Decentralized Neural Block Control for a Robot Manipulator based in UKF training

This paper presents a solution of the trajectory tracking problem for robotic manipulators using a recurrent high order neural network (RHONN) structure to identify the robot arm dynamics, and based on this model a discrete-time control law is derived, which combines block control and the sliding mode techniques. The block control approach is used to design a nonlinear sliding surface such that the resulting sliding mode dynamics is described by a desired linear system. The neural network learning is performed on-line by Kalman filtering. The local controller for each joint uses only local angular position and velocity measurements. The applicability of the proposed control scheme is illustrated via simulations.

This paper presents a decentralized control scheme, based on a recurrent neural identifier with a block control structure, and its application to robotics manipulators. A local joint controller is proposed for each joint, using only local angular position and velocity measurements. These very simple local joint controllers allow trajectory tracking, with reduced computations. The applicability of the proposed scheme is illustrated, via simulations, first by the applications to a two degree of freedom robotic manipulator and then to a seven degree of freedom one.

This paper presents a discrete-time decentralized control strategy for trajectory tracking of a seven degrees of freedom (DOF) redundant robot. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The neural network learning is performed online using Kalman filtering. The motion of each joint is controlled independently using only local angular position and velocity measurements. The proposed controller is validated via simulations.

Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot

This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high order neural network (RHONN) to identify the plant model. Using this model, a control law is derived, which combines block control and sliding modes techniques. The applicability of the scheme is illustrated via real time implementation for a DC motor with separate winding excitation.

The accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.

GPS disciplined crystal oscillator technology has been widely used to obtain high-precision and low-cost frequency standards. But there are two factors that would affect the accuracy of the frequency correction. The first is the random error and outliers contained in the one pulse per second (PPS) outputted by the GPS. The second factor is the frequency nonlinear drift of crystal oscillator because of the aging and the temperature. To solve these problems, a mathematical model of the frequency difference between IPPS signal and crystal oscillator signal is established in this paper. And then a novel high-precision method based on the sequence weighted adaptive Unscented Kalman filter (UKF) algorithm is proposed on the basis of the mathematical model. To be more specific, the UKF is used to address the problem of random error and frequency nonlinear drift. And the effect of outliers is eliminated by weighing sequence in the UKF algorithm. The adaptive filtering technology can reduce the impact of the variable and random system noise. Numerical simulation and verification are described. The results show that the proposed method can make frequency standards higher stability and precision.

Numerical simulation of a new early gas kick detection method using UKF estimation and GLRT

This paper studies the relay selection in wireless cooperative communication systems. Since the high mobility of Unmanned Aerial Vehicles (UAVs) will lead to the change of link state, a relay selection algorithm based on position prediction is proposed in this paper to better study this problem. First, the Unscented Kalman Filter (UKF) algorithm is used to predict the real-time location information of each UAV, and based on the location information, it is determined whether the current UAV relay node needs to be replaced. The real-time location information of each UAV is predicted by UKF algorithm, which is the basis for determining whether the current UAV relay node needs to be replaced. Then, the outage probability of each standby UAV relay node is calculated, and the standby UAV relay node is sorted according to the link outage probability. Finally, the greedy algorithm is used to find the node with the smallest interruption probability among the backup UAV relay nodes as the next UAV relay node. The simulation results show that in terms of position prediction, the UKF algorithm is more accurate than the Kalman Filtering (KF) algorithm, and the error is smaller. In terms of interruption probability, the relay selection algorithm proposed in this paper reduces the interruption probability of the link, and the interruption probability of the link is more stable.

For a comfortable thermal environment, the main parameters are indoor air humidity and temperature. These parameters are strongly coupled, causing the need to search for multivariable control alternatives that allow efficient results. Therefore, in order to control both the indoor air humidity and temperature for direct expansion (DX) air conditioning (A/C) systems, different controllers have been designed. In this paper, a discrete-time neural inverse optimal control scheme for trajectories tracking and reduced energy consumption of a DX A/C system is presented. The dynamic model of the plant is approximated by a recurrent high-order neural network (RHONN) identifier. Using this model, a discrete-time neural inverse optimal controller is designed. Unscented Kalman filter (UKF) is used online for the neural network learning. Via simulation the scheme is tested. The proposed approach effectiveness is illustrated with the obtained results and the control proposal performance against disturbances is validated.

State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time under different battery ageing degrees. Then, the TLS algorithm is used to estimate the battery SOH to improve the prior estimation accuracy of SOC. Next, the SOC is calculated by the UKF algorithm, and finally, a more accurate SOH can be obtained according to the UKF-based SOC trajectory. The battery-in-the-loop experiments are utilized to verify the proposed algorithm. For the cases of temperature change up to 35 °C and capacity decay up to 10%, our joint estimator can achieve ultra-low errors, bounded by 2%, respectively, for SOH and SOC. The proposed method paves the way for the advancement of battery use in applications, such as electric vehicles and microgrid applications.

<div class="section abstract"><div class="htmlview paragraph">The application of millimeter-wave radar technology in autonomous driving has become increasingly widespread with the rapid development of intelligent transportation systems. However, millimeter-wave radar is easily affected by environmental noise, multipath reflections, and electromagnetic interference, resulting in a large number of invalid target signals that reduce the system’s detection accuracy and safety. We proposes a method for filtering invalid targets based on interference signal characteristics and an Adaptive Interactive Multiple Model Kalman Filter (IMM-KF) target tracking algorithm. First, we effectively filter out empty targets, ghost targets, and false targets through a threshold method and lifecycle assessment, achieving a filtering rate exceeding 99.8%. Second, the improved Adaptive IMM-KF algorithm, combined with the Hungarian algorithm, associates and tracks multiple targets. The root mean square error (RMSE) of our methods is reduced by 7.07% and 8.05% compared to the traditional IMM and Unscented Kalman Filter (UKF) algorithms in scenarios with a single pedestrian moving in a straight line. And in scenarios involving both pedestrians and motor vehicles, the RMSE is reduced by 4.29% and 7.61% compared to the traditional IMM and UKF algorithms, respectively. Real-vehicle experiments have validated the robustness and accuracy of the proposed method across various scenarios.</div></div>

Constrained nonlinear state estimation based on the UKF approach

State estimation of induction machines may be a difficult problem, due to the non-linear behavior of theirs. For non-linear state estimation, the unscented Kalman filter (UKF) is a well-known extension of the linear Kalman filter. Operation of the UKF algorithm strongly depends on the process and measurement noise covariance parameters of the estimator. Determination of these parameters is not straightforward and can be difficult, especially if the number of state variables and hence the system complexity is relatively high. In this paper, the UKF algorithm is applied for speed sensorless state estimation of induction machines in such a way that seven state variables are estimated from the measured stator currents and from the known excitation voltages. In order to tune the noise parameters of the UKF, a new, optimization-based method is presented. This tuning method provides adequate behavior for the observer beside difficult operating conditions as it has been shown by simulation experiment.

Accurate remaining useful life (RUL) prediction under the noisy environment is a big challenge for the health management of modern industrial systems since the extraction of the accurate data structure from heavily corrupted data is difficult. In recent years, the kernel adaptive filter (KAF) has been widely adopted to solve the robust regression problem due to its low-complexity and high-approximation capability and robustness while the applications in battery RUL prediction are still few and far between. Thus, this paper is concerned with long-term RUL prediction using the KAF method. At first, concretely speaking, a robust KAF algorithm is derived based on the double-Gaussian-mixture (DGM) cost function, which is used to learn the capacity degradation mechanism from contaminated capacity data and so as to build the long-term prediction model. Second, a robust unscented Kalman filter (UKF) algorithm employing the DGM-based cost function is developed, which is then combined with the KAF-based prediction model to realize a more accurate and reliable prediction. Under the hybrid prognostic framework, the proposed UKF algorithm is applied to filter the noisy observations. When the observation data are inaccessible, the predicted data from the off-line trained KAF-based prediction model are adopted as the approximated value of the real observations for the UKF algorithm to optimize the prediction results and to provide the uncertainty representation. The experimental results reveal that the proposed method has great robustness when the measurements contain noise and large outliers, which makes it possible to get satisfactory prediction performance without preprocessing the data manually.