Adaptive control strategy‐based reference model for spacecraft motion trajectory

Abstract

SummaryIn aerospace field, the economic realization of a spacecraft is one of the main objectives that should be accomplished by conceiving the optimal propulsion system and the best control algorithms. This paper focuses on the development of a viable adaptive control approach (ACA) for spacecraft motion trajectory (SMT). The proposed strategy involves the nonlinear mathematical model of SMT expressed in the central field, which is linearized by the Taylor expansion, and the second Lyapunov method to offer a high rate and unfailing performance in the functioning. The adaptive control system is composed of the cascade of adaptation loop and feedback control loop. When the spacecraft deviates from its reference trajectory model, the ACA acts on the control system to correct this deviation and follow the optimal reference trajectory. Therefore, when the states of the adjustable model are different from its reference values, then the error signal is provided as an input to the adaptation law, which contains the adaptation algorithm. The output will be the state variable feedback control matrix, which will be used to calculate the new control law vector. The efficiencies of the linearization procedure and the control approach are theoretically investigated through some realistic simulations and tests under MATLAB. The steady‐state errors of control between the reference model and the adjustable model of SMT converge to zero. Copyright © 2014 John Wiley & Sons, Ltd.

Robust model reference control for uncertain second-order system subject to parameter uncertainties

“…On the other hand, robust model reference control is an important control strategy, which is applied to different types of systems, such as multivariable linear systems subject to model uncertainties (De La Torre et al, 2016; Duan et al, 2001), descriptor linear systems subject to parametric uncertainties (Duan and Zhang, 2007), linear parameter-varying systems (Abdullah, 2018; Rabaoui et al, 2018), discrete-time anti-linear systems (Wu et al, 2001), Markovian jump linear systems (Boukas, 2009), networked control systems (Gao and Chen, 2008; Sakthivel et al, 2017) and chaotic systems (Liu et al, 2017). It is notable that most results in the existing literature are limited to frist-order system (Duan et al, 2001; Samigulina et al, 2015; Song and Tao, 2020; Zhong and Lin, 2017). However, in some practical situation, second-order systems capture the dynamic behaviours of many natural phenomena.…”