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This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a modification of a Sliding Mode Classical Observer (SMCO) to adapt it to the fractional approach. This adaptation involves using a set of definitions based on fractional calculus theory, particularly the approach developed by Riemann–Liouville, resulting in a Sliding Mode Fractional Observer (SMFO). Both observers are used to perform disturbance reconstruction considered additive in a Quadrotor Unmanned Aerial Vehicle (UAV) model. Then, this work presents the fractional-order sliding mode observer's mathematical formulation and integration into the Quadrotor UAV model. To validate the quality of the disturbance reconstruction process of the proposed SMFO observer scheme, numerical simulations are carried out, where a reconstruction quality indicator (BQR) is proposed based on the analysis of performance indices such as the Mean Square Error (MSE), the First Probability Moment (FPM), and Second Probability Moment (SPM), which were obtained for both the SMCO and the SMFO. The simulation results demonstrate the efficacy of the proposed observer in accurately reconstructing disturbances under various environmental conditions. Comparative analyses with SMCO highlight the advantages of the fractional-order approach in terms of reconstruction accuracy and improvement of its transitory performance. Finally, the presented SMFO offers a promising avenue for enhancing the reliability and precision of disturbance estimation, ultimately contributing to the advancement of robust control strategies for Quadrotor UAV systems. [ABSTRACT FROM AUTHOR]

This paper proposes a robust control allocation for the capture control of the space inertial sensor's test mass under overcritical conditions. Uncertainty factors of the test mass control system under the overcritical condition are analyzed first, and a 6-DOF test mass dynamics model with system uncertainty is established. Subsequently, a time-varying weight function is designed to coordinate the allocation of 6-DOF generalized forces. Moreover, a robust control allocation method is proposed to distribute the commanded forces and torques into individual electrodes in an optimal manner, which takes into account the system uncertainties. This method transforms the robust control allocation problem into a second-order cone optimization problem, and its dual problem is introduced to simplify the computational complexity and improve the solving efficiency. Numerical simulation results are presented to illustrate and highlight the fine performance benefits obtained using the proposed robust control allocation method, which improves capture efficiency, increases the security margin and reduces allocation errors. [ABSTRACT FROM AUTHOR]

The fin field-effect transistor (FinFET) has been the mainstream technology on the VLSI platform since the 22 nm node. The silicon-on-insulator (SOI) FinFET, featuring low power consumption, superior computational power and high single-event effect (SEE) resistance, shows advantages in integrated circuits for space applications. In this work, a rad-hard design methodology for SOI FinFETs is shown to improve the devices' tolerance against the Total Ionizing Dose (TID) effect. Since the fin height direction enables a new dimension for design optimization, a 3D Source/Drain (S/D) design combined with a gate dielectric de-footing technique, which has been readily developed for the 14 nm node FinFETs, is proposed as an effective method for SOI FinFETs' TID hardening. More importantly, the governing mechanism is thoroughly investigated using fully calibrated technology computer-aided design (TCAD) simulations to guide design optimizations. The analysis demonstrates that the 3D rad-hard design can modulate the leakage path in 14 nm node n-type SOI FinFETs, effectively suppress the transistors' sensitivity to the TID charge and reduce the threshold voltage shift by >2x. Furthermore, the rad-hard design can reduce the electric field in the BOX region and lower its charge capture rate under radiation, further improving the transistor's robustness. [ABSTRACT FROM AUTHOR]

A neural network-based global fast terminal sliding mode control method with non-linear differentiator (NNFTSMC) is proposed in this paper to design the dynamic control system for three-axis stabilized platform. The dynamic model of the three-axis stabilized platform is established with various uncertainties and unknown external disturbances. To overcome the external disturbance and reduce the output chatter of the classical sliding mode control (SMC) system, the improved global fast terminal sliding mode control method using the nonlinear differentiator and neural network techniques is proposed and implemented in the three-axis stabilized platform system. The global fast terminal sliding mode controller can make the controlled state approach to the sliding surface in a finite time. To eliminate the system output chatter, the nonlinear differentiator is employed to obtain the differentiation of the signal. The neural network is introduced to estimate the uncertainties disturbances to improve the stability and the robustness of the control system. The stability and the robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNFTSMC method is verified and compared with the classical proportion-integral-differential (PID) controller, SMC controller and fast terminal sliding mode controller (FTSMC) through the computer simulation. Results validate the effectiveness and robustness of the proposed NNFTSMC method in presence of uncertainties and unknown external disturbances. [ABSTRACT FROM AUTHOR]

Coprime array with M + N sensors can achieve an increased degrees-of-freedom (DOF) of O (M N) for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Direct sequence spread spectrum (DSSS) signals are now widely used in air and underwater acoustic communications. A receiver which does not know the pseudo-random (PN) sequence cannot demodulate the DSSS signal. In this paper, firstly, the principle of principal component analysis (PCA) for PN sequence estimation of the DSSS signal is analyzed, then a modified online unsupervised learning machine (LEAP) is introduced for PCA. Compared with the original LEAP, the modified LEAP has the following improvements: (1) By normalizing the system state transition matrices, the modified LEAP can obtain better robustness when the training errors occur; (2) with using variable learning steps instead of a fixed one, the modified LEAP not only converges faster but also has excellent estimation performance. When the modified LEAP is converging, we can utilize the network connection weights which are the eigenvectors of the autocorrelation matrix of the DSSS signal to estimate the PN sequence. Due to the phase ambiguity of the eigenvectors, a novel approach which is based on the properties of the PN sequence is proposed here to exclude the wrong estimated PN sequences. Simulation results showed that the methods mentioned above can estimate the PN sequence rapidly and robustly, even when the DSSS signal is far below the noise level. [ABSTRACT FROM AUTHOR]

This paper investigates the skid steering of four-wheel independent-drive (4WID) electric vehicles (EV) and a differential steering of a 4WID EV with a steer-by-wire (SBW) system in case of steering failure. The dynamic models of skid steering vehicle (SSV) and differential steering vehicle (DSV) are established and the traditional front-wheel steering vehicle with neutral steering characteristics is selected as the reference model. On this basis, sideslip angle observer and two different sliding mode variable structure controllers for SSV and DSV are designed respectively. Co-simulation results of CarSim and Simulink show that the designed controller for DSV not only controls the yaw rate and sideslip angle of DSV to track those of the reference model exactly, but also ensures the robustness of the controlled system compared with the designed controller for SSV. And the differential driving torque needed to realize the differential steering is much smaller than that for skid steering, which indicates the possibility of the differential steering in case of steering failure. [ABSTRACT FROM AUTHOR]

Makkeh, Theis, and Vicente found that Cone Programming model is the most robust to compute the Bertschinger et al. partial information decomposition (BROJA PID) measure. We developed a production-quality robust software that computes the BROJA PID measure based on the Cone Programming model. In this paper, we prove the important property of strong duality for the Cone Program and prove an equivalence between the Cone Program and the original Convex problem. Then, we describe in detail our software, explain how to use it, and perform some experiments comparing it to other estimators. Finally, we show that the software can be extended to compute some quantities of a trivaraite PID measure. [ABSTRACT FROM AUTHOR]