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The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics.

Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value.

The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF.

The presented algorithm is applicable for spacecraft relative attitude and position estimation.

The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.

A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.

The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.

The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

The purpose of this paper is to conduct the particle swarm optimization (PSO)‐assisted adaptive Kalman filter (AKF) for global positioning systems (GPS) navigation processing. Performance evaluation for the PSO‐assisted Kalman filter (KF) as compared to the conventional KF is provided.

The position‐velocity also knows as constant velocity process model can be applied to the GPS KF adequately when navigating a vehicle with constant speed. However, when an abrupt acceleration motion occurs, the filtering solution becomes very poor or even diverges. To avoid the limitation of the KF, the PSO can be incorporated into the filtering mechanism as dynamic model corrector. The PSO is utilized as the noise‐adaptive mechanism to tune the covariance matrix of process noise and overcome the deficiency of KF. In other words, PSO‐assisted KF approach is employed for tuning the covariance of the GPS KF so as to reduce the estimation error during substantial maneuvering.

The paper provides an alternative approach for designing an AKF and provides an example in the application to GPS.

The proposed scheme enhances the improvement in estimation accuracy. Application of the PSO to the GPS navigation filter design is discussed. The method takes advantage of both the adaptation capability and the robustness of numerical stability.

The PSO are employed for assisting the AKF. The use of optimization such as PSO for AKF has seldom been seen in the open literature.

The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs.

In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model.

Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value.

This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

Fault detection, isolation and reconfiguration of the flight control system is an important problem to obtain healthy flight. This paper aims to propose an integrated approach for aircraft fault-tolerant control.

The integrated structure includes a Kalman filter to obtain without noise, a full order observer for sensor fault detection, a GOS (generalized observer scheme) for sensor fault isolation and a fuzzy controller to reconfigure of the healthy sensor. This combination is simulated using the state space model of a lateral flight control system in case of disturbance and under sensor fault scenario.

Using a dedicated observer scheme, the detection and time of sensor fault are correct, but the sensor fault isolation is evaluated incorrectly while the faulty sensor is isolated correctly using GOS. The simulation results show that the suggested approach works affectively for sensor faults with disturbance.

This paper proposes an integrated approach for aircraft fault-tolerant control. Under this framework, three units are designed, one is Kalman filter for filtering and the other is GOS for sensor fault isolation and another is fuzzy logic for reconfiguration. An integrated approach is sensitive to faults that have disturbances. The simulation results show the proposed integrated approach can be used for any linear system.

Estimating the pose – position and orientation – of a moving object such as a robot is a necessary task for many applications, e.g., robot navigation control, environment mapping, and medical applications such as robotic surgery. The purpose of this paper is to introduce a novel method to fuse the information from several available sensors in order to improve the estimated pose from any individual sensor and calculate a more accurate pose for the moving platform.

Pose estimation is usually done by collecting the data obtained from several sensors mounted on the object/platform and fusing the acquired information. Assuming that the robot is moving in a three-dimensional (3D) world, its location is completely defined by six degrees of freedom (6DOF): three angles and three position coordinates. Some 3D sensors, such as IMUs and cameras, have been widely used for 3D localization. Yet, there are other sensors, like 2D Light Detection And Ranging (LiDAR), which can give a very precise estimation in a 2D plane but they are not employed for 3D estimation since the sensor is unable to obtain the full 6DOF. However, in some applications there is a considerable amount of time in which the robot is almost moving on a plane during the time interval between two sensor readings; e.g., a ground vehicle moving on a flat surface or a drone flying at an almost constant altitude to collect visual data. In this paper a novel method using a “fuzzy inference system” is proposed that employs a 2D LiDAR in a 3D localization algorithm in order to improve the pose estimation accuracy.

The method determines the trajectory of the robot and the sensor reliability between two readings and based on this information defines the weight of the 2D sensor in the final fused pose by adjusting “extended Kalman filter” parameters. Simulation and real world experiments show that the pose estimation error can be significantly decreased using the proposed method.

To the best of the authors’ knowledge this is the first time that a 2D LiDAR has been employed to improve the 3D pose estimation in an unknown environment without any previous knowledge. Simulation and real world experiments show that the pose estimation error can be significantly decreased using the proposed method.

The purpose of this paper is to present a novel high-precision relative navigation method for tight formation-keeping based on thrust on-line identification.

Considering that thrust acceleration cannot be measured directly, an on-line identification method of thrust acceleration is explored via the estimated acceleration of major space perturbation and the inter-satellite relative states obtained from space-borne acceleration sensors; then, an effective identification model is designed to reconstruct thrust acceleration. Based on the identified thrust acceleration, relative orbit dynamics for tight formation-keeping is established. Further, using global positioning system (GPS) measurement information, a modified extended Kalman filter (EKF) is suggested to obtain the inter-satellite relative position and relative velocity.

Compared with the normal EKF and the adaptive robust EKF, the proposed modified EKF has better estimation accuracy in radial and along-track directions because of accurate compensation of thrust acceleration. Meanwhile, high-precision relative navigation results depend on high-precision acceleration sensors. Finally, simulation studies on a chief-deputy formation flying control system are performed to verify the effectiveness and superiority of the proposed relative navigation algorithm.

This paper provides a reference in solving the problem of high-precision relative navigation in tight formation-keeping application.

This paper proposes a novel on-line identification method for thrust acceleration and shows that thrust identification-based modified EKF is more efficient in relative navigation for tight formation-keeping.

This paper aims to propose a novel system identification and resonance suppression strategy for motor-driven system with high-order flexible manipulator.

In this paper, first, a unified mathematical model is proposed to describe both the flexible joints and the flexible link system. Then to suppress the resonance brought by the system flexibility, a model based high-order notch filter controller is proposed. To get the true value of the parameters of the high-order flexible manipulator system, a fuzzy-Kalman filter-based two-step system identification algorithm is proposed.

Compared to the traditional system identification algorithm, the proposed two-step system identification algorithm can accurately identify the unknown parameters of the high order flexible manipulator system with high dynamic response. The performance of the two-step system identification algorithm and the model-based high-order notch filter is verified via simulation and experimental results.

The proposed system identification method can identify the system parameters with both high accuracy and high dynamic response. With the proposed system identification and model-based controller, the positioning accuracy of the flexible manipulator can be greatly improved.

The purpose of the paper is to present an innovation-based new actuator/surface fault detection and isolation (FDI) method, which is sensitive to the changes in the innovation mean of the Kalman filter (KF) and the KF tuning method for the case of actuator/surface failure.

The multiple system noise scale factors (MSNSFs) are used in this method as the monitoring statistics. MSNSFs are determined to make it possible to perform the actuator/surface FDI operations simultaneously.

The introduced FDI algorithm can detect and isolate the loss of effectiveness type actuator/surface faults in real time. The proposed KF tuning method works effectively against actuator/surface fault. The actuator/surface fault detection, isolation and filter tuning are achieved by just using a simple modification over the conventional KF.

The MSNSF-based actuator/surface fault detection, isolation and filter tuning algorithms are investigated together for the first time. The actuator/surface FDI operations are performed simultaneously.

This paper mainly aims to deal with the problems of uncertainties including modelling errors, unknown dynamics and disturbances caused by load mutation in control of permanent magnet synchronous motor (PMSM).

This paper proposes an enhanced speed sensorless vector control method based on an active disturbance rejection controller (ADRC) for a PMSM. First, a state space model of the PMSM is obtained for the field orientation control strategy. Second, a sliding mode observer (SMO) based on back electromotive force (EMF) is introduced to replace the encode to estimate the rotor flux position angle and speed. Third, an infinite impulse response (IIR) filter is introduced to eliminate high frequency noise mixed in the output of the sliding mode observer. In addition, a speed control method based on an extended state observer (ESO) is proposed to estimate and compensate for the total disturbances. Finally, an experimental set-up is built to verify the effectiveness and superiority of the proposed ADRC-based control method.

The comparative experimental results show that the proposed speed sensorless control method with the IIR filter can achieve excellent robustness and speed tracking performance for PMSM system.

An enhanced sensorless control method based on active disturbance rejection controller is designed to realize high precision control of the PMSM; the IIR filter is used to attenuate the chattering problem of traditional SMO; this method simplifies the system and saves the total cost due to the speed sensorless technology.

The use of sensorless can reduce costs and be more beneficial to actual industrial application.

The proposed enhanced speed sensorless vector control method based on an ADRC with the IIR filter enriches the control method of PMSM. It can ameliorate system robustness and achieve excellent speed tracking performance.