Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples

Barrier Lyapunov function-based adaptive fuzzy attitude tracking control for rigid satellite with input delay and output constraint

This paper investigates the adaptive attitude tracking problem for the rigid satellite involving output constraint, input saturation, input time delay, and external disturbance by integrating barrier Lyapunov function (BLF) and prescribed performance control (PPC). In contrast to the existing approaches, the input delay is addressed by Pade approximation, and the actual control input concerning saturation is obtained by utilizing an auxiliary variable that simplifies the controller design with respect to mean value methods or Nussbaum function-based strategies. Due to the implementation of the BLF control, together with an interval notion-based PPC strategy, not only the system output but also the transformed error produced by PPC are constrained. An adaptive fuzzy controller is then constructed and the predesigned constraints for system output and the transformed error will not be violated. In addition, a smooth switch term is imported into the controller such that the finite time convergence for all error variables is guaranteed for a certain case while the singularity problem is avoided. Finally, simulations are provided to show the effectiveness and potential of the proposed new design techniques

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Structural dynamics branch research and accomplishments to FY 1992

This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications

Stabilization of Uncertain Systems using Backstepping and Lyapunov Redesign

This article presents stabilization method for uncertain system using backstepping technique and Lyapunov redesign. The design begins by obtaining stabilizing function for unperturbed system using control Lyapunov function. As such, the stabilizing function guarantees asymptotic stability in the sense of Lyapunov. The control Lyapunov function is re-used in the re-design phase whereby the nonlinear robust function is then augmented with pre-designed stabilizing function for robustness toward uncertainties. Lyapunov redesign is used in designing overall robust stabilizing function which guarantees asymptotic stability toward uncertainties and also toward any perturbed states within asymptotic stability region

Nonlinear Robust Neural Control with Applications to Aerospace Vehicles

Nonlinear control has become increasingly more used over the last few decades, mainly due to the research and development of better analysis tools, that can simulate real-world problems, which are almost always, nonlinear. Nonlinear controllers have the advantage of being more accurate and efficient when dealing with complex scenarios, such as orbit control, satellite rendezvous, or attitude control, compared to linear ones. However, common nonlinear control techniques require having a high-fidelity model, which is often not the case, thereby limiting their use. Additionally, rapid advancements in the field of machine learning have raised the possibility of using tools like neural networks to learn the dynamics of nonlinear systems in an effort to compute control inputs without the need to solve the highly complex mathematical equations that some nonlinear controllers require to solve, in real-time, therefore bypassing the need of higher computational power, which can reduce costs and weight, in space missions. This dissertation will focus on the development of a neural controller based on H8 pseudolinear control, to be applied to the satellite attitude control problem, as well as the satellite orbit control problem. The resulting controller is proven to be robust when dealing with important disturbances that are relevant in space missions, due to being trained using H8 controller data. Moreover, since the original controller is pseudolinear, the neural controller can capture the nonlinearities that exist in the equations of motion as well as in the attitude dynamics equations.Nas últimas décadas, o controlo não-linear tem sido cada vez mais utilizado, maioritariamente devido ao desenvolvimento de melhores ferramentas de análise, utilizadas para a simulação problemas reais, que tendem a ser não-lineares. Os controladores não-lineares têm a vantagem de serem mais precisos e eficientes quando utilizados em situações complexas, como controlo orbital, rendezvous de satélites, e controlo de atitude, comparados com controladores lineares. No entanto, as técnicas comuns de controlo não-linear requerem o uso de modelos com alto grau de fidelidade, o que muitas vezes não é o caso, limitando assim a sua utilização. Além disso, os rápidos avanços no campo de machine learning levantaram a possibilidade de utilizar ferramentas como redes neuronais para aprender a dinâmica de sistemas não lineares, numa tentativa de poder computar as entradas de controlo sem a necessidade de resolver as equações matemáticas altamente complexas que alguns controladores não lineares necessitam que sejam resolvidas, em tempo real, contornando assim a necessidade de maior potência computacional, que pode reduzir custos e peso, em missões espaciais. Esta dissertação focar-se-á no desenvolvimento de um controlador neuronal, baseado em controlo pseudolinear por H8, com o intuito de ser aplicado no problema de controlo orbital, bem como no problema de controlo de atitude. O controlador resultante provou ser robusto ao lidar com perturbações importantes, relevantes em missões espaciais, devido ao facto de ter sido treinado usando dados do controlador H8. Além disso, como o controlador original é pseudolinear, o controlador neuronal pode captar as dinâmicas não lineares que existem nas equações de movimento, bem como nas equações da dinâmica de atitude

Design of sliding mode controller based on radial basis function neural network for spacecraft autonomous proximity

Since the dynamic model of spacecraft has the characteristics of non-linear, kinematic couplings, uncertainties and nonstationary disturbance, it has become a challenging problem to accurately control the relative position and attitude of the spacecraft. A radial basis function neural network (RBFNN)-based sliding mode controller (SMC) is proposed for trajectory tracking of spacecraft autonomous proximity in this paper. Firstly, a six degree-of-freedom (DOF) relative motion dynamics model is developed for close proximity operations. The modified Rodrigues parameters are applied to solve the problem of singularity. Then, a SMC that does not require accurate model information is designed. RBFNN is used to adaptively eliminated the model uncertainty impacts on the system. Finally, the stability of the relative motion dynamics is proved via Lyapunov stability theory. Simulation results illustrate that the method can attenuate the attitude and position errors, reduce the chattering of the input and decrease the overshoot of the control torque effectively

Fault Diagnosis and Fault-Tolerant Control of Unmanned Aerial Vehicles

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults