Xiaohong Liao

Indirect Robust Adaptive Fault -Tolerant Control for Attitude Tracking of Spacecraft

Reliable and cost-effective control of spacecraft should account for modeling uncertainties, unexpected disturbances, subsystem failures, and limited resources simultaneously. This paper presents an indirect (nonregressor-based) approach to attitude tracking control of spacecraft. It is shown that the control algorithms developed are not only robust against external disturbances and adaptive to unknown and time-varying mass/inertia properties, but also able to accommodate actuator failures under limited thrusts. All are achieved with inexpensive online computations (a feature of practical importance in reducing the usage of onboard resources in terms of computing power and memory size). Furthermore, this method is user/designer friendly in that it does not involve a time-consuming design procedure and demands little redesigning or reprogramming during vehicle operation. The benefits of the proposed control method are analytically authenticated and also validated via simulation study.

Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings

This paper addresses the problem of wing motion control of flapping wing Micro Air Vehicles (MAVs). Inspired by hummingbirds wing structure as well as the construction of its skeletal and muscular components, a dynamic model for flapping wing is developed. As the model is highly nonlinear and coupled with unmeasurable disturbances and uncertainties, traditional strategies are not applicable for flapping wing motion control. A new approach called neural-memory based control is proposed in this work. It is shown that this method is able to learn from past control experience and current/past system behavior to improve its performance during system operation. Furthermore, much less information about the system dynamics is needed in construction such a control scheme as compared with traditional NN based methods. Both theoretical analysis and computer simulation verify its effectiveness.

Adaptive Path Control of Unmanned Ground Vehicle

As part of the effort in developing cooperative control schemes for multiple unmanned ground vehicles (UGVs), this work explores automatically steering a three-wheel mobile vehicle via highly adaptive approach. By introducing a virtual current vector, a set of control algorithms for direct controlling the left and right driving motors are derived. As confirmed by both theoretical analysis and computer simulation, the developed algorithms are effective in maintaining good path tracking precision in the presence of uncertain dynamics due to various disturbances emanating from external loads, the wheel-ground contact, and other unmodeled nonlinearities

A new approach to eliminating variable structure control chattering with application to flight vehicles

Because of its effectiveness in dealing with modeling uncertainties, variable structure control (VSC) has been widely used for robust control design. However, current VSC design method leads to chattering due to the discontinuous control action involved. How to eliminate the undesirable chattering in VSC has been an interesting yet challenging topic of research. Most existing methods for chattering elimination are achieved at the price of control precision. In this paper, a new approach is introduced to remove chattering without sacrificing control precise. The idea behind the proposed approach is extremely simple: instead of designing the control input directly, the control (compensating) rate is designed. Once the rate of the compensating unit is determined, the control input is readily obtained via integration. It is shown that such a treatment leads to bounded and smooth control action while at the same time maintains asymptotical system stability. Simulations on heading speed control of flight vehicle verify the effectiveness of the method.

Smooth Variable Structure Attitude Control of Crew Exploration Vehicles Driven by Nonlinear Actuators

*† ‡ § & Most existing attitude control methods are based on linear actuators. This work investigates the attitude control problem of CEV driven by nonlinear actuators. Nonlinear function inverse approach is often used to address such problem, which demands intensive calculation at each control step, and in general it is nontrivial to find such inverse analytically. In this work we investigate a new method that does not involve nonlinear function inverse. The fundamental idea behind this method is to directly design the control rate, instead of the control action itself, using VSC. This approach avoids using inverse of nonlinear function, thus reducing the computation burden. Furthermore, the chattering problem in most existing VSC control methods is removed because the integration of the control rate smoothens the control action. It is worth mentioning that the proposed method can also be applied to flight vehicle with reaction rocket engines whose propulsion directions are adjustable. The potential advantage of using such adjustable gimbal-like rocket engine is that the number of rocket engines can be significantly reduced for a given flight vehicle, which could make the overall system structurally simpler and operationally more effective.

Immune system inspired fault detection and identification with application to crew exploration vehicles

Immune system exhibits robust, adaptive and highly distributed cognitive capabilities comparable to the brain. It learns during its lifetime to differentiate and eliminate non-self from self substances of the body by some interesting mechanisms, such as mutation and cloning. In this work, we explore a new approach to fault detection and identification by mimicking the mechanisms of immune system. The developed fault detection and identification system (FDIS) is able to detect and identify diverse system faults dynamically. Moreover, as a learning system, the proposed FDIS can automatically adjust its detecting accuracy and is adaptive to new type of faults during its operation. Simulation on fault-detection in crew exploration vehicle (CEV) is conducted and the results verify the effectiveness of the proposed method

Control of reusable launch vehicle using neuro-adaptive approach

This work explores neural networks (NN) based control approach to nonlinear flight systems in the presence of disturbances and uncertainties. Neuro-adaptive control incorporating with two neural network (NN) units is proposed to cope with NN reconstruction error and other lumped system uncertainties. It is shown that with the proposed strategy, the angles of attack, sideslip and body-axis roll of the vehicle are effectively controlled. The method does not involve analytical estimation of the upper bounds on ideal weights, reconstruction error, or nonlinear functions. Stable on-line weight-tuning algorithms are derived based on Lyapunovs stability theory. Analyses and simulations confirm the effectiveness of the proposed control scheme.

Dealing with fault dynamics in nonlinear systems via double neural network units

Most fault detection and accommodation methods have traditionally been derived based on linear system modeling techniques, which restrict the type of practical failure situation that can be modeled. In this paper we explore a methodology for fault accommodation in nonlinear dynamic systems. A new control scheme is derived by incorporating two neural network (NN) units to effectively attenuate and compensate uncertain dynamics due to unpredictable faults. It is shown that the method is independent of the nature of the fault. Numerical simulations are included to demonstrate the effectiveness of the proposed method.

Control of DC motors using adaptive and memory-based approach

This study investigates the speed control problem of separately excited DC motors via automatically regulating armature and field voltages. Three set of control algorithms are developed to achieve this objective. Theoretical analysis and computer simulation demonstrate that the proposed algorithms are effective to achieve high performance control under varying operation conditions.

Neuro-Adaptive Formation Control of Multi-Mobile Vehicles: Virtual Leader Based Path Planning and Tracking

This paper presents a neuro intelligent virtual leader based approach for close formation of a group of mobile vehicles. Neural Network-based trajectory planning is incorporated into the leading vehicle so that an optimal reference path is generated automatically by the virtual leader, which guides the whole team vehicles to the area of interest as precisely as possible. The steering control scheme is derived based on the structural properties of the vehicle dynamics. Simulation on multiple vehicles formation is conducted as a verification of the effectiveness of the proposed method.