Application of Online Iterative Learning Tracking Control for Quadrotor UAVs

Pipatpaibul, Pong-in; Ouyang, P. R.

doi:10.5402/2013/476153

Cited by 23 publications

(26 citation statements)

References 29 publications

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“…The main result is summarized into the following theorem. (2), (3) and Assumptions 1 to 3, with the control law proposed as (24), (26) and ILC laws (27)-(32) has the following properties:…”

Section: Resultsmentioning

confidence: 99%

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Jin

2019

Intl J Robust & Nonlinear

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Summary In this paper, we present a novel parametric iterative learning control (ILC) algorithm to deal with trajectory tracking problems for a class of nonlinear autonomous agents that are subject to actuator faults. Unlike most of the ILC literature, the desired trajectories in this work can be iteration dependent, and the initial position of the agent in each iteration can be random. Both parametric and nonparametric system unknowns and uncertainties, in particular the control input gain functions that are not fully known, are considered. A new type of universal barrier functions is proposed to guarantee the satisfaction of asymmetric constraint requirements, feasibility of the controller, and prescribed tracking performance. We show that under the proposed algorithm, the distance and angle tracking errors can uniformly converge to an arbitrarily small positive number and zero, respectively, over the iteration domain, beyond a small user‐prescribed initial time interval in each iteration. A numerical simulation is presented in the end to demonstrate the efficacy of the proposed algorithm.

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“…The main result is summarized into the following theorem. (2), (3) and Assumptions 1 to 3, with the control law proposed as (24), (26) and ILC laws (27)-(32) has the following properties:…”

Section: Resultsmentioning

confidence: 99%

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Jin

2019

Intl J Robust & Nonlinear

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“…Moreover this approach is mainly devoted to periodic systems. However, there are some recent works dedicated to robust predictive ILC [20] and allowing on-line implementation [21].…”

Section: Iterative State Trackingmentioning

confidence: 99%

“…The matching conditions for the reference model and the system are then obtained by comparing the closed-loop system (21) and the reference model (19). The perfect transient tracking conditions are given by:…”

Section: Remark 1 For the Sake Of Simplicity The Model Reference Ismentioning

confidence: 99%

State constrained tracking control for nonlinear systems

Bezzaoucha

Marx

Maquin

et al. 2015

Journal of the Franklin Institute

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“…Subsequently, the reverse-time tracking equation is solved: (8) discretized as (9) with the boundary condition .…”

Section: A Lq Tracking Of the Optimal Path Found In Rpomentioning

confidence: 99%

“…It is extensively applied for industrial robots [5], agricultural robots [6], quadrotor unmanned aerial vehicles (UAV's) [7][8], and several other areas. The objective of optimal control is to enable an actuating strategy to achieve best performance typically in the sense of minimum time of mission, minimum energy required, minimum fuel used, or minimum departure from a desired prescribed trajectory.…”

Section: Introductionmentioning

confidence: 99%

Optimal tracking of a mobile robot with path determined by Random Particle Optimization and genetic algorithms

Khan

Koreshi

2014

2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE)

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This work considers optimal tracking of difficult paths for mobile robots in 2D space in the presence of obstacles. For determination of optimal trajectory, heuristic methods such as Genetic Algorithms and Random Particle Optimization (RPO) method are used due to their computational efficiency for NPhard problems. The optimal path thus found is fed as a reference trajectory to compute the optimal control required to enable the robot to follow the path. This paper uses the Linear Quadratic formulation with a variational approach for the robot represented by Newton's second law of motion. The adequacy of a linear model and the effect of the sampling time on numerical convergence is examined to determine its accuracy i.e in maintaining low off-track error.

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Application of Online Iterative Learning Tracking Control for Quadrotor UAVs

Cited by 23 publications

References 29 publications

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

Nonrepetitive trajectory tracking for nonlinear autonomous agents with asymmetric output constraints using parametric iterative learning control

State constrained tracking control for nonlinear systems

Optimal tracking of a mobile robot with path determined by Random Particle Optimization and genetic algorithms

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