An-Min Zou

Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network

A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the reaching phase and the sliding phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.

Adaptive Control of a Class of Nonlinear Pure-Feedback Systems Using Fuzzy Backstepping Approach

A controller is proposed for the robust backstepping control of a class of nonlinear pure-feedback systems using fuzzy logic. The proposed control scheme utilizes fuzzy logic systems to learn the behavior of the unknown plant dynamics. Filtered signals are employed to circumvent algebraic loop problems encountered in the implementation of the usual controllers, and the approximation errors can be efficiently counteracted by employing smooth robust compensators. Most importantly, the uniform ultimate boundedness of all signals in the closed-loop system can be guaranteed, and a priori knowledge of the plant dynamics is no longer required. Furthermore, the proposed method can be used for adaptive control of a large class of single-input--single-output nonlinear systems in both strict-feedback and pure-feedback forms, and has great potential in many diverse applications. The performance of the proposed approach is demonstrated through three simulation examples, including one nonlinear pure-feedback and two nonlinear strict-feedback systems.

Adaptive Control of an Electrically Driven Nonholonomic Mobile Robot via Backstepping and Fuzzy Approach

This paper investigates the tracking control of an electrically driven nonholonomic mobile robot with model uncertainties in the robot kinematics, the robot dynamics, and the wheel actuator dynamics. A robust adaptive controller is proposed with the utilization of adaptive control, backstepping and fuzzy logic techniques. The proposed control scheme employs the adaptive control approach to design an auxiliary wheel velocity controller to make the tracking error as small as possible in consideration of uncertainties in the kinematics of the robot, and makes use of the fuzzy logic systems to learn the behaviors of the unknown dynamics of the robot and the wheel actuators. The approximation errors and external disturbances can be efficiently counteracted by employing smooth robust compensators. A major advantage of the proposed method is that previous knowledge of the robot kinematics and the dynamics of the robot and wheel actuators is no longer necessary. This is because the controller learns both the robot kinematics and the robot and wheel actuator dynamics online. Most importantly, all signals in the closed-loop system can be guaranteed to be uniformly ultimately bounded. For the dynamic uncertainties of robot and actuator, the assumption of ldquolinearity in the unknown parametersrdquo and tedious analysis of determining the ldquoregression matricesrdquo in the standard adaptive robust controllers are no longer necessary. The performance of the proposed approach is demonstrated through a simulation example.

Quaternion-Based Adaptive Output Feedback Attitude Control of Spacecraft Using Chebyshev Neural Networks

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Finite-Time Output Feedback Attitude Tracking Control for Rigid Spacecraft

This brief investigates the finite-time output feedback attitude control of a rigid spacecraft. First, a nonlinear observer is designed. Through geometric homogeneity and Lyapunov theories, it is shown that the proposed observer can achieve the semiglobal finite-time stability. Then, a finite-time output feedback controller is proposed based on the finite-time observer. Rigorous proof shows that the proposed control law ensures semiglobal stability and guarantees the attitude of a rigid spacecraft to track a time-varying reference attitude in finite time. Simulation results are presented to illustrate the performance of the proposed controller.

Distributed Attitude Coordination Control for Spacecraft Formation Flying

A distributed attitude coordination control scheme using terminal sliding mode (TSM) is proposed for a group of spacecraft in the presence of external disturbances. A novel fast terminal sliding manifold is presented, and a robust control term based on the hyperbolic tangent function is employed to suppress bounded external disturbances. The finite-time stability of the overall closed-loop system is guaranteed by a Lyapunov-based approach, and numerical simulations are presented to demonstrate the performance of the proposed controller.

A behavior controller based on spiking neural networks for mobile robots

Spiking neural networks (SNNs), as the third generation of artificial neural networks, have unique advantages and are good candidates for robot controllers. A behavior controller based on a spiking neural network is designed for mobile robots to avoid obstacles using ultrasonic sensory signals. Detailed structure and implementation of the controller are discussed. In the controller the integrated-and-firing model is used and the SNN is trained by the Hebbian learning algorithm. Under the framework of SNNs, fewer neurons are employed in the controller than those of the classical neural networks (NNs). Experimental results show that the proposed controller is effective and is easy to implement.

Attitude Coordination Control for a Group of Spacecraft Without Velocity Measurements

This paper investigates the problem of velocity-free attitude coordination control for a group of spacecraft with attitude represented by modified Rodrigues parameters. The communication flow among neighbor spacecraft is described by an undirected connected graph. Two velocity-free attitude coordination control schemes are proposed. By employing linear reduced-order observers, robust control and Chebyshev neural networks, the first velocity-free control scheme allows a group of spacecraft to simultaneously align their attitude and track a time-varying reference attitude even in the presence of unknown mass moment of inertia matrix and external disturbances, where all spacecraft have access to the common reference attitude. The second control law guarantees a group of spacecraft to track a time-varying reference attitude without requiring velocity measurements even when the common reference attitude is available only to a subset of the group members. Furthermore, the stability of the overall closed-loop system for both control laws is guaranteed by a Lyapunov-based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controllers.

Robust Attitude Coordination Control for Spacecraft Formation Flying Under Actuator Failures

This paper examines attitude coordination control for spacecraft formation flying. A class of distributed adaptive fault-tolerant attitude coordination control laws is proposed. The proposed control laws do not require online identification of failures. Using the Lyapunov approach and graph theory, it is shown that the control laws guarantee a group of spacecraft that simultaneously track a common time-varying reference attitude, even when the reference attitude is available only to a subset of the members of a group. Finally, numerical simulation is presented to show that the distributed controller is successful in achieving high-attitude tracking and synchronization performance, even in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, actuator failures, and control saturation limits.

Neural Network-Based Distributed Attitude Coordination Control for Spacecraft Formation Flying With Input Saturation

This brief considers the attitude coordination control problem for spacecraft formation flying when only a subset of the group members has access to the common reference attitude. A quaternion-based distributed attitude coordination control scheme is proposed with consideration of the input saturation and with the aid of the sliding-mode observer, separation principle theorem, Chebyshev neural networks, smooth projection algorithm, and robust control technique. Using graph theory and a Lyapunov-based approach, it is shown that the distributed controller can guarantee the attitude of all spacecraft to converge to a common time-varying reference attitude when the reference attitude is available only to a portion of the group of spacecraft. Numerical simulations are presented to demonstrate the performance of the proposed distributed controller.