Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

Optimal state feedback control of brushless direct-current motor drive systems based on Lyapunov stability criterion

This paper develops a unified methodology for a real-time speed control of brushless direct-current motor drive systems in the presence of measurement noise and load torque disturbance. First, the mathematical model and hardware structure of system is established. Next, an optimal state feed back controller using the Kalman filter state estimation technique is derived. This is followed by an adaptive control algorithm to compensate for the effects of noise and disturbance. Those two algorithms working together can provide a very-high-speed regulation and dynamic response over a wide range of operating conditions. Simulated responses are presented to highlight the effectiveness of the proposed control strategy.

Study on optimal state estimation strategy with dual distributed controllers based on Kalman filtering

Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments.

Preparation and control of atomic optimal entropy squeezing states for a moving two-level atom under control of the two-mode squeezing vacuum fields

From the viewpoint of quantum information, this paper studies preparation and control of atomic optimal entropy squeezing states （AOESS） for a moving two-level atom under control of the two-mode squeezing vacuum fields. Necessary conditions of preparation of the AOESS are analysed, and numerical verification of the AOESS is finished. It shows that the AOESS can be prepared by controlling the time of the atom interaction with the field, cutting the entanglement between the atom and field, and adjusting squeezing factor of the field. An atomic optimal entropy squeezing sudden generation in different components can alternately be realized by controlling the field-mode structure parameter.

Preparation of optimal entropy squeezing state of atomic qubit inside the cavity via two-photon process and manipulation of atomic qubit outside the cavity

Considering two atomic qubits initially in Bell states, we send one qubit into a vacuum cavity with two-photon resonance and leave the other one outside. Using quantum information entropy squeezing theory, the time evolutions of the entropy squeezing factor of the atomic qubit inside the cavity are discussed for two cases, i.e., before and after rotation and measurement of the atomic qubit outside the cavity. It is shown that the atomic qubit inside the cavity has no entropy squeezing phenomenon and is always in a decoherent state before the operating atomic qubit outside the cavity. However,the periodical entropy squeezing phenomenon emerges and the optimal entropy squeezing state can be prepared for the atomic qubit inside the cavity by adjusting the rotation angle, choosing the interaction time between the atomic qubit and the cavity, controlling the probability amplitudes of subsystem states. Its physical essence is cutting the entanglement between the atomic qubit and its environment, causing the atomic qubit inside the cavity to change from the initial decoherent state into maximum coherent superposition state, which is a possible way of recovering the coherence of a single atomic qubit in the noise environment.

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.

Stabilization of linear time-varying systems with state and input constraints using convex optimization

The stabilization problem of linear time-varying systems with both state and input constraints is considered. Sufficient conditions for the existence of the solution to this problem are derived and a gain-switched（gain-scheduled） state feedback control scheme is built to stabilize the constrained timevarying system. The design problem is transformed to a series of convex feasibility problems which can be solved efficiently. A design example is given to illustrate the effect of the proposed algorithm.

Optimizing Control of Bio-dissimilation Process of Glycerol to 1,3-Propanedlol

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation （AI- SOPE） technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential ofthe approach presented for the optimizing control of the bioTdissimilation process of glycerol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.

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.

Supercritical carbon dioxide as a new working medium for pneumatic launch:A theoretical study

Compared with the conventionally gaseous or liquid working media,the specific internal energy of supercritical carbon dioxide(SCD)is higher at the same temperature and pressure,and the critical temperature of carbon dioxide is close to room temperature,making SCD a potential new working medium for pneumatic launch.To analyze the feasibility of this conception,an analytical model of a pneumatic catapult is established on basis of the conservations of mass and energy.The model consists of a high-pressure chamber and a low-pressure chamber connected by multiple valves,and there is a movable piston in the low-pressure chamber that can push an aircraft to accelerate.The effects of the launch readiness state of SCD in the high-pressure chamber,the initial volume of the low-pressure chamber and the valve control on the movement of the aircraft are analyzed.It is found that there is a restrictive relation between the temperature and pressure of the launch readiness state of SCD,i.e.,there is a maximum allowable launch readiness pressure when the launch readiness temperature is fixed.If this restrictive relation is not satisfied,the working medium in the low-pressure chamber will drop to its triple point within a few milliseconds,leading to a launch failure.Owing to this restrictive relation,there is an optimal launch readiness state of SCD with the highest working capacity for any allowable launch readiness temperature.The pressure of the low-pressure chamber will decrease significantly as the initial volume increases,leading to a decreased acceleration of the aircraft.The acceleration can be controlled below a critical value by a designed sequential blasting technique of multiple valves.The calculated results show that a 500 kg aircraft can be accelerated from 0 to 58 m/s in 0.9 s with 36 kg of carbon dioxide.This research provides a new technique for the controllable cold launch of an aircraft.

Hierarchical control based on Hopfield network for nonseparable opti mization problems

The nonseparable optimization control problem is considered, where the overall objective function is not of an additive form with respect to subsystems. Since there exists the problem that computation is very slow when using iteratire algorithms in multiobjective optimization, Hopfield optimization hierarchical network based on IPM is presented to overcome such slow computation difficulty. Asymptotic stability of this Hopfield network is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale non.separable steady state systems.

Integrating Mobile Ad Hoc Network to the Internet

A novel scheme is presented to integrate mobile ad hoc networks （MANETs） with the lnternet and support mobility across wireless local area networks （WLANs） and MANETs. The mobile nodes, connected as a MANET, employ the optimize d link state routing （OLSR） protocol for routing within the MANET. Mobility management across WLANs and MANETs is achieved through the hierarchical mobile IPv6 （HMIPv6） protocol. The performance is evaluated on a HMIPv6 based test-bed composed of WLANs and MANETs. The efficiency gain obtained from using HMIPv6 in such a hybrid network is investigated. The investigation result shows that the use of HMIPv6 can achieve up to 27% gain on reducing the handoff latency when a mobile roams within a domain. Concerning the reduction of the signaling load on the lnternet, the use of HMIPv6 can achieve at least a 54% gain and converges to 69%.

LQ Speed Control of Pump Controlled Motor System

A linear quadric (LQ) optimal speed control algorithm is proposed for the speed control of a pump controlled motor hydraulic system. The control theme consists of optimal state feedback and disturbing compensation based on observation. The optimal state feedback bases on LQ cost function. The disturbing compensation is realized through reconstructing the state of load torque. A series of simulation are performed, and the results show that the control performance is satisfactory and can be maintained under changes of load torque.

The state capital investing and operating company pilot reform and SOE bailouts

This paper explores the influence of the State Capital Investing and Operating Company(SCIOC)pilot reform on SOE bailout using a staggered differencein-differences model.Based on a sample of listed SOEs during 2011-2018,we find that when the real controllers of listed SOEs enter the list of SCIOCs,soft budget constraints are alleviated and listed subsidiaries are less likely to become distressed.Mechanism tests indicate that SCIOCs help distressed firms through exiting the market,reducing bank loans,enhancing corporate governance,and improving operating efficiency.Heterogeneity tests show that the effect of SCIOC establishment is more significant in central and western regions,in public welfare and special function industries,for central SCIOCs,for state capital investing companies,when firms are organized in more layers,and for firms that engage in M&As.The empirical results show that the implementation of SCIOCs benefits both micro-enterprise development and state capital layout optimization.

Synergistic effects of 1T MoS_(2)and interface engineering on hollow NiCoP nanorods for enhanced hydrogen evolution activity

Metallic 1T-phase molybdenum disulfide(1T-MoS_(2))shows more excellent electrocatalytic performance for hydrogen evolution reaction(HER)than semiconducting 2H-phase MoS_(2)(2H-MoS_(2)).Therefore,the facile controllable synthesis of hierarchical structure with rich 1T-MoS_(2)is desired for highly efficient electrocatalytic performance.In this work,a simple solvothermal method is proposed to fabricate hol-low NiCoP/MoS_(2)-V heterostructure with 63.2%1T-MoS_(2),in which the abundant catalytic active sites are exposed,the mass transfer properties are improved,and the electronic states are optimized.Moreover,the low energy difference between 2H and 1T phases and near zero free energy of hydrogen adsorption(△G H∗)result in fast kinetics and excellent catalytic performances.Specifically,the NiCoP/MoS_(2)-V com-posite exhibits enhanced HER activity with a low overpotential of 74.6 mV at 10 mA cm^(-2)and superior stability in alkaline electrolytes.This efficient design opens up new vistas for developing high-activity electrocatalysts.

Quantum Parameter Estimation: From Experimental Design to Constructive Algorithm

In this paper we design the following two-step scheme to estimate the model parameter ω0 of the quantum system: first we utilize the Fisher information with respect to an intermediate variable v = cos(ω0 t) to determine an optimal initial state and to seek optimal parameters of the POVM measurement operators; second we explore how to estimate ω0 from v by choosing t when a priori information knowledge of ω0 is available. Our optimal initial state can achieve the maximum quantum Fisher information. The formulation of the optimal time t is obtained and the complete algorithm for parameter estimation is presented. We further explore how the lower bound of the estimation deviation depends on the a priori information of the model.

Energy Management Strategy for Hybrid Electric Vehicle Based on System Efficiency and Battery Life Optimization

A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.