Optimal observation configuration of UAVs based on angle and range measurements and cooperative target tracking in three-dimensional space

This article investigates the optimal observation configuration of unmanned aerial vehicles(UAVs) based on angle and range measurements, and generalizes predecessors' researches in two dimensions into three dimensions. The relative geometry of the UAVs-target will significantly affect the state estimation performance of the target, the cost function based on the Fisher information matrix(FIM) is used to derive the FIM determinant of UAVs' observation in three-dimensional space, and the optimal observation geometric configuration that maximizes the determinant of the FIM is obtained. It is shown that the optimal observation configuration of the UAVs-target is usually not unique, and the optimal observation configuration is proved for two UAVs and three UAVs in three-dimension. The long-range over-the-horizon target tracking is simulated and analyzed based on the analysis of optimal observation configuration for two UAVs. The simulation results show that the theoretical analysis and control algorithm can effectively improve the positioning accuracy of the target. It can provide a helpful reference for the design of over-the-horizon target localization based on UAVs.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

A modified active disturbance rejection control for a wastewater treatment process

Waste water treatment process(WWTP)control has been attracting more and more attention.However,various undesired factors,such as disturbance,uncertainties,and strong nonlinear couplings,propose big challenges to the control of a WWTP.In order to improve the control performance of the closed-loop system and guarantee the discharge requirements of the effluent quality,rather than take the model dependent control approaches,an active disturbance rejection control(ADRC)is utilized.Based on the control signal and system output,a phase optimized ADRC(POADRC)is designed to control the dissolved oxygen and nitrate concentration in a WWTP.The phase advantage of the phase optimized extended state observer(POESO),convergence of the POESO,and stability of the closed-loop system are analyzed from the theoretical point of view.Finally,a commonly accepted benchmark simulation model no.1.(BSM1)is utilized to test the POESO and POADRC.Linear active disturbance rejection control(LADRC)and the suggested proportion-integration(PI)control are taken to make a comparative research.Both system responses and performance index values confirm the advantage of the POADRC over the LADRC and the suggested PI control.Numerical results show that,as a result of the leading phase of the total disturbance estimation,the POESO based POADRC is an effective and promising way to control the dissolved oxygen and nitrate concentration so as to ensure the effluent quality of a WWTP.

Fault diagnosis using robust cascade observers with application to spacecraft attitude control

This paper proposes a new gyro and star sensor fault diagnosis architecture that designs two groups of cascade H∞ optimal fault observers using LMI for spacecraft attitude control systems.The basic idea of the approach is to identify the gyro fault to good effect first and then makes a further diagnosis for the star sensor based on the former.The H∞ optimal fault observer in design has the robustness with respect to model uncertainties and diagnosis uncertainties.Its robustness to unknown inputs is as a special study in frequency domain.Finally,simulation results demonstrate the effectiveness and feasibility of the proposed control algorithm.

DESIGN OF DISTURBANCE DECOUPLED FILTER AND ITSAPPLICATION TO MANEUVERING TARGETS TRACKING

A novel disturbance decoupled filter (DDF) design scheme is presented. Firstly, the system with unknown input is translated into an equivalent system without unknown imputs by a simple algebraic transformation. Then, a new DDF design scheme, which is very simple, is proposed via innovations theorem. At last, the application of DDF to Maneuvering Targets Tracking is simulated and the simulation results show that DDF is suitable for high maneuvering cases.

MAC Layer Resource Allocation for Wireless Body Area Networks

Wireless body area networks （WBANs） can provide low-cost, timely healthcare services and are expected to be widely used for e-healthcare in hospitals. In a hospital, space is often limited and multiple WBANs have to coexist in an area and share the same channel in order to provide healthcare services to different patients. This causes severe interference between WBANs that could significantly reduce the network throughput and increase the amount of power consumed by sensors placed on the body. There-fore, an efficient channel-resource allocation scheme in the medium access control （MAC） layer is crucial. In this paper, we devel-op a centralized MAC layer resource allocation scheme for a WBAN. We focus on mitigating the interference between WBANs and reducing the power consumed by sensors. Channel and buffer state are reported by smartphones deployed in each WBAN, and channel access allocation is performed by a central controller to maximize network throughput. Sensors have strict limitations in terms of energy consumption and computing capability and cannot provide all the necessary information for channel allocation in a timely manner. This deteriorates network performance. We exploit the temporal correlation of the body area channel in order to minimize the number of channel state reports necessary. We view the network design as a partly observable optimization prob-lem and develop a myopic policy, which we then simulate in Matlab.

Signifying quantum uncertainty relations by optimal observable sets and the tightest uncertainty constants

Quantum uncertainty relations constrain the precision of measurements across multiple non-commuting quantum mechanical observables.Here,we introduce the concept of optimal observable sets and define the tightest uncertainty constants to accurately describe these measurement uncertainties.For any quantum state,we establish optimal sets of three observables for both product and summation forms of uncertainty relations,and analytically derive the corresponding tightest uncertainty constants.We demonstrate that the optimality of these sets remains consistent regardless of the uncertainty relation form.Furthermore,the existence of the tightest constants excludes the validity of standard real quantum mechanics,underscoring the essential role of complex numbers in this field.Additionally,our findings resolve the conjecture posed in[Phys.Rev.Lett.118,180402(2017)],offering novel insights and potential applications in understanding preparation uncertainties.

THE MAXIMUM PRINCIPLE FOR PARTIALLY OBSERVED OPTIMAL CONTROL OF FORWARD-BACKWARD STOCHASTIC SYSTEMS WITH RANDOM JUMPS

This paper studies the problem of partially observed optimal control for forward-backward stochastic systems which are driven both by Brownian motions and an independent Poisson random measure. Combining forward-backward stochastic differential equation theory with certain classical convex variational techniques, the necessary maximum principle is proved for the partially observed optimal control, where the control domain is a nonempty convex set. Under certain convexity assumptions, the author also gives the sufficient conditions of an optimal control for the aforementioned optimal optimal problem. To illustrate the theoretical result, the author also works out an example of partial information linear-quadratic optimal control, and finds an explicit expression of the corresponding optimal control by applying the necessary and sufficient maximum principle.

Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model

Using the conditional nonlinear optimal perturbation(CNOP) approach, sensitive areas of adaptive observation for predicting the seasonal reduction of the upstream Kuroshio transport(UKT) were investigated in the Regional Ocean Modeling System(ROMS). The vertically integrated energy scheme was utilized to identify sensitive areas based on two factors: the specific energy scheme and sensitive area size. Totally 27 sensitive areas, characterized by three energy schemes and nine sensitive area sizes, were evaluated. The results show that the total energy(TE) scheme was the most effective because it includes both the kinetic and potential components of CNOP. Generally, larger sensitive areas led to better predictions. The size of 0.5% of the model domain was chosen after balancing the effectiveness and efficiency of adaptive observation. The optimal sensitive area OSen was determined accordingly. Sensitivity experiments on OSen were then conducted, and the following results were obtained:(1) In OSen, initial errors with CNOP or CNOP-like patterns were more likely to yield worse predictions, and the CNOP pattern was the most unstable.(2) Initial errors in OSen rather than in other regions tended to cause larger prediction errors. Therefore, adaptive observation in OSen can be more beneficial for predicting the seasonal reduction of UKT.

Observation of wave-breaking-free square pulses in a fiber ring laser

We experimentally and numerically demonstrate the generation of square pulses without any wave-breaking in a fiber ring laser. A segment of nonzero dispersion-shifted fiber is used to increase the laser cavity length and to optimize the parameters of the laser cavity. In the experiment, the pulse width can be tuned in a wide range from13.5 to 119.5 ns without wave-breaking while the peak power remains almost constant. The maximum singlepulse energy is up to 65.58 n J at a pump power of 508 m W. Numerical results are in good agreement with the experimental results. Numerical results also reveal the role of cavity length and nonlinearity in generating a square pulse without pulse breakup.

On-line Ladle Lining Temperature Estimation by Using Bounded Jacobian Nonlinear Observer

The knowledge of transient temperature of the ladle wall is a key factor in optimizing energy consumption in steelmaking process.The transient temperature needs to be estimated.A nonlinear lumped parameter model was used to model the thermal dynamics of the ladle.Then,the bounded Jacobian nonlinear observer was utilized to estimate the temperature.With this method,the estimation model became a closed-loop model and the observer gains were obtained by solving linear matrix inequalities and simply implemented to the system.Comparison between the simulation and recorded data at a participating steel plant in Thailand showed that the nonlinear observer accurately estimated the temperature of the ladle lining.This estimated temperature was very useful in determining suitable tapping temperature for energy conservation and steel quality.

Fault detection for a class of Markov jump systems with unknown disturbances

An optimized fault detection observer is designed for a class of Markov jump systems with unknown disturbances.By reconstructing the system,the residual error dynamic characteristics of unknown input and fault signals,including unknown disturbances and modeling error are obtained.The energy norm indexes of disturbance and fault signals of the residual error are selected separately to reflect the restraint of disturbance and the sensitivity of faults,and the design of the fault detection observer is described as an optimization problem.By using the constructed Lyapunov function and linear matrix inequalities,a sufficient condition that the solution to the fault detection observer exists is given and proved,and an optimized design approach is presented.The designed observer makes the systems have stochastic stability and better capability of restraining disturbances,and the given norm index is satisfied.Simulation results demonstrate that the proposed observer can detect the faults sensitively,and the influence of unknown disturbance on residual error can be restrained to a given range.