Search results

This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed.

An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF.

Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm.

The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

Quadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.

First, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.

The figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.

This paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

Accurate glide path tracking is vital to the automatic carrier landing task of unmanned aerial vehicle (UAV). The purpose of this paper is to develop a reliable flight controller that can simultaneously deal with external disturbance, structure fault and actuator fault.

The automatic carrier landing task is resolved into the glide path tracking problem and attitude tracking problem. The disturbance observer-based adaptive sliding mode control scheme is proposed for system stabilization, disturbance rejection and fault tolerance.

Both the Lyapunov method and exemplary simulations can prove that the disturbance estimation error and the attitude tracking error converge in finite time in the presence of external disturbances and various faults.

The presented algorithm is testified by a UAV automatic carrier landing simulation, which shows the potential of practical usage.

The barrier function is introduced to adaptively update both the sliding mode observer gain and sliding mode controller gain, so that the sliding mode surface could converge to a predefined region without overestimation. The proposed flight controller ensures a secure carrier landing task.

This paper aims to tackle the problem for a fully actuated unmanned aerial vehicle (FUAV) to perform physical interaction tasks in the Global Positioning System-denied environments without expensive motion capture system (like VICON) under disturbances.

A tether-based positioning system consisting of a universal joint, a tether-actuated absolute position encoder and an attitude sensor is designed to provide reliable position feedback for the FUAV. To handle the disturbances, including the tension force caused by the taut tether, model uncertainties and other external disturbances such as aerodynamic disturbance, a hybrid disturbance observer (HDO) combining the position-based method and momentum-based technology with force sensor feedback is designed for the system. In addition, an HDO-based impedance controller is built to allow the FUAV interacting with the environment and meanwhile rejecting the disturbances.

Experimental validations of the proposed control algorithm are implemented on a real FUAV with the result of nice disturbance rejection capability and physical interaction performance.

A cheap alternative to indoor positioning system is proposed, with which the FUAV is able to interact with external environment and meanwhile reject the disturbances under the help of proposed hybrid disturbance observer and the impedance controller.

The purpose of this paper is to consider the process of design and implementation of an enhanced fuzzy H_∞ (EFH_∞) estimation algorithm to determine the attitude and heading angles of ground vehicles, which are frequently affected by considerable exogenous disturbances. To detect the changes of disturbances, a fuzzy system is designed based on expert knowledge and experiences of a navigation engineer. In the EFH_∞ estimator, the intensity bounds of disturbances affecting the measurements are updated using a heuristic combination of three change‐detection indices. Performance of the proposed estimator is evaluated by Monte‐Carlo simulations and field tests of three kinds of vehicles using a manufactured attitude‐heading reference system (AHRS). In both simulations and real tests, the proposed estimator results in a superior performance compared to those of the recently developed and standard H_∞ estimators.

Design, implementation and real tests of the EFH_∞ estimator are considered for an AHRS specialized for vehicular applications. In the AHRS, three‐axis accelerometers (TAA) and three‐axis magnetometers (TAM) may be affected by large disturbances due to non‐gravitational accelerations and local magnetic fields. Therefore, the design parameters of EFH_∞ estimator including the theoretic bound of disturbance intensity and the attenuation level are adaptively tuned using a fuzzy combination of three change‐detection indices. Once a sensor is affected by an exogenous disturbance, the fuzzy system will increase the scale factor of the corresponding measurement disturbance to place more confidence on the data of the AHRS dynamics including measurements of gyros with respect to the data coming from the TAA and TAM.

An intelligent fault detector is proposed for considering changes of disturbances to adjust the upper bounds of the estimator's disturbances and the length of data to update the fuzzy system inputs. The EFH_∞ estimator is suitable to attenuate the effects of disturbances changes on accurate estimation of the attitude and heading angles, intelligently.

The paper provides a fuzzy state estimator for adaptively adjusting the theoretic disturbance matrices according to the actual intensity of the disturbances affecting the AHRS dynamics and the measurement sensors.

Discrete‐event simulation (DES) and disturbance reduction techniques are a combination for improving efficiency in manufacturing systems. The DES modelling allows different tests to be carried out by step‐by‐step alteration. The use of manufacturing improvement techniques should be combined for best results. The changes in disturbances will show us different alternatives in output of the manufacturing system. Two case studies have been drawn up to study the possibilities for disturbance reduction in manufacturing systems by using DES with the proposed method for improved overall manufacturing efficiency. The case studies showed an improvement of output of 14 per cent and 18 per cent, respectively.

The purpose of this paper is to handle the problem of the radial disturbance caused by rotor mass unbalance and load change in a bearingless induction motor (BIM).

The active disturbance rejection controller (ADRC) is used to replace the traditional PI controller, and a cubic interpolation method is used to fit the nonlinear function of ADRC, so as to improve the control performance. Meanwhile, a disturbance observer is applied to the suspension system, and the observed disturbance acceleration is compensated to the suspension system in the form of current; thus, the suppression of the rotor radial disturbance is realized.

The proposed method can effectively suppress the radial disturbance of the rotor, meliorate the suspension performance of the motor and enhance the anti-interference ability of the system. Besides, it has excellent dynamic and static performance.

A radial disturbance control strategy of the BIM based on improved ADRC is proposed is to suppress the radial disturbance of the rotor. The improved ADRC is to enhance the control performance of the system, and the disturbance observer is designed to observe and compensate the disturbance.

The purpose of this paper is to develop a new method for classification of power quality (PQ) disturbances such as the sag, interruption, swell, harmonic, notch, oscillatory transient and impulsive transient.

A PQ disturbances classification system based on wavelet packet energy and multiclass support vector machines (MSVM) is proposed to discriminate seven types of PQ disturbances. The PQ disturbance signals are first decomposed into components in different subbands using discrete wavelet packet transform (DWPT). Statistical features of the decomposed signals are required to characterize the PQ disturbances. A MSVM classifier follows to classify the PQ disturbances.

The proposed method could effectively detect information from disturbance waveforms using DWPT and MSVM techniques, which is verified on over 700 samples.

The classification stage of the proposed method does not differentiate the disturbances occurred simultaneously.

The proposed method possesses high recognition rate, so it is suitable for the PQ monitoring system for detection and classification of disturbances.

The paper describes a new and efficient way of classification of PQ disturbances. In this paper, an attempt has been made to extract efficient features of the PQ disturbances using DWPT. It is observed that these features can help correctly classify the PQ disturbances, even under noisy conditions. The MSVM is compared with artificial neural network (ANN) and it is found that the MSVM classifier gives the better result.

The aim of this paper is to design a nonlinear disturbance observer-based control (DOBC) method obtained by patching a control method developed using a robust adaptive technique and a DO.

For designing a DOBC, initially a class of nonlinear system is considered with an external disturbance. First, a DO is designed to estimate the external disturbances. This estimate is combined with the controller to reject the disturbances and obtain the desired control objective. For designing a controller, the robust sliding mode control theory is used. Furthermore, instead of using a constant switching gain, an adaptive gain tuning criterion is designed using Lyapunov candidate function. To investigate the stability and effectiveness of the developed DOBC, stability analysis and simulation study are presented.

The major findings of this paper include the criteria of designing the robust adaptive control parameters and investigating the disturbance rejection when robust adaptive control based DOBC is developed.

In practice, the flight of quadrotor is affected by different kind of external disturbances, thus leading to the change in dynamics. Hence, it is necessary to design DOBCs based on robust adaptive controllers such that the quadrotor model adapts to the change in dynamics, as well as nullify the effect of disturbances.

Designing DOBCs based on robust control method is a common practice; however, the robust adaptive control method is rarely developed. This paper contributes in the domain of DOBC based on robust adaptive control methods such that the behavior of controller varies with the change in dynamics occurring due to external disturbances.