Interior-Point Algorithms for Control Allocation

Petersen, John A. M.; Bodson, Marc

doi:10.2514/6.2003-5566

Cited by 16 publications

(28 citation statements)

References 10 publications

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“…(Härkegård 2002, Petersen & Bodson 2006, Petersen & Bodson 2005. As an example for such a formulation, consider the control allocation formulation…”

Section: Error Minimization Using Quadratic Programmingmentioning

confidence: 99%

“…QPs are usually solved using active set methods or interiorpoint methods, (Nocedal & Wright 1999), and both have been studied in the context of control allocation (Härkegård 2002, Petersen & Bodson 2005). An iterative fixed-point method has also been proposed and shown to be efficient, (Burken, Lu, Wu & Bahm 2001).…”

Section: Error Minimization Using Quadratic Programmingmentioning

confidence: 99%

“…The active set methods tend to perform well in control allocation problems, (Härkegård 2002), while interior point methods have their advantage for larger-scale problems (Petersen & Bodson 2005). Active set methods have the advantage that their initialization can take advantage of the solution from the previous sample (known as warm start), which is often a good guess for the optimal solution at the current sample.…”

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confidence: 99%

“…For non-real-time implementations, the Matlab toolbox QCAT ) is dedicated to QP-based control allocation and implements the methods of (Härkegård 2002, Petersen & Bodson 2005. For realtime implementation on embedded systems, there exists a few portable C code solvers such as the FORTRAN-to-Cconverted active set solver QLD (Schittkowski 1986), the interior-point method automatic code generation tool CVX-GEN (Mattingley & Boyd 2010), the active-set-like solver QPOASES (Potschka, Kirches, Bock & Schlöder 2010), and the conjugate gradient method, (Press et al 1992).…”

mentioning

confidence: 99%

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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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“…(Härkegård 2002, Petersen & Bodson 2006, Petersen & Bodson 2005. As an example for such a formulation, consider the control allocation formulation…”

Section: Error Minimization Using Quadratic Programmingmentioning

confidence: 99%

Section: Error Minimization Using Quadratic Programmingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Control allocation—A survey

2013

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“…We say a vehicle is over-actuated if it has more control effectors than control variables. The control allocation of over-actuated vehicles has been formulated as a constrained optimization problem by many researchers [2][3][4][5][6][7][8][9][10][11][12] . A real-time solution to the control allocation problem is desirable to enable the system to run on NextGen aircraft during flight.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Optimal Control Allocation for Gust Load Alleviation in Flight Control

Frost

Taylor

Bodson

2012

AIAA Atmospheric Flight Mechanics Conference

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Advances in sensors and avionics computation power suggest real-time structural load measurements could be used in flight control systems for improved safety and performance. A conventional transport flight control system determines the moments necessary to meet the pilot's command, while rejecting disturbances and maintaining stability of the aircraft. Control allocation is the problem of converting these desired moments into control effector commands. In this paper, a framework is proposed to incorporate real-time structural load feedback and structural load constraints in the control allocator. Constrained optimal control allocation can be used to achieve desired moments without exceeding specified limits on monitored load points. Minimization of structural loads by the control allocator is used to alleviate gust loads. The framework to incorporate structural loads in the flight control system and an optimal control allocation algorithm will be described and then demonstrated on a nonlinear simulation of a generic transport aircraft with flight dynamics and static structural loads.

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Interior‐Point Method to Optimize Tire Force Allocation in 4‐Wheeled Vehicles Using High‐Level Sliding Mode Control with Adaptive Gain

Tavasoli¹,

Naraghi²

2012

Asian Journal of Control

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Nonlinear vehicle control allocation is achieved by distributing the control task to tire forces with nonlinear saturation constraints. The overall vehicle control is accomplished by developing a hierarchical scheme. First, a high-level sliding mode control with adaptive gain is considered to obtain the body force/moment for stable vehicle motion. The proposed controller only requires online adaptation of control gains without acquiring the knowledge of upper-bounds on system uncertainties. Then, optimal distribution of tire forces (ODF) with nonlinear saturation constraints is considered. The high-level control objectives are mapped to individual tire forces by formulating a nonlinear optimization problem. The interior-point (IP) method is adopted for a nonlinear programming task at each time step. Evaluation of the overall system is accomplished by simulation testing with a nine-degrees-of-freedom vehicle nonlinear model. Comparison with a well-recognized control system shows the effect of saturation constrained ODF (SCODF) on improving vehicle handling and stability.

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Interior-Point Algorithms for Control Allocation

Cited by 16 publications

References 10 publications

Control allocation—A survey

Control allocation—A survey

Investigation of Optimal Control Allocation for Gust Load Alleviation in Flight Control

Interior‐Point Method to Optimize Tire Force Allocation in 4‐Wheeled Vehicles Using High‐Level Sliding Mode Control with Adaptive Gain

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