Implementation of CAPIO for Composite Adaptive Control of Cross-Coupled Unstable Aircraft

Abstract

This paper presents an implementation of a recently developed control allocation algorithm CAPIO (a Control Allocation technique to recover from Pilot Induced Oscillations) for composite adaptive control of an inertially cross coupled unstable aircraft. When actuators are rate-saturated due to either an aggressive pilot command, high gain of the flight control system or some anomaly in the system, the effective delay in the control loop may increase due to the phase shifting between the desired and the achieved system states. This effective time delay may deteriorate the performance or even destabilize the system in some cases, depending on the severity of rate saturation. CAPIO reduces the effective time delay by minimizing the phase shift between the commanded and the actual attitude accelerations. We present simulation results for an unstable aircraft with cross-coupling controlled with a composite adaptive controller in the presence of rate saturation. The simulations demonstrate the potential of CAPIO serving as an effective rate saturation compensator in adverse conditions.

Time‐varying sliding mode controller for over‐actuated systems with constrained and uncertain actuators in flight control applications

“…Actuator limits can induce nonlinear behavior and lead to performance degradation, limit cycles, multiple equilibria, and even instability. 6,10,32,42 Several control allocation methods have been proposed in the literature that can handle actuator saturation. These include direct control allocation, 24 daisy chaining, 43 pseudo-inverse-based control allocation, 44 and iterative approaches that use the null space of the control input matrix.…”

“…27 Optimization-based control allocation is another commonly used method of accounting for actuator magnitude and rate constraints. 1,6,13,28,29,31,45,46 Furthermore, a control allocation approach by Naderi et al, 47 employs model predictive control to handle actuator magnitude constraints. In order to allocate control signals in the presence of uncertainty as well as actuator constraints, an adaptive control allocator for constrained systems has been developed by Tohidi et al 38,40 An adaptive control allocator which exploits a modified projection algorithm to handle magnitude and rate constraints in over-actuated systems is proposed by Tohidi et al 48 Although control allocation methods enable modularity for the overall control system design, as they separate the generation of the control signal and its distribution, control allocation errors can be significant in transients and degrade the performance.…”

Adaptive control allocation for constrained systems

“…Control allocation is the process of distributing control signals among redundant actuators which is employed in many engineering domains such as aerial vehicles (Acosta et al, 2014;Bodson, 2002;Ducard, 2009;Liao, Lum, Wang, & Benosman, 2010;Sadeghzadeh, Chamseddine, Zhang, & Theilliol, 2012;Shen, Wang, Zhu, & Poh, 2015Tohidi, Yildiz, & Kolmanovsky, 2018;Yildiz & Kolmanovsky, 2011a, 2011b, marine vehicles (Chen, Ge, How, & Choo, 2013;Gierusz & Tomera, 2006;Johansen, Fuglseth, Tøndel, & Fossen, 2008;Podder & Sarkar, 2001;Sørensen, 2011), automobiles (Demirci & Gokasan, 2013;Tjønnås & Johansen, 2010), robots (Taghirad & Bedoustani, 2011), and power systems (Bouarfa, Bodson, & Fadel, 2017;Raoufat, Tomsovic, & Djouadi, 2017). In particular, in aircraft and spacecraft applications, the flight control law determines forces and moments while control actuator settings are determined through control allocation.…”

Pilot Induced Oscillation Mitigation for Unmanned Aircraft Systems: An Adaptive Control Allocation Approach

“…The main idea behind CAPIO is that unlike conventional control allocation methods, instead of minimizing the error between the desired and achieved commanded signals, the derivative of the error is minimized, which eliminates the introduction of the phase shift. The stability of a closed loop control system utilizing CAPIO is rigorously analyzed in [2], and [19] and successful human-in-the-loop experimental results with real pilots are reported in [20]. In this paper, we develop an adaptive version of CAPIO, termed "Adaptive CAPIO", inspired by our recent work on adaptive control allocation, reported in [21] and [22], and apply it to UAS recovery from PIOs.…”