Research on Mixed Logic Dynamic Modeling and Finite Control Set Model Predictive Control of Multi-Inverter Parallel System

Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.

Modeling and Control of a Continuous Stirred Tank Reactor Based on a Mixed Logical Dynamical Model

A novel control strategy for a continuous stirred tank reactor(CSTR)system,which has the typical characteristic of strongly pronounced nonlinearity,multiple operating points,and a wide operating range,is initiated from the point of hybrid systems.The proposed scheme makes full use of the modeling power of mixed logical dy- namical(MLD)systems to describe the highly nonlinear dynamics and multiple operating points in a unified framework as a hybrid system,and takes advantage of the good control quality of model predictive control(MPC) to design a controller.Thus,this approach avoids oscillation during switching between sub-systems,helps to relieve shaking in transition,and augments the stability robustness of the whole system,and finally achieves optimal(i.e. fast and smooth)transition between operating points.The simulation results demonstrate that the presented ap- proach has a satisfactory performance.

Vehicle height control of electronic air suspension system based on mixed logical dynamical modelling

Due to the coexistence and coupling of continuous variables and discrete events, the vehicle height adjustment process of electronic air suspension system can be regarded as a typical hybrid system. Therefore, the hybrid system theory was applied to design a novel vehicle height control strategy in this paper. A nonlinear mechanism model of the vehicle height adjustment system was established based on vehicle system dynamics and thermodynamic theory for variable-mass gas charge/discharge system. In order to model both the continuous/discrete dynamics of vehicle height adjustment process and the on-off statuses switching of solenoid valves, the framework of mixed logical dynamical(MLD) modelling was used. On the basis of the vehicle height adjustment control strategy, the MLD model of the adjustment process was built by introducing auxiliary logical variables and auxiliary continuous variables. Then, the co-simulation of the nonlinear mechanism model and the MLD model was conducted based on the compiling of HYSDEL. The simulation and experimental results show that the proposed control strategy can not only adjust the vehicle height effectively, but also achieve the on-off statuses direct control of solenoid valves.

Vehicle height and leveling control of electronically controlled air suspension using mixed logical dynamical approach

Vehicle height and leveling control of electronically controlled air suspension(ECAS) still poses theoretical challenges for researchers that have not been adequately addressed in prior research. This paper investigates the design and verification of a new controller to adjust the vehicle height and to regulate the roll and pitch angles of the vehicle body(leveling control) during the height adjustment procedures. A nonlinear mechanism model of the vehicle height adjustment system is formulated to describe the dynamic behaviors of the system. By using mixed logical dynamical(MLD) approach, a novel control strategy is proposed to adjust the vehicle height by controlling the on-off statuses of the solenoid valves directly. On this basis, a correction algorithm is also designed to regulate the durations of the on-off statuses of the solenoid valves based on pulse width modulated(PWM) technology, thus the effective leveling control of the vehicle body can be guaranteed. Finally, simulations and vehicle tests results are presented to demonstrate the effectiveness and applicability of the proposed control methodology.

MLD-MPC Approach for Three-Tank Hybrid Benchmark Problem

The present paper aims at validating a Model Predictive Control(MPC),based on the Mixed Logical Dynamical(MLD)model,for Hybrid Dynamic Systems(HDSs)that explicitly involve continuous dynamics and discrete events.The proposed benchmark system is a three-tank process,which is a typical case study of HDSs.The MLD-MPC controller is applied to the level control of the considered tank system.The study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating constraints.This feature of MLD modeling is very advantageous when an MPC controller synthesis for the HDSs is designed.Once the MLD model of the system is well-posed,then the MPC law synthesis can be developed based on the Mixed Integer Programming(MIP)optimization problem.For solving this MIP problem,a Branch and Bound(B&B)algorithm is proposed to determine the optimal control inputs.Then,a comparative study is carried out to illustrate the effectiveness of the proposed hybrid controller for the HDSs compared to the standard MPC approach.Performances results show that the MLD-MPC approach outperforms the standardMPCone that doesn’t consider the hybrid aspect of the system.The paper also shows a behavioral test of the MLDMPC controller against disturbances deemed as liquid leaks from the system.The results are very satisfactory and show that the tracking error is minimal less than 0.1%in nominal conditions and less than 0.6%in the presence of disturbances.Such results confirm the success of the MLD-MPC approach for the control of the HDSs.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Constraints Adjustment and Objectives Coordination of Satisfying Optimal Control Applied to Heavy Oil Fractionators

In this paper, the feasibility and objectives coordination of real-time optimization (RTO) are systemically investigated under soft constraints. The reason for requiring soft constraints adjustment and objective relaxation simultaneously is that the result is not satisfactory when the feasible region is apart from the desired working point or the optimization problem is infeasible. The mixed logic method is introduced to describe the priority of the constraints and objectives, thereby the soft constraints adjustment and objectives coordination are solved together in RTO. A case study on the Shell heavy oil fractionators benchmark problem illustrating the method is finally presented.

A Hybrid Model Predictive Control for Handling Infeasibility and Constraint Prioritization

A hybrid approach using MLD (mixed logical dynamical) framework to handle infeasibility and constraint prioritization issues in MPC (model predictive control) based on input-output model is introduced. By expressing constraint priorities as propositional logics and by transforming the propositional logics into inequalities,the infeasibility and constraint prioritization issues are solved in the MPC. Constraints with higher priorities are met first, and then these with lower priorities are satisfied as much as possible. This new approach is illustrated in the control of a heavy oil fractionator-Shell column. The overall control performance has been significantly improved through the infeasibility and control priorities handling.

State Feedback Predictive Control for Hybrid System via Parametric Optimization

In this article, state feedback predictive controller for hybrid system via parametric programming is proposed. First, mixed logic dynamic （MLD） modeling mechanism for hybrid system is analyzed, which has a distinguished advantage to deal with the logic rules and constraints of a plant. Model predictive control algorithm with moving horizon state estimator （MHE） is presented. The estimator is adopted to estimate the current state of the plant with process disturbance and measurement noise, and the state estimated are utilized in the predictive controller for both regulation and tracking problems of the hybrid system based on MLD model. Off-line parametric programming is adopted and then on-line mixed integer programming problem can be treated as the parameter programming with estimated state as the parameters. A three tank system is used for computer simulation, results show that the proposed MHE based predictive control via parametric programming is effective for hybrid system with model/olant mismatch, and has a potential for the engineering applications.

Multi-model predictive control with local constraints based on model switching

Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.

Failure-tolerant control for small agile satellites using single-gimbal control moment gyros and magnetic torquers

This paper focuses on the attitude control prob- lem of small agile satellites using single-gimbal control mo- ment gyros （CMG） and magnetic torquers （MTQ）. CMGs are regarded as effective torque generators for agile satel- lites because of their torque amplification capability. How- ever, they are vulnerable to failure due to their complex inner mechanism. In this paper, different failure cases of CMGs are analyzed. A flexible failure-tolerant control strategy is developed by automatically redistributing the required con- trol torque among the operating CMGs and MTQs, with a variable limiter to accommodate the actuator dynamics changes introduced by CMG failures. The performances of maneuvers about different directions under different failure cases are also discussed and examined. Numerical simula- tions demonstrate that the proposed strategy maintains cer- tain agility in the cases of one or two CMGs failing. More- over, a survival strategy with only one CMG left is also ver- ified. Both sun-pointing stabilization and earth-pointing sta- bilization can be achieved in this case, which fulfill some basic mission requirements.

Canonical logic units using bidirectional four-wave mixing in highly nonlinear fiber

All-optical canonical logic units at 40 Gb/s using bidirectional four-wave mixing(FWM) in highly nonlinear fiber are proposed and experimentally demonstrated. Clear temporal waveforms and correct pattern streams are successfully observed in the experiment. This scheme can reduce the amount of nonlinear devices and enlarge the computing capacity compared with general ones. The numerical simulations are made to analyze the relationship between the FWM efficiency and the position of two interactional signals.

Investigation on hierarchical control for driving stability and safety of intelligent HEV during car-following and lane-change process

The longitudinal and lateral coordinated control for autonomous vehicles is fundamental to achieve safe and comfortable driving performance.Aiming at this for hybrid electric vehicles(HEV)during the car-following(CF)and lane-change(LC)process while accelerating,a hierarchical control strategy for vehicle stability control is proposed.This new approach is different from the conventional hierarchical control.On the basis of model predictive control(MPC)theory,a two-layer MPC controller is designed at the top level of the control structure.The upper layer is a linear time-varying MPC(LTV-MPC),while the lower layer is a hybrid MPC(HMPC).For the LTV-MPC controller,a control-oriented linear discrete model for HEV is established,which integrates the dynamic model with three degrees of freedom(DOF)and the car-following model.The lower-layer HMPC controller is designed on the basis of the analysis for HEV hybrid characteristics and the modelling for the mixed logic dynamic(MLD)model of the HEV powertrain.As for the bottom level,a control plant including the HEV powertrain model and the 7 DOF nonlinear dynamics of the vehicle body is established.In addition,the system stability is proven.A deep fusion of vehicle dynamics control and energy management is achieved.Compared with LC-ACC control and conventional ACC control,the simulation and the hardware-in-the-loop(HIL)test results under different driving scenarios show that the proposed hierarchical control strategy can effectively maintain lateral stability and safety under severe driving conditions.Additionally,the HEV powertrain output torque and the gear-shift point are coordinated and controlled by the HMPC controller.

Hybrid model predictive control for speed control of permanent magnet synchronous motor with saturation

A discrete-time hybrid model of a permanent magnet synchronous motor (PMSM) with saturation in voltage and current is formulated.The controller design with incorporated constraints is achieved in a systematic way from modeling to control synthesis and implementation.The Hybrid System Description Language is used to obtain a mixed-logical dynamical (MLD) model.Based on the MLD model,a model predictive controller is designed for an optimal speed regulation of the motor.For reducing computation complexity and computation time,the MPC controller is converted to its equivalent explicit piecewise affine form by multiparametric programming.Simulations and experiments show that good and robust control performance is achieved by the hybrid model predictive controller as compared with the linear quadratic regulator (LQR) and the PID controller.