Switching Criterion Research Articles

Performance analysis of MIMO FSO adaptive mode switching in Malaga turbulent channels with pointing error

MIMO FSO (Free Space Optical) systems can provide high data rates through spatial multiplexing or improve transmission reliability using spatial diversity, but atmospheric turbulence is a bottleneck that restricts communication performance. Adaptive transmission technology can effectively suppress the influence of turbulence and improve communication performance. In this paper, switching between spatial multiplexing and diversity is proposed as an adaptive transmission technology for MIMO links to improve the diversity performance of spatial multiplexing of FSO systems. The switching criterion is based on the Minimum Euclidean Distance (MED) of the spatial mapping scheme. The MED expressions for two spatial modes at the receiver are derived, and the bit error rate (BER) approximations are provided, respectively. We evaluate the BER performance of spatial multiplexing and spatial diversity schemes, respectively, taking into account the joint effects of turbulence strengths, pointing error and path loss. To choose the optimal adaptive switching strategy for the system in different signal-to-noise ratio (SNR) ranges, a lookup table for the adaptive switching statistical SNR threshold was created based on the BER curve corresponding to the minimal Euclidean distance. The results show that compared with fixed spatial mode systems, adaptive spatial mode switching systems can achieve significant BER performance gains. The Demmel condition number of matrix channels is considered to provide a sufficient condition for spatial multiplexing to be superior to spatial diversity.

Fast inspirals and the treatment of orbital resonances

Abstract Extreme mass ratio inspirals (EMRIs), where a compact object orbits a massive black hole, are a key source of gravitational waves for the future Laser Interferometer Space Antenna (LISA). Due to their small mass ratio, ε ~ 10-4 -- 10-7 , the binary evolves slowly and EMRI signals will be in-band for years. Additionally, astrophysical EMRIs are expected to have complex dynamics featuring both spin-precession and eccentricity. A standard approach to modelling these inspirals is via the method of osculating geodesics (OG) which we employ along with a toy model for the gravitational self-force. Using this method requires resolving tens of thousands radial and polar orbital librations over the long duration of the signal which makes the inspiral trajectory expensive to compute.In this work we accelerate these calculations by employing Near-Identity (averaging) Transformations. 
However, this averaging technique breaks down at orbital resonances where the radial and polar frequencies are an integer ratio of each other. Thus, we switch to a partial averaging transformation in the vicinity of the resonance where the dynamics are characterised by the slow evolution of the so-called ``resonant phase". Additionally, we develop an optimal switching criterion to minimise the computation time while maximising accuracy. We find the error in the waveform phase is improved from O(ε-1/2) in the fully averaged scheme to O(ε4/7) in the switching scheme. At the same time, this scheme improves the scaling of the computation time from being inversely proportional to ε using OG, to a very weak scaling with ε. This results in a speed-up of at least two orders of magnitude for LISA EMRIs with room for further optimisation.

A phase-field model for study of ferroelastic deformation behavior in yttria stabilized zirconia

A modified phase-field model coupled with linear elasticity is formulated to study the effect of ferroelastic domain switching on the deformation behavior of tetragonal-prime yttria-stabilized zirconia. Simulations are performed under uniaxial tension and compression using realistic domain microstructures. This work provides new insights into the mechanism of ferroelastic deformation by studying the evolution of domains under different loading directions and strain rates. The model predicts realistic stress-strain curves consisting of stress-plateaus with accurate domain switching behavior without setting a switching criterion based on a critical stress for nucleation of new domains. The results reveal that the origin of coercive stress lies in the balance between the energy absorbed by the domain switching process and the externally applied energy. Loading in different directions but parallel to the spontaneous strain results in different levels of coercive stress as domain switching absorbs different amounts of energy. For the same reason when the strain-rate increases, the coercive stress also increases. This work establishes the relation between the coercive stress and microstructural changes during ferroelastic domain switching.

Research on linear/nonlinear active disturbance rejection switching control of PMSM speed servo system

An improved linear/nonlinear active disturbance rejection switching control strategy is proposed to enhance the dynamic response and disturbance suppression capabilities of the permanent magnet synchronous motor (PMSM) speed servo system. Firstly, facilitated by a tracking differentiator, rapid setpoint tracking is achieved without speed overshoot, effectively addressing the inherent conflict between fast system response and overshooting. Secondly, weight factors are introduced into the control law, and a novel switching criterion based on the step function is designed, enabling the system to smoothly transition between operating modes in the presence of disturbances with varying amplitudes. This facilitates accurate estimation and swift compensation for disturbances, thereby enhancing control precision and robustness. Additionally, a frequency domain analysis of the extended state observer is conducted, leading to the formulation of a parameter tuning scheme for the controller. Lastly, simulation tests validate the feasibility and effectiveness of this strategy as a PMSM speed controller. The system exhibits superior dynamic tracking and disturbance suppression performance over a wide speed range.

A small mobile communication system for communication in remote areas

In the age of the Internet, mobile communications have become an indispensable part of our everyday lives, let us connect with other people, get information, and participate in various activities but the communication in remote areas is still difficult due to terrain constraints and other factors. Although communication in remote areas is very important and challenging, there are also many measures and real cases exist. The article describes a small mobile communication system that can be used in remote areas and analyses its characteristics. First, the coverage area is hexagonal base stations, and I draw the distribution map of base stations and users, and then design a switching criterion to determine when a user changes the service base station during the movement. Second, the distribution is compared with the distribution of different types of base stations to study the difference in their system capacity and the effect of the number of antennas on them. to determine who is more efficient.

Event-Triggered Adaptive Antidisturbance Switching Control for Switched Systems With Dynamic Neural Network Disturbance Modeling.

In this article, a dynamic event-triggered adaptive antidisturbance (ETAAD) switching control strategy is proposed for switched systems subject to multisource disturbances. The disturbances are divided into two categories: the available unmodeled disturbance and the unavailable dynamic neural network modeled disturbance. First, a dynamic ET criterion is set based on the system state. Then, a novel dynamic ETA disturbance estimator is introduced to observe the modeled disturbance. Furthermore, according to the ET rule and adaptive disturbance observer, a switched controller is designed. Next, under the controller and switching criterion with the average dwell time limitation, sufficient conditions are given to force the switched systems to realize multisource disturbance suppression (DS), trajectory tracking, and communication resource (CR) saving simultaneously. Meanwhile, the Zeno phenomenon may be caused by the ET rule being excluded. In addition, the presented ETAAD approach is also applicable to the nonswitched systems case. Finally, a simulation case is given to validate the effectiveness of the dynamic ETAAD switching control method.

Coordinated Control for Islanded DC Microgrid Based on Derivative of Bus Voltage

The unbalanced power generated by unintentional islanding is a tremendous challenge to the stable operation of the DC microgrid. When dealing with this challenge, the existing communication-free voltage-threshold-based coordinated control lags far behind the actual power variation, which exacerbates the risk of voltage violations in the microgrid. To address the drawback of the existing control, this paper proposes a new method based on the derivative of bus voltage. Firstly, based on the mathematical correlation between unbalanced power and the derivative of bus voltage, a coordination control strategy and its switching criterion before the battery energy storage system (BESS) exits operation is proposed. Meanwhile, we propose a communication-free approach to sense the operation state of the BESS based on the dynamic process of the derivative of bus voltage. Thus the information obtained under complicated circumstances could be further enriched. Subsequently, a new control strategy after BESS operates operation is established to coordinate different sources among the microgrid. Compared with the existing coordinated control methods, the proposed one can better utilize the information contained in the dynamic process of the bus voltage. Numerical results validate the effectiveness and advantages of the proposed method.

A New Adaptive Implicit Method for Multicomponent Surfactant-Polymer Flooding Reservoir Simulation

Summary In the oil industry, chemicals can improve oil production by mobilizing trapped and bypassed oil. Such processes are known as chemical-enhanced oil recovery (CEOR). Surfactants and polymers are important chemicals used in CEOR with different mechanisms to improve oil recoveries, such as reduction in residual saturation, oil solubilization, and mobility control. However, both surfactant and polymer may increase the cost of oil production, making optimizing these processes essential. Reservoir simulators are tools commonly used when performing such field optimization. The simulation of surfactant flooding processes has been historically performed with the implicit pressure explicit composition (IMPEC) approach. The injection of surfactants requires modeling the brine/oil/microemulsion phase behavior along with other processes, such as capillary desaturation and retention. The microemulsion phase behavior and the complex relative permeability behavior can lead to convergence issues when using fully implicit (FI) schemes. Only recently, the FI approach has been efficiently applied to simulate this process using new modeling. The adaptive implicit method (AIM) can combine the benefits of the FI and IMPEC approaches by dynamically selecting the implicitness level of gridblocks in the domain. This work presents a new AIM in conjunction with recently developed models to mitigate discontinuities in the microemulsion relative permeabilities and phase behavior. The approach presented here considers the stability analysis method as a switching criterion between IMPEC and FI. To the best of our knowledge, the approach presented here is the first AIM to consider the brine/oil/microemulsion three-phase flow in its conception. The new approach uses the finite volume method in conjunction with Cartesian grids as spatial discretization and is applied here for field-scale problems. The new approach is tested for polymer flooding and surfactant-polymer (SP) flooding for problems with several active cells ranging from about a hundred thousand to almost a million. The AIM approach was compared with the FI and IMPEC approaches and displayed little variation in the computational performance despite changes in the timestep size. The AIM also obtained the fastest performance for all cases, especially for SP flooding cases. Furthermore, the results here suggest that the gap in performance between the AIM and FI seems to increase as the number of gridblocks increases.

A Re-detection Target Tracking Framework after Loss for Unmanned Aerial Vehicle

Currently, the target tracking algorithm based on discriminative correlation filter (DCF) and deep learning (DL) plays an increasingly important role in UAV (unmanned aerial vehicle). However, existing algorithms have limitations such as limited search region, difficulty in re-capturing targets when tracking is lost, and high computational complexity, which makes them difficult to apply to the UAV platform. In this paper, a target re-detection tracking algorithm (RDT) after tracking loss is designed for mobile platforms. RDT uses DCF in simple scenes to track targets and establish a target model between different frames. To ensure the algorithm when tracking is lost due to occlusion or moving out of view, an efficient switching criterion is designed to indirectly invoke the detection algorithm to re-capture the target and guide online learning of DCF. And the experiment results on the OTB benchmark show that RDT can re-capture the target after tracking is lost, and the speed of operation on the CPU is 85fps.

Combined Kalman and sliding innovation filtering: An adaptive estimation strategy

This paper proposes a new adaptive estimation strategy for a nonlinear system with modeling uncertainties. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) are optimal estimators which have been used extensively for state estimation in literature and industry. While the EKF uses a first order Taylor series expansion to approximate nonlinearities, the UKF uses sigma points from the projected probability distribution of states. The sliding innovation filter (SIF) is a suboptimal, yet robust estimation strategy which has recently been proposed. For nonlinear systems, the extended SIF (ESIF) is formulated by using a first order Taylor series expansion like the EKF. This work proposes a novel adaptive estimation strategy which combines and balances the optimality of the EKF and UKF with the robustness of the ESIF. These new methods are referred to as the EKF-ESIF and UKF-ESIF, respectively. A time-varying sliding boundary layer is used as a means of detecting the presence of faults or uncertainties and as a criterion for switching between the EKF or UKF and the ESIF. In normal operating conditions the algorithm computes estimates using an optimal KF-based gain, and an SIF-based gain when a fault is detected. The system examined in this study consists of a magnetorheological (MR) damper with a constant current. Faults or uncertainties are introduced as unwanted behavior in the power supply in the form of undercurrent and overcurrent. The robustness of the EKF-ESIF and UKF-ESIF was validated for force estimation exerted by the MR damper and the results were compared with the standard EKF and UKF.

Event-Triggered-Based Antidisturbance Switching Control for Switched T–S Fuzzy Systems

This investigation proposes an event-triggered-based anti-disturbance switching control technique for the switched Takagi-Sugeno (T-S) fuzzy systems (FSs) subject to multiple disturbances as well as unavailable system state. The disturbances are comprised of two parts, i.e., the unavailable modeled disturbances and the available unmodeled disturbances. First, a composite observer is constructed to capture the unavailable system state and unavailable modeled disturbances. Then, on the basis of the observer, a switching controller with an eventtriggered program inserted is established. Meanwhile, a switching criterion is formulated. Further, under the developed controller and switching criterion, the sufficient conditions are presented for the switched T-S FSs to achieve the multiple disturbance suppression and the communication transmission resource saving. In the end, the reasonability of the raised event-triggered-based anti-disturbance switching control scheme is verified with the employed simulation example.

Position-Based Multipath Route Switching Protocol for Intelligent VANETs Using Wiedemann Car-Following Model

The high dynamicity in VANET topology increases the possibility of frequently broken links and degraded the type of service provided to its customers (i.e., comfort and safety services) that may lead into a catastrophe. In this paper, a Quality-of-Service (QoS)-aware position-based multipath route switching protocol for VANETs-based intelligent transportation systems is proposed. The proposed protocol adopts one of three main route switching criteria: Least Connection Delay (LDD), least number of path-switching (LPS), and the least number of path-switching with the minimum delay (LPSMD). The proposed protocol finds multiple paths between the source and the destination and predicts the future connectivity of each path using the Wiedemann Car-Following Model (Wiedemann and Reiter-99). According to the requested QoS and the route switching criterion adopted, the route switching protocol dynamically switches between the pre-defined multiple paths such that the Network Performance Metrics (NPMs) are guaranteed for the entire VANET. Using different network parameters such as vehicular node density, vehicular node speed, and the time-period for collecting vehicle information, extensive simulation results show that the proposed LDD-based route switching protocol outperforms the other route switching criteria in guaranteeing the studied NPMs (i.e., 1) Packet delivery ratio with a 3.6% enhancement over LPSMD and 8.6% enhancement over LPS; 2) Average end-to-end total packet delay with a 4.2% enhancement over LPSMD and 3.4% enhancement over LPS).

Vision Assisted Control of Lower Extremity Exoskeleton for Obstacle Avoidance With Dynamic Constraint Based Piecewise Nonlinear MPC

This article proposed a humanoid obstacle passability strategy (OPS) to enhance human-exoskeleton integrated system to cross over multi-obstacle in sagittal plane. A hybrid bounding box integrated with closeness regression in L-shape section and synchronous convergence in convex hull search (HBB-LC) is designed to recognize geometric features of staggered obstacles with partial surface occluded. A human-in-the-loop gait pattern planning mechanism based on nonlinear model predictive control (NMPC) is designed for decoupling mapping. To overcome local oscillation and achieve humanoid behavior transition during NMPC optimization, an intermediate switching criterion based on instantaneous capture point (ICP) transfer principle and guiding interpolation inflection is established to trigger the external intervention of piecewise NMPC models. Exploitation of OPS achieves the contributions below: 1) Sectional attitude and dimension features of spatial multi-obstacle are accurately extracted, with size deviation within 3.4–16.1 mm, recognition accuracy within 89.2%–96.8%. 2) Traversal avoidance region of swing toe/heel orbit (SHO/STO) is enhanced by 28.7%–86.8% (with uplift/sidesway range (UR/SR) by 0.071–0.154 m/0.057–0.092 m). 3) Security margin between STO/SHO and obstacle vertex is improved by 0.05–0.19 m (53.3%–161.1%). 4) Elapsed time of crossing obstacles is reduced by 0.62–1.03 s (with gait phase by 36.2%–43.5%). Experimental verification proves significant augmentation for crossing obstacles by OPS.

Semi-active switching vibration control with tree-based prediction and optimization strategy

A novel control strategy that combines model predictive control (MPC) with semi-active vibration control that uses a highly effective, energy-efficient, and stable piezoelectric transducer is proposed in this paper. Incorporating MPC into semi-active vibration control enables significant improvements in control performance and robustness. However, it is challenging to directly predict and optimize the input trajectory because the semi-active input has a state-dependent discontinuous nature. To realize effective optimal control, we need a strategy that can predict the discontinuous semi-active input trajectory in a reasonable manner and is computationally cost-efficient. The proposed method employs a prediction algorithm based on a tree data structure. The proposed algorithm achieves flexible prediction and optimization of a semi-active input trajectory with a simple tree traversal. In addition, the proposed method employs a switching criterion to minimize the computational cost and implement fast prediction and optimization. The proposed method is called predictive switching vibration control with tree-based formulation and optimization, or the PSTFO method. The simulation proved that the proposed PSTFO method can predict discontinuous semi-active input and realizes optimal vibration control performance and high robustness. In addition, the high control performance and robustness of the proposed method were experimentally validated.

An LES-Like Multiscale Multiphase Flow Model Based on Break-up and Coalescence Phenomena

The multiscale multiphase flow contains both small-scale (dispersed phase) and large-scale (continuous phase) structures. Standard interface-averaging multiphase models are appropriate for the simulation of flows including small-scale structures. Standard interface-resolving multiphase models are commonly used for the simulation of flow regimes containing large-scale structures. The accurate simulation of different regimes has a crucial role to investigate the physics of multiphase flows. To cover the inability of standard models to simulate multiscale multiphase flows, various generalized hybrid models have been developed. The present research aims to present an LES-like approach to identify the large-scale structures by comparing the equivalent diameter of structures and the averaging length scale. The main difference between the presented model and the models available in the literature is the independency of the model to the thresholds of the local volume fraction to recognize the flow regime. The switching criterion is set based on the cell size and the physical phenomena including the break-up and coalescence mechanisms. To assess the capabilities of the presented multiscale model, four different benchmark cases including the bubble column, the impinging jet, the dam break, and the Rayleigh-Taylor instability are investigated. The physical behavior of the flow is considered as a reference and compared with numerical results. It is demonstrated that the present multifluid model is capable to capture the physical characteristics of both dispersed and segregated flow regimes, and it is a forward step to develop a generalized multiscale hybrid multiphase model.

Dynamic simulation model with virtual interfaces of supercritical working fluid heat exchanger based on moving boundary method

The supercritical cycle has a better variable-temperature match between the working fluid and heat source, potentially providing higher efficiency. Accurate simulation of the transient performance of supercritical heat exchangers is significant in advanced thermal system control. This paper proposes a dynamic model of supercritical heat exchangers based on the moving boundary method. The model redefines the concept of “interface” in the traditional moving boundary model. The new interfaces are determined by the thermophysical properties of the working fluid, called “virtual interfaces”. A control-volume separation method is proposed based on the virtual interface. Additionally, the specific enthalpy is selected as the switching criterion for the configuration of the control volumes, so the model can dynamically adjust the number of control volumes. The integrity check results show that the simulation accuracy is at least equivalent to the finite volume model with 20 control volumes; however, the time cost is approximately 1/5 of that of the finite volume model with the same precision. An experimental study on the convective heat transfer of supercritical n-decane in an electrically heated vertical tube was conducted. The maximum absolute error of the steady-state outlet temperature is ±1.5 K, and the relative error is ±3% under different pressures.

Switching Control of Nonlinear Systems Based on the Quasi-ARX Model and the SVR Algorithm

A nonlinear switching control method was proposed based on an improved quasilinear autoregressive with exogenous inputs (quasi-ARX) radial basis function (RBF) network model and the support vector regression (SVR) algorithm. The RBF network was chosen as the nonlinear part of the improved quasi-ARX prediction model. The proposed controller design method was divided into 3 steps: firstly, the nonlinear parameters of the model were determined with the clustering method; secondly, the linear SVR algorithm was used to solve the robustness problem of the control system; thirdly, the switching criterion function was given based on the control error, and the control sequence were determined according to the switching law. Finally, a numerical example was given to verify the effectiveness of the proposed method.

Simulation analysis on operation performance of a hybrid heat pump system integrating photovoltaic/thermal and air source

• The evaporator model considering dynamic mass flow distribution is established. • The γ for switching between the solar-air mode and the solar mode is about 0.75. • The system performance is less affected by a single environmental factor. • The COP can reach 4.76 and the electric efficiency is 13.5% at 10 °C and 400 W/m 2 . To solve the instability of conventional single source heat pump and further improve energy efficiency, this paper presented a hybrid solar/air dual source assisted heat pump (S-A-AHP) water heater. Based on the system, a quasi-steady-state mathematical model was constructed to study the mode switching criterion and operation performance. The simulation results showed that it is more suitable to use the mass flow ratio of refrigerating medium (i.e., the ratio of the photovoltaic/thermal collector/evaporator to the whole system) as the criterion of system mode switching than directly using environmental factors, and the critical ratio for switching between the solar-air mode and the solar mode is about 0.75. In addition, compared with the conventional solar assisted heat pump (SAHP) and air source heat pump (ASHP), under the low environmental benefit conditions of ambient temperature of 0℃ and solar radiation intensity of 100 W/m 2 , the average COP of S-A-AHP is 29.7% and 19.9% higher than that of SAHP and ASHP, respectively. And the S-A-AHP system's heating time is only 36.7% of the SAHP and 83.7% of the ASHP. And the impact of collector area on system performance was also discussed.

Predictive Semi-active Vibration Control with the Tree-based Formulation and Optimization Algorithm

This paper introduces a novel strategy that combines model predictive control (MPC) and semi-active vibration control with a piezoelectric transducer. Semi-active vibration control is an energy-efficient method that realizes effective suppression performance and stability. Formulating an effective control strategy for the semi-active vibration control further improves its suppression performance, and incorporating MPC is a promising solution. Since the semi-active input changes discontinuously as the switching of the control circuit, it is challenging to apply the commonly-used MPC strategy for prediction and optimization. The proposed method constructs a future prediction and optimization algorithm based on the semi-active input by using a tree data structure. The algorithm achieves flexible prediction and optimization of a semi-active input trajectory directly with a simple tree traversal. Furthermore, the switching criterion introduced in our method reduces the computational cost by only performing future predictions at which a promising performance is expected. The proposed predictive switching vibration control with tree-based formulation and optimization or PSTFO method achieves a direct and flexible optimization of the semi-active input trajectory while reducing the computational burden. The control performance of the conventional and proposed method was compared with the experiment, validating the effectiveness of the proposed method.

Receiver Position Estimation Method for Multitransmitter WPT System Based on Machine Learning

This article proposes a receiver position estimation method suitable for multitransmitter(multi-TX)wireless power transfer systems from a new perspective of machine learning. The novel method uses the voltage and current of the transmitter side, and is realized through self-learning, which can solve the problems of complex electromagnetic environment and slow response in the traditional method. First, the switching criterion of the multi-TX wireless power transfer (WPT) system based on receiver position is established. In addition, the receiver position estimation method is determined by comparing the estimation accuracy of the back propagation (BP) and support vector regression (SVR) algorithms. On this basis, a swithcing control strategy of multi-TX WPT system based on receiver position estimation is further proposed. Finally, the experimental prototype with FPGA controller is designed to verify the correctness of the theoretical analysis. The average relative error of the position estimation method is about 5%, and the system efficiency is stable near 90% with the misalignment conditions.