Input-output tracking control of a 2-DOF laboratory helicopter with improved algebraic differential estimation

Xin, Ying; Qin, Zhi‐Chang; Sun, Jian‐Qiao

doi:10.1016/j.ymssp.2018.07.027

Cited by 27 publications

(17 citation statements)

References 28 publications

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“…Where & : generalized coordinates related to and , respectively : Lagrangian function -the difference between the total kinetic and potential energies of the system Based on the Euler-Lagrange formulation, the non-linear dynamic equation that describes the pitch and yaw attitude motions relative to the motor are given as [1]:…”

Section: -Dof Helicopter System Modelingmentioning

confidence: 99%

“…Besides, it is also utilized in a wide range of applications with particular reference to the military and civilian sectors. However, it is known to be multi-variable, highly non-linear, and strongly coupled system [1]. It also faces several impediments during tracking specific paths such as instability, moving and fixed obstacles, motors failure, external disturbances, and model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Practical Real-Time Implementation of a Disturbance Rejection Control Scheme for a Twin-Rotor Helicopter System Using Intelligent Active Force Control

2021

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Section: -Dof Helicopter System Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical Real-Time Implementation of a Disturbance Rejection Control Scheme for a Twin-Rotor Helicopter System Using Intelligent Active Force Control

2021

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“…By neglecting the unmodeled dynamics, system uncertainties, and external disturbances in Equation 2, the 2-DOF helicopter system can be described accurately with the system parameters listed in Table 1. The linear quadratic regulator (LQR) control [51] and an optimal feedback linearization control (OFLC) [7] are taken as two comparison baselines to evaluate the control performances of the two model-free control methods above. It's worth noting that the two modelbased control methods are based on the state equation 2that ignores the unmodeled dynamics, system uncertainties, and external disturbances.…”

Section: A Model-based Control For Comparisonmentioning

confidence: 99%

“…Neglecting the unmodeled dynamics and system uncertainties in Equation 2 and extracting the last two rows of this equation, we havë y = F(y,ẏ) + G(y)u, ]. Referring to the results in [7], the optimal feedback linearization controller (OFLC) is…”

Section: ) Lqr Designmentioning

confidence: 99%

“…Generally, the model-based control method requires the designer to establish a mathematical model that can reflect the laws of helicopter motion, often in the form of differential equations or difference equations. There are many control methods that fall into this category, such as LQR [1]- [4], feedback linearization control [5]- [7], backstepping control [8]- [10], sliding mode control [11]- [13], model predictive control [14]- [16] and so on. When the helicopter works near the hovering point, for the sake of simplicity, a linear model can be built for controller design.…”

Section: Introductionmentioning

confidence: 99%

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Dual-Loop Robust Attitude Control for an Aerodynamic System With Unknown Dynamic Model: Algorithm and Experimental Validation

2020

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This paper proposes two kinds of dual-loop nonlinear robust control strategies that are implemented on an open-loop unstable two-degree-of-freedom (2-DOF) helicopter system with unmodeled dynamics and uncertainties. The inner feedback loop is considered as a nominal controller realized by an existing ''intelligent'' proportional differential controller (iPD) while the outer layer feedback control is regarded as a compensation loop. We study two different forms of outer loop in this paper. One is modelfree sliding mode compensator (MFSMC) and another is model-free data-driven compensator (MFDDC). The combination of the shared inner loop and either of the outer loops forms two different kinds of model-free robust control strategies, i.e., iPD-MFSMC and iPD-MFDDC. Both robust control approaches are validated experimentally on the attitude tracking control of a 2-DOF laboratory helicopter, whose control objective is to have the helicopter attitudes, i.e., pitch and yaw motions, track specified trajectories. To demonstrate the utility of the two control approaches, we compare them with linear quadratic regulator (LQR), optimal feedback linearization control (OFLC) and iPD, respectively. The extensive comparison of the simulation and experimental results shows that the dual-loop robust control approaches are quite promising in controlling the systems with unknown dynamical models. INDEX TERMS Dual-loop nonlinear robust control, sliding mode compensator, data-driven compensator, unmodeled dynamics and uncertainties, small helicopter.

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Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method

Qin

Xin

Sun

2019

Asian Journal of Control

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This paper presents a study of multi‐objective optimal design of nonlinear control systems and has validated the control design with a twin rotor model helicopter. The gains of the porportional integral differential (PID) control are designed in the framework of multi‐objective opitmization. Eight design paramaters are optimized to minimize six time‐domain objective objective functions. The study of multi‐objective optimal design of feedback control with such a number of design paramaters and objective functions is rare in the literature. The Pareto optimal solutions are obtained by the proposed parallel simple cell mapping method consisting of a robust Pareto set recovery algorithm and a rolling subdivision technique. The proposed parallel simple cell mapping algorithm has two features: the number of cells in the invariant set grows linearly with the rolling subdivisions, and the Pareto set is insensitive to the inital set of seed cells. The current control design is compared with the classical LQE control for linear systems, and is also experimentally validated. The current design provides improved control performance as compares with the LQR control, and is applicable to complex nonlinear systems.

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Input-output tracking control of a 2-DOF laboratory helicopter with improved algebraic differential estimation

Cited by 27 publications

References 28 publications

Practical Real-Time Implementation of a Disturbance Rejection Control Scheme for a Twin-Rotor Helicopter System Using Intelligent Active Force Control

Practical Real-Time Implementation of a Disturbance Rejection Control Scheme for a Twin-Rotor Helicopter System Using Intelligent Active Force Control

Dual-Loop Robust Attitude Control for an Aerodynamic System With Unknown Dynamic Model: Algorithm and Experimental Validation

Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method

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