An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay

Carlos, Barbara Barros; Sartor, Tommaso; Zanelli, Andrea; Frison, Gianluca; Burgard, Wolfram; Diehl, Moritz; Oriolo, Giuseppe

doi:10.1109/icarcv50220.2020.9305513

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…It is noted that the controllers have been implemented for a linear model of the batch distillation column. , Due to its own advantages in handling the soft and hard constraints, the use of predictive control and other optimal control strategies have been used in the aerospace industry/batch reactor for trajectory tracking problems. This trend is evident by the increase in the literature available in the use of optimal control in autopilot design for multirotor aircrafts, especially, quadrotor systems. − The use of a quadrotor system for real-time experiments with nonlinear model predictive − and simulations ,, for the trajectory tracking problem of the quadrotor system have been presented.…”

Section: Background Studymentioning

confidence: 99%

“…The nonlinear model predictive control strategy involves the use of an optimization algorithms such as particle swarm optimization, quadratic programming-based optimization, − ,,− improved continuation/generalized minimal residual (iC/GMRES) algorithm, neural networking-based optimization, reinforcement learning based optimization, Laguerre function-based optimization, and so forth. The optimization algorithm in each case is used to minimize the cost function of the predictive algorithm to generate the optimal control input.…”

Section: Background Studymentioning

confidence: 99%

“…This is crucial in the case of the quadrotor trajectory tracking problem. Literature provides different types of predictive control algorithms of both linear ,,,− and non-linear ,,,, nature.…”

Section: Background Studymentioning

confidence: 99%

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Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System

Kumar

Thirunavukkarasu

Itty³

et al. 2022

ACS Omega

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This paper presents the novelty on a nonlinear proportional integral derivative (NPID) controller developed from the gain values obtained using the Lyapunov-based nonlinear model predictive controller (LyNMPC). The tuning parameters of the proposed controller are taken from the dynamics of the nonlinear system, and these parmeters are dynamic with their value varying according to the error in the system. In this article, the authors have considered two highly nonlinear systems, namely, batch polymerization reactor and quadrotor unmanned aerial vehicle systems. The nonlinear mathematical modeling of the batch reactor as well as the quadrotor system considered from the past literature of authors. The acrylamide polymerization reaction under consideration is an exothermic reaction, thereby making the temperature profile tracking and control a challenging task. The primary aim of this article is to develop the NPID controller based on the LyNMPC algorithm and to validate the NPID on a batch reactor bench-scale plant and on an hardware-in-the-loop platform for the quadrotor hardware. A comparative study of trajectory tracking and control capabilities of LyNMPC on derived non-linear models of the batch reactor and quadrotor system is presented. The system mathematical models are obtained with the help of the first-principle energy balance equation for the batch reactor and with the nonlinear dynamics of the quadrotor which is derived based on Newton–Euler formulations. With LyNMPC, the stability of the nonlinear systems can be improved because the error sensitivity is considered in the cost function.

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Section: Background Studymentioning

confidence: 99%

Section: Background Studymentioning

confidence: 99%

See 1 more Smart Citation

Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System

Kumar

Thirunavukkarasu

Itty³

et al. 2022

ACS Omega

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“…When using NMPC to control a system, a nonlinear nonconvex program is solved using the current state as the initial value at each sampling instant. However, as the computational burden associated with the solution can be rather long, the employment of NMPC has only recently been extended to applications where shorter sampling times are required [16]. Typically, a continuous-time, infinite-dimensional optimal control problem (OCP) is tailored according to the problem at hand, discretized into N intervals using a time step ∆t over a fixed time horizon t N , and then solved.…”

Section: B Mixed-initiative Controllermentioning

confidence: 99%

“…with state ξ = (p, γ, v b , ω, Ω) ∈ R 16 . The quadrotor's mass is denoted by m, vector G = (0, 0, g) ∈ R 3 with g being the gravitational acceleration; d represents the drag coefficient of the rotor whose scalar inertia is J m .…”

Section: Predicted Healthiness Indexmentioning

confidence: 99%

Towards Safe Human-Quadrotor Interaction: Mixed-Initiative Control via Real-Time NMPC

Carlos

Franchi

Oriolo

2021

IEEE Robot. Autom. Lett.

Self Cite

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This paper presents a novel algorithm for blending human inputs and automatic controller commands, guaranteeing safety in mixed-initiative interactions between humans and quadrotors. The algorithm is based on nonlinear model predictive control (NMPC) and involves using the state solution to assess whether safety-and/or task-related rules are met to mix control authority. The mixing is attained through the convex combination of human and actual robot costs and is driven by a continuous function that measures the rules' violation. To achieve real-time feasibility, we rely on an efficient real-time iteration (RTI) variant of a sequential quadratic programming (SQP) scheme to cast the mixed-initiative controller. We demonstrate the effectiveness of our algorithm through numerical simulations, where a second autonomous algorithm is used to emulate the behavior of pilots with different skill levels. Simulations show that our scheme provides suitable assistance to pilots, especially novices, in a workspace with obstacles while underpinning computational efficiency.

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Aggressive Trajectory Tracking for Nano Quadrotors Using Embedded Nonlinear Model Predictive Control

Kazim,

Sim,

Shin

et al. 2024

Lecture Notes in Networks and Systems

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An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay

Cited by 18 publications

References 20 publications

Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System

Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System

Towards Safe Human-Quadrotor Interaction: Mixed-Initiative Control via Real-Time NMPC

Aggressive Trajectory Tracking for Nano Quadrotors Using Embedded Nonlinear Model Predictive Control

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