAV trajectory tracking problem

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