Daidai Chen

Ensuring safe and stable vehicle operation during high-speed emergency obstacle avoidance and low-adhesion roads is the core priority of active research. This paper proposes a novel stability control method for distributed drive vehicles based on neural network adaptive terminal sliding mode control (NNATSMC), aiming to establish the upper layer control architecture for direct yaw moment control (DYC). Firstly, to address the problem that conventional sliding mode control (SMC) relies on the unknown upper bound, an adaptive switching gain based on barrier function (BF) is proposed under sliding mode state constraints to alleviate sliding mode chattering. Secondly, a terminal complementary sliding mode surface is designed based on complementary sliding mode theory, which ensures the terminal sliding mode to achieve rapid converges within finite time. In addition, the Radial Basis Function Neural Network (RBFNN) estimator can compensate for lumped uncertainties online, reducing the dependence of controller design on model accuracy, avoiding control performance degradation caused by the small convergence region of the sliding surface and further improves the robustness of the system. Furthermore, lower controller considering multiple constraints is established to output the four wheel torque. Finally, comparative simulations through four groups of operating conditions with different severity levels are carried out for verification; the proposed NNATSMC in this paper can better guarantee vehicle stability under extreme conditions and possesses better adaptability to multiple operation conditions.

Accurate state-of-charge (SOC) estimation is essential for the safe and reliable operation of lithium-ion batteries. However, conventional SOC estimation methods mainly use electrical signals, which provide limited information about internal battery changes. Ultrasonic sensing provides additional information related to internal structural changes and has attracted increasing attention for SOC estimation. To better assess the value of ultrasonic features as auxiliary inputs for SOC estimation, this study examines them in terms of both correlation with SOC and stability under varying operating conditions. Two representative features, time of flight (TOF) and signal amplitude (SA), are systematically compared under different temperatures and C-rates. Their effectiveness is then further validated by fusing ultrasonic features with voltage, current, and temperature in a multilayer perceptron model. The results show that SA exhibits stronger correlation with SOC and better thermal stability than TOF, and that incorporating SA into the input set of voltage, current, and temperature reduces the root mean square error by 29.0% under cycling conditions and 48.4% under dynamic load profiles. These results indicate that ultrasonic information can effectively improve SOC prediction performance, and the improvement is greater when more stable features are used.

Traditional generalized likelihood test method can detect faults while can not isolate multiple faults in redundant strapdown inertial navigation system. In this paper, a multi-faults detection and isolation method based on generalized likelihood test and linear prediction approach is proposed. A generalized likelihood test method is used to detect the fault of the system, and the linear prediction method is used to estimate the values of inertial sensors. The faulty inertial sensors can be isolated through comparing the estimated values with measurements of inertial sensors, and the faulty information is recorded for system reconstruction. Through simulation and experiment, it is proved that new method can accurately detect and isolate multiple faults and thus guarantee the reliability of strapdown inertial navigation system.

This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements.

To obtain the absolute position of a target is one of the basic topics for non-cooperated target tracking problems. In this paper, we present a simultaneously calibration method for an Inertial navigation system (INS)/Global position system (GPS)/Laser distance scanner (LDS) integrated system based target positioning approach. The INS/GPS integrated system provides the attitude and position of observer, and LDS offers the distance between the observer and the target. The two most significant errors are taken into jointly consideration and analyzed: (1) the attitude measure error of INS/GPS; (2) the installation error between INS/GPS and LDS subsystems. Consequently, a INS/GPS/LDS based target positioning approach considering these two errors is proposed. In order to improve the performance of this approach, a novel calibration method is designed to simultaneously estimate and compensate these two main errors. Finally, simulations are conducted to access the performance of the proposed target positioning approach and the designed simultaneously calibration method.

In order to improve the accuracy and reliability of micro-electro mechanical systems (MEMS) navigation systems, an orthogonal rotation method-based nine-gyro redundant MEMS configuration is presented. By analyzing the accuracy and reliability characteristics of an inertial navigation system (INS), criteria for redundant configuration design are introduced. Then the orthogonal rotation configuration is formed through a two-rotation of a set of orthogonal inertial sensors around a space vector. A feasible installation method is given for the real engineering realization of this proposed configuration. The performances of the novel configuration and another six configurations are comprehensively compared and analyzed. Simulation and experimentation are also conducted, and the results show that the orthogonal rotation configuration has the best reliability, accuracy and fault detection and isolation (FDI) performance when the number of gyros is nine.

For long-time operation applications based on single-axis rotation inertial navigation system (SRINS), the estimation and compensation of gyroscope's constant drift along the rotational axis in comprehensive calibration is the most important problem. In this paper, a novel comprehensive calibration method for SRINS is presented. During this process, SRINS is operating in the outer level damping mode. Kalman filter has been designed based on the SRINS model with the damping network. The velocity and position provided by differential GPS (DGPS) are introduced as reference information for the estimation of rotational axis gyroscope drift. For stationary scenario, the observability degree of system states are given by an observability analysis based on singular value decomposition. The validity of this method is confirmed by simulations. The influence of linear accelerated motion on the estimation of drift is obtained.

Transfer alignment is a moving base alignment method which has been widely used in shipborne low-accuracy inertial navigation system (INS). The accuracy of transfer alignment has mainly determined the entire navigation performance of low-accuracy INS and should be obtained. In this paper, transfer alignment accuracy evaluation methods for shipborne salve INS are analyzed. Observability analysis of evaluation system is implemented, and its validity on the illustration and prediction for the potential smoothing results are verified. The relationships between accelerated motion, rotation observation variable and the performance of evaluation approaches are obtained. Then, a novel transfer alignment accuracy evaluation approach is presented. The azimuth information provided by differential GPS (DGPS) is introduced together with the velocity and position provided by DPGS as the observation variables. Kalman filter and RTS smoother are applied to process these data. Simulations are conducted and the results show that this method exhibits excellent performance, and all the errors of smoothed estimations are less than 1 percent, especially with a better estimation result of the azimuth misalignment angle.