Jianda Han

An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot

For improving the estimation accuracy and the convergence speed of the unscented Kalman filter (UKF), a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. On the basis of the MIT rule, an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function. The updated covariance is fed back into the normal UKF. Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations. The asymptotic properties of this adaptive UKF are discussed. Simulations are conducted using an omni-directional mobile robot, and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.

A novel adaptive unscented Kalman filter for nonlinear estimation

The normal unscented Kalman filter (UKF) suffers from performance degradation and even divergence while mismatch between the noise distribution assumed to be known as a priori by UKF and the true ones in a real system. In this paper, a novel adaptive UKF (AUKF) is developed and applied to nonlinear joint estimation of both time-varying states and parameters. A cost function is built based on the error between the covariance matrices of innovation and their corresponding estimations. An adaptive algorithm is then designed to online update the covariance of the process noise by minimizing the cost function. The updated covariance is further fed back into the normal UKF. As a result of using such an adaptive mechanism, the robustness of conventional UKF is substantially improved with respect to the uncertain and time-varying noise covariance in the real system. To illustrate this mechanism, simulations are conducted on the dynamics of an unmanned helicopter by jointly estimating both the states and model errors. The improvements of the proposed AUKF are demonstrated by comparing the results with and without the adaptive mechanism.

Experimental Comparison Research on Active Vibration Control for Flexible Piezoelectric Manipulator Using Fuzzy Controller

Space manipulators are flexible structures. Vibration problem will be unavoidable due to motion or external disturbance excitation. Model based control methods will not maintain the required accuracy because of the existence of nonlinear factors and parameter uncertainties. To solve these problems, fuzzy logic control laws with different membership function groups are adopted to suppress vibrations of a flexible smart manipulator using collocated piezoelectric sensor/actuator pair. Also, dual-mode controllers combining fuzzy logic and proportional integral control are designed, for suppressing the lower amplitude vibration near the equilibrium point significantly. Experimental comparison research is conducted, using fuzzy control algorithms and the dual-mode controllers with different membership functions. The experimental results show that the adopted fuzzy control algorithms can substantially suppress the larger amplitude vibration; and the dual-mode controllers can also damp out the lower amplitude vibration significantly. The experimental results demonstrate that the proposed fuzzy controllers and dual-mode controllers can suppress vibration effectively, and the optimal placement is feasible.

Enhanced LQR control for unmanned helicopter in hover

Real time adaptability is of central importance for the control of unmanned helicopter flying under different circumstances. In this paper, an active model is employed to handle the time varying uncertainties involved in the helicopter dynamics during flight. In the scheme, a normal LQR control designed from a simplified model at hovering is enhanced by means of unscented-Kalman-filter (UKF) based estimation, which tries to online capture the error between the simplified model and the full dynamics. This is intended to achieve adaptive performance without the need of adjusting the controller modes or parameters along with the changing dynamics of helicopter. Simulations with respect to a model helicopter are conducted to verify both the UKF-based estimation and the enhanced LQR control. Results are also demonstrated with the normal LQR control with the active model enhancement

An Adaptive UKF Algorithm and Its Application in Mobile Robot Control

In order to improve the performance of the UKF a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. Based on the MIT rule, an adaptive algorithm is designed to online update the covariance of the process uncertainties by minimizing the cost function. The updated covariance is further fed back into the normal UKF. Such an adaptive mechanism is intended to compensate the lack on the priori knowledge of process uncertainty distribution and improve the performance of UKF for the applications such as active state and parameter estimations. Simulations are conducted with respect to the dynamics of an omni-directional mobile robot, and the results obtained by the proposed AUKF are compared with those by normal UKF to demonstrate the effectiveness and improvements.

Active Model-Based Predictive Control and Experimental Investigation on Unmanned Helicopters in Full Flight Envelope

For the control of unmanned helicopters in full flight envelope, an active model based predictive control scheme is developed in this brief. Dynamics in full envelope is modeled, with uncertainties represented by the system model error and process noise. The model error depends on both helicopter dynamics and flight mode, and the process noise is assumed unknown but bounded. Based on the set-membership filter, an active modeling based stationary increment predictive control, based on the estimated model error and its boundary to optimally compensate the model error, as well as the aerodynamics time delay, is proposed. The proposed method has been implemented on the ServoHeli-40 unmanned helicopter platform and experimentally tested; the results have demonstrated its effectiveness.

Acceleration-Feedback-Enhanced Robust Control of an Unmanned Helicopter

While a few proposed control strategies have shown their acceptable effectiveness, performance improvement on stability and robustness of unmanned helicopters are still imperative and a great challenge due to strong nonlinearities, extensive parameter uncertainties and external disturbances when the flight condition is terrible, such as flight on a windy day. Because acceleration feedback control is advantageous in terms of simple controller structure and easy implementation, we attempt to incorporate it into the tracking control of an unmanned helicopter that is highly nonlinear and underactuated. In this paper, we use a prefilter to formulate a new acceleration feedback control and then use it as a robust enhancement for the H(infinity) algorithm to attenuate uncertainties and external disturbances involved in the tracking control of an unmanned helicopter. We conduct simulations with an unmanned model helicopter and compare the tracking performance of the helicopter with and without acceleration feedback control. The results show that the use of acceleration feedback control does enhance tracking performance greatly compared to the standard H(infinity) control.

A novel HCI based on EMG and IMU

The technology of human-computer interaction (HCI) is developing rapidly in tandem with the advancement of information and biological technologies. Many new types input device are introduced into this field; some of them are aimed to benefit special groups of people like old or disabled persons. In the meantime, Electromyography (EMG) and Inertia Measure Unit (IMU) have been readily available and extensively applied in control systems in many fields. In this paper, we propose a novel EMG-IMU based mouse controller that controls cursor movements based on IMU signals. The displacement of the cursor is determined by integrating the acceleration signal from the IMU, which moves with the operators arm. The mouse operations such as left click, right click and wheel scroll, are commanded through EMG signals. The pattern recognition algorithm, Linear Discriminant Analysis (LDA), is adopted to classify the EMG data into several clusters, which correspond to the pre-defined mouse operations. Experimental results have indicated that the proposed mouse controller can achieve an accuracy of 88%.

A Review on Fault Diagnosis and Fault Tolerant Control Methods for Single-rotor Aerial Vehicles

Faults or failures are inevitable to occur and their prompt detection and isolation are essential for the dependability of various systems and for avoiding damages to the system itself, persons and the environment. Therefore, the safety of helicopter platforms have attracted the attention of many researchers in the past two decades. In order to deal with these problems, this paper presents an overview of the recent development and current researches in the field of fault diagnosis, including analytical/model-based, signal processing-based and knowledge-based techniques, and also passive/active fault- tolerant control approaches. Among various helicopters, single-rotor aerial vehicles, i.e. manned helicopters, unmanned helicopters, two and three degree-of-freedom unmanned helicopter experimental platforms, are considered for providing an overall picture of the fault diagnosis and fault-tolerant control approaches based on the review of journal articles in last two decades, conference articles in last several years and some books.

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcraft Unmanned Aerial Vehicle (RUAV) sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.