Robust Multivariable Flight Control

Adams, Richard J.; Buffington, James M.; Sparks, Andrew G.; Banda, Siva S.

doi:10.1007/978-1-4471-2111-4

Cited by 161 publications

(58 citation statements)

References 17 publications

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“…The daisy chaining method (Adams, Buffington, Sparks & Banda 1994, Buffington & Enns 1996, Oppenheimer et al 2010) offers a very simple alternative, but is often less effective than the above mentioned methods. This method groups the effectors into two or more groups that are ranked such that first the control allocation problem is solved for the highest prioritized group.…”

Section: Redistributed Pseudo-inverse and Daisy Chainingmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Redistributed Pseudo-inverse and Daisy Chainingmentioning

confidence: 99%

Control allocation—A survey

2013

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“…where I -integration coefficient of the PID regulator and meaning of other parameters is same like in equation (4). The structure of the autopilot is depicted in Fig.…”

Section: Nd Algorithmmentioning

confidence: 99%

The Most Suitable Algorithm for Inverted Pendulum Control

Pástor¹

2011

Acta Electrotechnica Et Informatica

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“…[1], [2], [3]). The proposed design methods include both classical techniques based for instance on the wellknown PID (Proportional Integral Derivative) control laws, optimal approaches based on the linear quadratic problem, the H ∞ norm minimization and nonlinear methods including nonlinear inversion, etc ([4], [5], [6], [7], [8]). The methods mentioned above have advantages and drawbacks; the main difficulty in choosing the control design method is to ensure a trade-off between the complexity of the automatic flight control system and its performances.…”

Section: Introductionmentioning

confidence: 99%