Fault‐tolerant control allocation for Mars entry vehicle using adaptive control

Abstract

96M. MARWAHA AND J. VALASEK the concepts of feedback linearization, dynamic inversion, and structured model reference adaptive control. Because dynamic inversion assumes perfect knowledge of all the system parameters which are used to solve for the control, but is approximate in the actual implementation due to inaccurate modeling of system parameters, in SAMI an adaptive controller is wrapped around the dynamic inversion to handle the uncertainties in system parameters. Specific to the Mars entry problem, Restrepo and Valasek in [5] conducted the modeling and SAMI adaptive control formulation for a potential planetary entry vehicle that has 18 discrete reaction control system (RCS) jets.Another challenge during operation is recovery or tolerance in the case of actuator faults. Actuator faults can be due to any malfunction in the physical system or subsystem of the controller which results in its failure to perform as designed. The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required, with most application studies based upon aerospace systems [6]. Patton provides a thorough review of fault-tolerant control systems in [6]. When combined with adaptive control, control allocation algorithms provide the capability to recover from actuator faults. In their basic form control allocation algorithms are useful for finding solutions to meet the desired control objectives by delivering the desired moments [7]. Bodson provides a comprehensive survey of constrained, numerical-based optimization methods for control allocation in [8], and Page and Steinberg in [9] compare the closed and open-loop performance for 16 different control allocation approaches. Numerous linear control allocation algorithms are currently available. In [10] a faulttolerant control allocation scheme is introduced to handle actuator failures in discrete systems and is validated with a numerical example of a disturbance-free case. Tjonnas and Johansen [11] considers a dynamic approach by constructing actuator reference adaptive laws for over-actuated nonlinear time-varying systems. Shertzer et al. [12] discusses control allocation algorithms specifically in the context of next generation entry vehicles. Recently, Bolender and Doman have used a concept in which control allocation is used for aerodynamic surfaces, and dynamic inversion-based adaptive control is used for system identification, which helps in identifying any failure [13]. The control variable rates or moments are nonlinear functions of control positions. These schemes are applied to different aerovehicles and are shown to have good performance for continuous controls. However, this approach has not been applied to discrete controls, since they require a different allocation algorithm from aerodynamic surfaces [14].This paper introduces a novel use of fault-tolerant control allocation for discrete controls to nonlinear, time-varying systems. Specifically, we address the problem of discrete c...

Actuator Fault Estimation and Reconfiguration Control for the Quad-Rotor Helicopter

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A Fault Tolerant Control Scheme Using the Feasible Constrained Control Allocation Strategy

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Fixed-time rendezvous control of spacecraft with a tumbling target under loss of actuator effectiveness

“…In contrast to active FTC approach, neither an FDD scheme nor a controller reconfiguration mechanism is needed in the passive approach. The passive method utilizes a single robust controller to deal with a certain well defined fault sets, and there also have been a great deal of results in the literature on spacecraft control, such as the work of Qian et al [36] about the spacecraft rendezvous system, the work reported in Marwaha et al [37] about the Mars entry vehicle and the work of Hu et al [38][39] about the spacecraft attitude control. Both of active FTC and passive FTC have their respective advantages and limitations Even though the control objective of their methods are the same, each approach have its own unique ways to achieve the objective.…”