Modeling, Control and Coordination of Helicopter Systems

Ren, Beibei; Ge, Shuzhi Sam; Chen, Chang; Fua, Cheng-Heng; Lee, Tong Heng

doi:10.1007/978-1-4614-1563-3

Cited by 39 publications

(31 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step in determining trim values for the helicopter, is to define the equations of equilibrium and reference flight conditions. These trim values can be found both analytically, as seen in [154], or numerically, as seen in [160].…”

Section: Trimmentioning

confidence: 99%

Survey of Unmanned Helicopter Model-Based Navigation and Control Techniques

Alvarenga

Vitzilaios

Valavanis

et al. 2014

J Intell Robot Syst

View full text Add to dashboard Cite

Unmanned Aircraft Systems (UAS) have seen unprecedented levels of growth during the last two decades. Although many challenges still exist, one of the main UAS focus research areas is in navigation and control. This paper provides a comprehensive overview of helicopter navigation and control, focusing specifically on small-scale traditional main/tail rotor configuration helicopters. Unique to this paper, is the emphasis placed on navigation/control methods, modeling techniques, loop architectures and structures, and implementations. A 'reference template' is presented and used to provide a basis for comparative studies and determine the capabilities and limitations of algorithms for unmanned/autonomous flight, as well as for navigation, and control. A detailed listing of related research is provided, which includes model structure, helicopter platform, control method and loop architecture, flight maneuvers and results for each. The results of this study was driven by and has led to the development of a 'one-fits-all' comprehensive and modular navigation controller and timing architecture applicable to any rotorcraft platform.

show abstract

Section: Trimmentioning

confidence: 99%

Survey of Unmanned Helicopter Model-Based Navigation and Control Techniques

Alvarenga

Vitzilaios

Valavanis

et al. 2014

J Intell Robot Syst

View full text Add to dashboard Cite

show abstract

“…In reality, many practical systems possess these features, such as biochemical processes [1], [15], flight control systems [16], mechanical systems [17], etc. Recent examples of practical systems falling into this category are dynamic models for a small-scale autonomous helicopter [18].…”

Section: A Problem Statementmentioning

confidence: 99%

Adaptive Neural Control of MIMO Nonlinear Systems With a Block-Triangular Pure-Feedback Control Structure

Chen

Zhang

et al. 2014

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

View full text Add to dashboard Cite

Abstract-This paper presents adaptive neural tracking control for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine purefeedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem (MVT) is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularityfree adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SGUUB). Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this work.Index Terms-Adaptive neural control, backstepping, neural networks (NN), coupling, multi-input-multi-output (MIMO) nonlinear systems.

show abstract

“…The control objective is to design an output feedback control for system (1) such that the output y is stabilized at the origin, and meanwhile all the signals in the closed-loop system remain uniformly ultimately bounded (UUB). Remark 1.…”

Section: Problem Formulationmentioning

confidence: 99%

Output Feedback Stabilization of a Class of Uncertain Nonaffine Nonlinear Systems

Chen¹,

Zhang²

2014

Asian Journal of Control

View full text Add to dashboard Cite

In this paper, output feedback control is presented for a general class of uncertain nonaffine nonlinear systems, that does not rely on state estimation. Under the condition that only the system output is available for feedback, a dynamic linear filter is built to estimate unknown nonlinearities, and an output feedback controller is developed to stabilize the systems by utilizing the estimation to compensate for the unknown nonlinearities. One important feature of the proposed control is that the controller is developed under mild conditions with simple control algorithms, which is of great significance in engineering practice. Simulation results show the effectiveness of the control approach.

show abstract

Modeling, Control and Coordination of Helicopter Systems

Cited by 39 publications

References 0 publications

Survey of Unmanned Helicopter Model-Based Navigation and Control Techniques

Survey of Unmanned Helicopter Model-Based Navigation and Control Techniques

Adaptive Neural Control of MIMO Nonlinear Systems With a Block-Triangular Pure-Feedback Control Structure

Output Feedback Stabilization of a Class of Uncertain Nonaffine Nonlinear Systems

Contact Info

Product

Resources

About