Flight Control Reconfiguration Based on Online Physical Model Identification and Nonlinear Dynamic Inversion

Abstract

There are many control approaches possible in order to achieve fault tolerant flight control. An important aspect of these algorithms is that they should not only be robust, but even adaptive in some way, in order to adapt to the faulty situation, see Ref. [1] and [5] in the literature. In the category of adaptive control algorithms, a distinction is made between indirect adaptive control and direct adaptive control. Indirect adaptive control involves two stages. First, an estimate of the plant model is generated online. Once the model is available, it is used to generate controller parameters. Instead of estimating a plant model, a direct adaptive control algorithm estimates the controller parameters directly in the controller. This can be done via two main approaches: output error and input error. Of both main categories mentioned here, indirect adaptive control is preferable due to its flexibility and its property of being model based. In both categories, there are also two subversions, namely model reference adaptive control (MRAC) and self-tuning control (STC). In the former, one relies on a reference model and works on minimizing the tracking error between plant output and reference output (such as the concept of sliding mode control). With model reference indirect adaptive control it is feasible to achieve three important goals, namely trim value adjustment for the inputs, decoupling of inputs and outputs and closed loop tracking of pilot commands, see Ref. [1]. Self-tuning control focuses on adapting the (PID) control gains of the controller by making use of the estimated parameter values and is known to be more flexible, see Ref. [21]. Currently, much research is performed in the field of indirect adaptive control, where the adaptation is more extensive than only tuning the PID control gains. One of these new indirect control possibilities is adaptive model predictive control (AMPC), which is an interesting algorithm thanks to its nature to deal with (input) inequality constraints. These constraints are a good representation for actuator faults. It should be noted that there have been already some successful applications of MPC in the field of fault tolerant flight control, see Ref. [10] and [14]. An alternative indirect adaptive nonlinear control approach is discussed in this chapter, which allows to develop a reconfigurable control routine placing emphasis on the use of physical models, and thus producing internal parameters which are physically interpretable at any time.