Shenmin Song

Asymptotical stability analysis of “PD+” controller for spacecraft attitude tracking system*

To overcome the difficulty that the asymptotical stability of spacecraft attitude tracking system, when “PD+” controller is employed, cannot be obtained by directly using common Lyapunovs theorems, several methods are introduced and summarized. Owing to the powerful features of Matrosovs theorem and Barbalats lemma in the field of non-autonomous system stability analysis, they are respectively utilized to acquire the results of asymptotical stability of spacecraft attitude tracking system to which a common “PD+” controller is applied. Further more, to simplify the stability analysis, another special “PD+” controller is designed, by virtue of an auxiliary variable, and the corresponding stability analysis and comparisons with the preceding two methods are given as well.

Decentralized robust coordinated control for formation flying spacecraft with coupled attitude and translational dynamics

This article investigates the decentralized coordinated control problem for formation flying spacecraft associated with coupled attitude and translational dynamics by probing into local information exchange among spacecraft. A six degree-of-freedom coupled model is established by employing the unified Euler–Lagrange formulations of attitude dynamics and translational dynamics. With the novel use of adaptive control, a six degree-of-freedom continuous robust coordinated controller is proposed in the presence of parametric uncertainty and time-varying disturbances with unknown bounds. Furthermore, it is extended to the case where both of switching topologies and non-constant communication delays exist. The six degree-of-freedom coordinated controller can guarantee formation spacecraft follow a time-varying reference trajectory while aligning their attitudes and keeping some special formation configuration. Numerical simulations demonstrate the effectiveness of the proposed control scheme.

Safe approaching to non-cooperative spacecraft using potential function guidance based fuzzy logic system

A guidance control method is proposed for the mission of autonomous rendezvous and docking with non-cooperative target spacecraft. Firstly, the relative motion equations of state are described in a line of sight coordinate frame. Then artificial potential function guidance is used for chaser to approach non-cooperative target. Moreover, this guidance method is based on fuzzy logic system which satisfies safe approaching constraints in final approaching corridor scenario between the two spacecrafts. Stability of the proposed method is analyzed by using Lyapunov theory. Results obtained from numerical simulation studies demonstrate the validity of the method formulated in this paper.

The Design of RBF Neural Networks for Solving Overfitting Problem

One of the biggest problems in designing or training RBF neural networks are the overfitting problem. The traditional design of RBF neural networks may be pursued in a variety of ways. In this paper, we present a method for the design of RBF networks to solve overfitting problem. For a practical application, frequency information is usually available for designing RBF networks by frequency domain analysis, which has a sound mathematical basis. We try to include the frequency information into the design of RBF networks, which achieve the task of approximated a function in certain frequency range and have the property of structural risk minimization. After the structure of designed network is determined, the linear weights of the output layer are the only set of adjustable parameters. The approach of design is verified by approximation cases

New algorithm to calculate the strapdown inertial measurement unit parameters

A new calibration procedure was proposed for strap down inertial measurement unit (IMU) in a body reference frame which was independent of the turntable. The proposed methodology required no precise mechanical platform for the calibration of the accelerometer and the rate-gyros. Error models of IMU parameters were derived from the physical characteristic of three accelerometers and three rate gyros, which taked into account the sensor axis misalignments, scale factor, gyro constant drifts, and biases inherent in the manufacture of IMU. Least squares method was used to evaluate the accelerometers and gyros model coefficients. According to the experimental results, we can see that the proposed algorithms is less affected by turntables accuracy, process is simplified.

Robust adaptive neural networks with an online learning technique for robot control

A new robust adaptive neural networks tracking control with online learning controller is proposed for robot systems. A learning strategy and robust adaptive neural networks are combined into a hybrid robust control scheme. The proposed controller deals mainly with external disturbances and nonlinear uncertainty in motion control. A neural network (NN) is used to approximate the uncertainties in a robotic system. Then the disadvantageous effects on tracking performance, due to the approximating error of the NN in robotic system, are attenuated to a prescribed level by an adaptive robust controller. The learning techniques of NN will improve robustness with respect to uncertainty of system, as a result, improving the dynamic performance of robot system. A simulation example demonstrates the effectiveness of the proposed control strategy.

Unscented particle filter with estimation windows in submarine tracking

In order to estimate the state of uncertain models, a robust filter based on risk sensitive estimator is proposed, which could automatically change the state noise covariance according to the magnitude of the risk function. As a result, sample impoverishment could be mitigated. Another contribution of this paper is to take every sensor measurement into account, when large sample sets are needed to represent the systems uncertainty, thereby avoiding the risk of losing valuable sensor information during the update of the filter. A simulation example of submarine bearing and frequency tracking is presented, the experiment results show that new algorithm performs better than generic particle filter and unscented particle filter.

Robust control for linear system based on gradient flow neural network

A gradient flow algorithm model developed for the on-line robust pole assignment is proposed for solving Sylvester equations. The algorithm shows to be capable of synthesizing linear feedback control systems via on-line computing feedback gain matrix and desired closed-loop poles. Meanwhile, the close-loop system matrix is least sensitive to perturbation or uncertainty, and uniformly asymptotically stable in largely range. Simulation results are shown that the proposed approach is suitable to problem of robust stabilization for nonlinear system and on-line robust pole assignment.

Risk Sensitive Unscented Particle Filter for Bearing and Frequency Tracking

Robust filter based on risk sensitive estimator is derived to estimate the state of the uncertain models, while the estimation error involves two terms, the first term coincides with the minimum value of the risk sensitive cost function, the second one is the distance between the true and design probability models. The proposed algorithm, which introduces risk sensitive estimator into the unscented particle filter, could automatically change the state noise covariance according to the magnitude of the risk function. As a result, sample impoverishment could be mitigated. In the simulation of submarine bearing and frequency tracking, the performance of the new algorithm is compared with the unscented kalman filter and the unscented particle filter. Simulation results show that the new algorithm performs better than the two others.

Adaptive wavelet neural network friction compensation of mechanical systems

Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L2(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme.