基于飞轮的欠驱动航天器姿态控制器设计

在以飞轮作为姿态控制执行机构的航天器中,如果部分飞轮发生故障而使得航天器欠驱动时,姿态控制性能会急剧下降.本文对两个匕轮的刚性航天器,研究了姿态控制问题.在零动量的假设下,利用Backstepping方法,为欠驱动姿态控制系统设计了一个新型的姿态控制器.设计过程分两步进行:首先,根据姿态运动学模型,设计出可使航天器姿态全局渐近稳定的控制角速率;然后,根据姿态动力学模型,得到使航天器姿态全局渐近稳定的控制力矩.该控制器为非连续控制器,可使航天器姿态误差全局一致渐近收敛为零,并使系统具有良好的动态性能.计算机仿真表明,本文所设计出的控制器是可行的.     

Double EWMA controller using neural network-based tuning algorithm for MIMO non-squared systems

The double exponentially weighted moving average (dEWMA) control method is a popular algorithm for adjusting a process from run to run in semiconductor manufacturing. For MIMO non-squared statistic systems, the singular value decomposition (SVD) method is used for decoupling and the SVD-based dEWMA control scheme is treated as a MIMO extension of dEWMA control design. To enhance the performance and robustness of the linear system in the presence of ramp disturbances and white noises, the neural network-based adaptive algorithm is used to automatically tune the dEWMA controller parameters. Under the specified input patterns, the early stop criterion for the training-validation neural networks, and the stability constraints added in the tuning mechanism, the simulations show that the proposed control technique can effectively improve the means and standard deviations of the process outputs.     

Synthesis of a linearly interpolated gain scheduling controller for large flexible spacecraft ETS-VIII

This paper illustrates a design procedure for a linearly interpolated gain scheduling controller for Engineering Test Satellite VIII (ETS-VIII) using its linear parameter-varying (LPV) model. The LPV model here consists of piecewise-linear functions of the paddle rotation angle and a norm-bounded perturbation. The main purpose of this research is to derive a simple structured scheduling law that can be easily implemented in a satellite onboard computer. The derived gain has only two grid points and is scheduled simply by linear interpolation, which is desirable from the standpoint of implementability. Moreover, since the synthesis condition is based on parameter-dependent Lyapunov functions, it gives less conservative results than existing methods. Simulation results are presented to show the effectiveness of the proposed synthesis.     

Adaptive fuzzy fault-tolerant attitude control of spacecraft

This paper investigates the attitude control of spacecraft in the presence of unknown mass moment of inertia matrix, external disturbances, actuator failures, and control input constraints. A robust adaptive controller is proposed with the utilization of fuzzy logic and backstepping techniques. The unit quaternion is employed to describe the attitude of spacecraft for global representation without singularities. The system uncertainty is estimated by introducing a fuzzy logic system. The adaptive mechanism has only two parameters to be adapted on-line because the adaptive law of the proposed controller is derived from the norm of the weight matrix. The stability of the closed-loop system is guaranteed by Lyapunov direct approach. Results of numerical simulations state that the proposed controller is successful in achieving high attitude performance in the presence of parametric uncertainties, external disturbances, actuator failures, and control input constraints.     

Design of an analytic constrained predictive controller using neural networks

This paper shows how the solution of the standard predictive control problem can be recast as a continuous function of the state, the reference signal, the noise and the disturbances, and hence can be approximated arbitrarily closely by a feed-forward neural network. The existence of such a continuous mapping eliminates the need for linear independency of the active constraints, and therefore the resulting analytic constrained predictive controller will combine constraint handling with speed while being applicable to fast and complex control systems with many constraints. The effectiveness of the proposed controller design methodology is shown for a simulation example of an elevator model and for a real-time laboratory inverted pendulum system.     

Servo controller design using neural networks

The dynamics of a physical plant may be difficult to express as concise mathematical equations. In practice there exist uncertainties that cannot be modeled with the system equations. Hence, robustness against system uncertainties is essential in a control system design. In this article, multilayered neural networks (MNNs) are used to compensate for model uncertainties of a dynamical system. Neural network models are used along with a classical linear servo controller derived from the linear state space equations. These models are trained so that system uncertainties are compensated. The design of a servo system indicates the enhanced performance of the neural-network-based servo controller as compared to the classical servo controller.     

基于迭代学习观测器的航天器姿态主动容错控制

针对含有外部扰动和执行器故障的一类航天器姿态控制系统,本文提出基于迭代学习观测器的主动容错控制方案.首先,建立了含有外部扰动和执行器故障的航天器姿态控制系统的运动学和动力学模型.其次,为了提高观测器的故障估计精度,在传统迭代学习观测器设计基础上引入上一时刻状态估计误差信息,文章提出一种改进型学习估计算法.进一步,基于滑模控制和指定时间稳定理论,利用学习观测器的故障估计信息设计指定时间主动容错控制器.与现有的航天器主动容错控制方案相比,本文所提出的算法的优势在于可以使故障系统的姿态能在指定时间跟踪上指令信号.基于Lyapunov方法,本文从理论上证明了改进型学习观测器和姿态容错控制系统的稳定性.最后,通过数值仿真,说明了所提容错控制方案的有效性和可行性.     

Fuzzy neural networks for tuning PID controller for plants withunderdamped responses

In this paper, the fuzzy neural network (FNN) for tuning proportional-integral-derivative (PID) controller for plants with underdamped step responses is proposed. The underdamped systems are modeled by second-order-plus-dead-time transfer functions. For deriving the FNN, the dominant pole assignment method is applied to design the PID controllers for a batch of test plant models that represent the plants with underdamped responses. Then, a fuzzy neural modeling method is utilized to identify the relationship between the parameters that characterize the plant dynamics and the controller parameters. We then utilize the obtained FNN to tune the PID controller for plants with underdamped responses. Several simulation examples are given to demonstrate the effectiveness and robustness of the FNN obtained     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.     

Realisation of a Riccati equation-based controller using gradient-type neural networks

This paper presents a solution to an algebraic Riccati-matrix equation-based robust control law using a set of gradient-type neural networks. The proposed neural network solves a representative Riccati-matrix equation, which is commonly encountered in robust control problems. The class of neural networks is a variant of the continuous-time Hopfiled network. To verify the proposed feedback control scheme in real-time applications, a high-speed digital signal processor has been used to emulate the network operations. This allows an on-line implementation that is adaptable to system parameter changes. Finally, illustrative examples show the potential of simulation under hard real-time conditions.     

Real-time implementation of a dynamic fuzzy neural networks controller for a SCARA

This paper presents the design, development and implementation of a Dynamic Fuzzy Neural Networks (D-FNNs) Controller suitable for real-time industrial applications. The unique feature of the D-FNNs controller is that it has dynamic self-organising structure, fast learning speed, good generalisation and flexibility in learning. The approach of rapid prototyping is employed to implement the D-FNNs controller with a view of controlling a Selectively Compliance Assembly Robot Arm (SCARA) in real time. Simulink, an iterative software for simulating dynamic systems, is used for modelling, simulation and analysis of the dynamic system. The D-FNNs controller was implemented through Real-Time Workshop (RTW). RTW generates C-codes from the Simulink block diagrams and in turn, the generated codes (object codes) are downloaded to the dSPACE DS1102 floating-point processor, together with the supporting files, for execution. The performance of the D-FNNs controller was found to be superior and it matches favourably with the simulation results.     

Attitude control of spacecraft simulator without angular velocity measurement

In this paper the attitude control of a spacecraft simulator using Reaction Wheels (RW) as the actuators is investigated. The main goal of the current study is to bring the RWs to the rest at the end of the maneuver without angular velocity measurement. A modified feedback linearization controller is applied by considering the Euler angles of the simulator as the output and the RWs angular momentums as the internal state variables. The stability of the proposed controller and the internal dynamics is analyzed using Lyapunov theory. Two modified sliding mode observers are designed to estimate the angular velocities of the spacecraft attitude control subsystem simulator. The proposed observers do not use the control input and the detailed knowledge of the model and thus it can be implemented easily. The global stability of the system is proved. The proposed controller and observers are finally evaluated numerically and experimentally on an attitude spacecraft simulator.     

Global finite-time attitude regulation using bounded feedback for a rigid spacecraft

This paper investigates the problem of global attitude regulation control for a rigid spacecraft under input saturation. Based on the technique of finite-time control and the switching control method, a novel global bounded finite-time attitude regulation controller is proposed. Under the proposed controller, it is shown that the spacecraft attitude can reach the desired attitude in a finite time. In addition, the bound of a proposed attitude controller can be adjusted to any small level to accommodate the actuation bound in practical implementation.     

Stochastic neural networks

The first purpose of this paper is to present a class of algorithms for finding the global minimum of a continuous-variable function defined on a hypercube. These algorithms, based on both diffusion processes and simulated annealing, are implementable as analog integrated circuits. Such circuits can be viewed as generalizations of neural networks of the Hopfield type, and are called diffusion machines.Our second objective is to show that learning in these networks can be achieved by a set of three interconnected diffusion machines: one that learns, one to model the desired behavior, and one to compute the weight changes.This research was supported in part by U.S. Army Research Office Grant DAAL03-89-K-0128.     

Synchronized multiple spacecraft rotations

The objective of this paper is to present formation control laws for maintaining attitude alignment among a group of spacecraft in either deep space or earth orbit. The paper presents two control strategies based on emergent behavior approaches. Each control strategy considers the desired formation behaviors of convergence to the final formation goal, formation keeping, and the desire to rotate the spacecraft about fixed axes. The first approach uses velocity feedback and the second approach used passivity-based damping. In addition, we prove analytically that our approach guarantees formation keeping throughout the maneuver. Simulation results demonstrate the effectiveness of our approach.     

几何约束下的航天器姿态机动控制

针对航天器姿态机动过程中受几何约束的问题,提出一种控制方法使系统在满足局部约束的情况下获得较好的全局性能.为了考虑全局性能并减少全局规划的计算代价,采用伪谱法以较少节点得到满足几何约束的路径节点.在局部节点跟踪控制中,通过分析航天器姿态机动所受的几何约束以及四元数在线性化状态方程中的传递形式,设计具有保守性的几何约束凸化表达式,并给出了姿态机动预测控制律.仿真结果验证了所提出方法的可行性和有效性.     

Distributed adaptive attitude synchronization of multiple spacecraft

This paper addresses the distributed attitude synchronization problem of multiple spacecraft with unknown inertia matrices. Two distributed adaptive controllers are proposed for the cases with and without a virtual leader to which a time-varying reference attitude is assigned. The first controller achieves attitude synchronization for a group of spacecraft with a leaderless communication topology having a directed spanning tree. The second controller guarantees that all spacecraft track the reference attitu...     

Multiple neural-network-based adaptive controller using orthonormalactivation function neural networks

A direct adaptive control scheme is developed using orthonormal activation function-based neural networks (OAFNNs) for trajectory tracking control of a class of nonlinear systems. Multiple OAFNNs are employed in these controllers for feedforward compensation of unknown system dynamics. Choice of multiple OAFNNs allows a reduction in overall network size reducing the computational requirements. The network weights are tuned online, in real time. The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis. The developed neural controllers are evaluated experimentally and the experimental results are shown to support theoretical analysis. The effects of network parameters on system performance are experimentally evaluated and are presented. The superior learning capability of OAFNNs is demonstrated through experimental results. The OAFNNs were able to model the true nature of the nonlinear system dynamics characteristics for a rolling-sliding contact as well as for stiction.     

Robust attitude tracking control for a rigid spacecraft under input delays and actuator errors

Attitude control of a rigid spacecraft under input delays, disturbances, parameter uncertainties, actuator errors, and constraints is a challenging problem. In this paper, these problems are considered simultaneously, and a robust control approach to attitude tracking of a rigid spacecraft is exploited. The design methodology is based on three steps: (1) compensating input delays by using the backstepping technique, (2) design of a disturbance observer for the delayed system by using the super-twisting algorithm to estimate unknown internal and external disturbances, then adding a feedforward compensation law based on the estimated signal to the backstepping controller to attenuate the effects of disturbances, (3) employing a robust least-square scheme to map the specified control command on the redundant actuators in the presence of actuator error, including actuator magnitude deviation and misalignment, with regard to actuator amplitude and rate constraints. The effectiveness of the proposed algorithm is shown by various numerical simulations.     

Finite-time attitude quantised control for rigid spacecraft

This paper investigates the quantised finite-time attitude control for rigid spacecraft in the presence of external disturbance. First, a novel quantiser is designed with the combination and improvement of the traditional hysteresis logarithmic quantiser and hysteresis uniform quantiser, so that the innovative quantiser in this paper has the advantages of both hysteresis and uniform quantisers in ensuring chattering free and acceptable quantisation errors for better transient and steady-state performances. Second, a finite-time controller is synthesised even under disturbances and quantisation errors, and the closed-loop system/state converges to the region near zero in finite time. Finally, the attitude stabilisation and attitude tracking simulation results for the rigid spacecraft are presented to illustrate the effectiveness and feasibility of the proposed control strategy.