A PID Tuning Approach for Inertial Systems Performance Optimization

In the practice of control the industrial processes, proportional-integral-derivative controller remains pivotal due to its simple structure and system performance-oriented tuning process. In this paper are presented two approaches for synthesis the proportional-integral-derivative controller to the models of objects with inertia, that offer the procedure of system performance optimization based on maximum stability degree criterion. The proposed algorithms of system performance optimization were elaborated for model of objects with inertia second and third order and offer simple analytical expressions for tuning the PID controller. Validation and verification are conducted through computer simulations using MATLAB, demonstrating successful performance optimization and showcasing the effectiveness PID controllers’ tuning. The proposed approaches contribute insights to the field of control, offering a pathway for optimizing the performance of second and third-order inertial systems through robust controller synthesis.

About the Thermodynamics and Aging of Self-Organizing Systems

We know that the total daily energy dissipation increases in complex organisms like the humans. It’s very probable that this increase in total energy dissipation is related to the progressive increase in mass. But we also know that day by day the dissipation of energy per unit mass decreases in these organisms. We intend to verify if this decrease is only an expression of the second law of thermodynamics, or if it is related to the increase in mass that occurs in these organisms. For this, we set ourselves the following objectives: verify the correlation between total energy dissipation and the evolution of body mass, and verify the correlation between the dissipation of energy per unit of mass and the evolution of body mass. As a result of the data analysis, we found a high degree of correlation between total energy dissipation and the evolution of body mass. And we also found a high correlation between the energy dissipated per unit of mass and the evolution of body mass. We can conclude that self-organization produces not only an increase in mass, but also a decline in energy dissipation per unit mass beyond what is expected by the second law of thermodynamics.

ROTATING FIBER OPTIC GYRO STRAP-DOWN INERTIAL NAVIGATION SYSTEM WITH THREE ROTATING AXES

The principle of the inertial navigation system（INS） with rotating inertial measurement unit （IMU） is analyzed. A new IMU is established to rotate round each axis in three directions. Then, the related error models for the designed system during rotating are deduced and the improved system is built. Finally, the performance simulation of the proposed system is provided. The simulation result indicates that the designed system can improve the accuracy of the roll and the pitch as well as heading by rotating three axes, thus guaranting the heading accuracy. Moreover, based on the principle of rotation at six different positions, such structure can carry out real-time calibration, and improve the system performance.

NONLINEAR FILTERING ALGORITHM FOR IN S INITIAL ALIGNMENT

The initial alignment error equation of an INS (Inertial Navigation System) with large initial azimuth error has been derived and nonlinear characteristics are included. When azimuth error is fairly small, the nonlinear equation can be reduced to a linear one. Extended Kalman filter, iterated filter and second order filter formulas are derived for the nonlinear state equation with linear measurement equation. Simulations results show that the accuracy of azimuth error estimation using extended Kalman filter is better than that of using standard Kalman filter while the iterated filter and second order filter can give even better estimation accuracy.

STUDY OF FAST TRANSFER ALIGNMENT ON MOVING BASE

This paper holds that the key to improve the hitting rate of air-to-air missiles is to decrease the error of initial alignment of the inertial navigation system. Therefore, considering that the alignment should be completed within a specified short time, this paper presents the theory of transfer alignment and the computing way of accuracy in an air-to-air missile, where the platform inertial navigation system, or master INS, is adopted on aircraft, and the strap-down inertial navigation system, or slave INS, is used on missile. It emphasizes the idea of transfer alignment, that is, calibration of the slave INS is based on the master platform, and adopts a reasoning measure to deal with the installing-error-angle. And it is proved by simulation that the transfer alignment can be quickly achieved.

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.

A rotating inertial navigation system with the rotating axis error compensation consisting of fiber optic gyros

An effective and flexible rotation and compensation scheme is designed to improve the accuracy of rotating inertial navigation system （RINS）. The accuracy of single-axial R1NS is limited by the errors on the rotating axis. A novel inertial measurement unit （IMU） scheme with error compensation for the rotating axis of fiber optic gyros （FOG） RINS is presented. In the scheme, two couples of inertial sensors with similar error characteristics are mounted oppositely on the rotating axes to compensate the sensors error. Without any change for the rotation cycle, this scheme improves the system＇s precision and reliability, and also offers the redundancy for the system. The results of 36 h navigation simulation prove that the accuracy of the system is improved notably compared with normal strapdown INS, besides the heading accuracy is increased by 3 times compared with single-axial RINS, and the position accuracy is improved by 1 order of magnitude.

A novel temperature-compensation method based on correlation analysis for multi-FOG INS

Aimed at improving the bias stability of Fiber-Optic Gyroscope(FOG)-based inertial navigation systems in environments of various ambient temperatures,a novel temperaturecompensation method based on a correlation analysis of the same batch of FOGs is proposed.The empirical mode decomposition method was employed to filter the high-frequency noises of the FOGs.Then,the correlation information of the multiple FOGs was used to analyze the feasibility of the method.Eventually,the same residual error of the FOGs was compensated via the simple piecewise linear models.The experimental results indicate that excellent compensation effects for both high-and low-accuracy FOGs are achieved using the proposed method.Specifically,the accuracies of high-accuracy FOGs are improved by approximately 33.9%,20%,and 31.2%,while those of low-accuracy FOGs are improved by approximately 39.1%,20.8%,and 26.1%.The method exhibits the merits of simplicity,validity,and stability,and thus is expected to be widely used in engineering applications.

Multiple Fading Factors Kalman Filter for SINS Static Alignment Application

To solve the problem that the standard Kalman filter cannot give the optimal solution when the system model and stochastic information are unknown accurately, single fading factor Kalman filter is suitable for simple systems. But for complex systems with multi-variable, it may not be sufficient to use single fading factor as a multiplier for the covariance matrices. In this paper, a new multiple fading factors Kalman filtering algorithm is presented. By calculating the unbiased estimate of the innovation sequence covariance using fenestration, the fading factor matrix is obtained. Adjusting the covariance matrix of prediction error Pk｜k-1 using fading factor matrix, the algorithm provides different rates of fading for different filter channels. The proposed algorithm is applied to strapdown inertial navigation system （SINS） initial alignment, and simulation and experimental results demonstrate that, the alignment accuracy can be upgraded dramatically when the actual system noise characteristics are different from the pre-set values. The new algorithm is less sensitive to uncertainty noise and has better estimation effect of the parameters. Therefore, it is of significant value in practical applications.

Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration

Inertial Navigation System/Celestial Navigation System(INS/CNS)integration,especially for the tightly-coupled mode,provides a promising autonomous tactics for Hypersonic Vehicle(HV)in military demands.However,INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity,non-additive noise and dynamic complexity.This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter(CKF)to handle the strong INS error model nonlinearity caused by HV's high dynamics.It combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial measurements.Subsequently,a technique for the detection of dynamic model uncertainty is developed,and the augmented CKF is modified with fading memory to tackle dynamic model uncertainty by rigorously deriving the fading factor via the theory of Mahalanobis distance without artificial empiricism.Simulation results and comparison analysis prove that the proposed method can effectively curb the adverse impacts of non-additive noise and dynamic model uncertainty for inertial measurements,leading to improved performance for HV navigation with tightly-coupled INS/CNS integration.