Mixed Logical Dynamical Model Research Articles

MLD Modeling and MPC-Based Energy Management Strategy for Hydrogen Fuel Cell/Supercapacitor Hybrid Electric Vehicles

Energy management strategies for hydrogen fuel cell hybrid electric vehicles (FCHEVs) are a key factor in achieving real-time vehicle energy optimization control, vehicle driving economy, and fuel cell durability. In this paper, for an FCHEV equipped with a fuel cell and supercapacitor, the quantitative information, logic rules, and operational constraints are transformed into linear integer inequalities according to its different operating modes, and the Hysdel language is used to establish its mixed logic dynamic model (MLD). Then, the energy management strategy based on model predictive control (MPC) is developed using the MLD model as the prediction model and the equivalent hydrogen consumption and the performance degradation of the fuel cell as the optimization performance indexes. Finally, under the World Light Vehicle Test Cycle, a joint simulation was carried out with Advisor and Simulink to verify the proposed strategy’s superiority by comparing it with the power following control strategy (PFCS) and the compound fuzzy control strategy (CFCS). The results show that the strategy not only ensures real-time FCHEV energy control, but also reduces hydrogen consumption by 10.98% and 1.98% and the number of start/stop times of a fuel cell by six and four, compared to PFCS and CFCS, respectively, which improves the economy of the whole vehicle as well as the durability of the fuel cell.

Fault‐tolerant control of current residual vector three‐phase four‐switch motor drive system based on MLD model

AbstractAiming at the problem of single‐phase switch tube open circuit fault in inverters, a current residual vector three‐phase four‐switch fault‐tolerant control strategy based on the mixed logic dynamic (MLD) model of permanent magnet synchronous motor (PMSM) drive system is proposed. Firstly, the MLD and three‐phase four‐switch mathematical models of the motor drive system are established and a calculation method of DC bus capacitance is proposed. Secondly, to improve the dynamic performance of the motor, an improved vector control based on the MLD model is proposed. Finally, by setting the threshold and dividing the area of the current residual vector, the accuracy of inverter fault diagnosis is improved, and the fast and smooth switching of the drive system after the fault is realized. Simulation and experimental results show that this strategy can effectively reduce the influence of calculation error and system noise, and has strong robustness for the change of motor speed and load.

Research on MLD Modeling and Predictive Control of Magnetically Coupled Resonant Bidirectional WPT System

The recent studies on magnetically coupled resonant bidirectional wireless power transfer (MCR-BWPT) systems disregard the challenges posed by nonlinearity, discrete switching action, and hybrid properties within the system. This research focuses on the D-LCL resonant compensation topology MCR-BWPT system. The switch tube’s switching state dictates the division of various working modes and the determination of the switching conditions between them. The coupling relationship between the continuous dynamic characteristics of the system and discrete events and the constrained conditions of the system are derived. The Hybrid System Description Language (HYSDEL) is used to build the Mixed Logic Dynamic (MLD) model of the system. The MLD model is employed as the prediction model, and the hybrid model predictive controller of the MCR-BWPT system is constructed according to the quadratic performance index. Finally, to verify the accuracy of the MLD model and the feasibility of the control strategy, the simulation model of the MLD model is established in MATLAB/Simulink. The study’s findings show that, in terms of response time at system startup and power fluctuation suppression, the approach put forward in this research performs better than both the conventional bilateral dual-phase-shift control strategy and the PQ-based bilateral power control strategy. The MCR-BWPT system can operate more steadily now that PQ’s bidirectional power control technique is in place. The system’s forward and reverse transmission efficiency is increased by 0.29% and 0.32% compared to the conventional bilateral dual-phase-shift control approach; the increases are 0.28% and 0.09%, each compared to the bilateral power control strategy based on PQ.

Longitudinal Velocity Regulation of UGVs: A Composite Control Approach for Acceleration and Deceleration

In this paper, a composite longitudinal controller for velocity regulation of unmanned ground vehicles (UGVs) is proposed. First, both the empirical model and mixed logical dynamic model are developed in terms of the experimental data processing method. Second, a composite longitudinal control strategy incorporating steady-state controller and active disturbance rejection state feedback controller is designed for velocity regulation control during acceleration and deceleration. Finally, the stability analysis of velocity error is given with the global sector condition, and the comparative experimental tests show the effectiveness and robustness of the proposed composite longitudinal regulation control strategy.

Model Predictive Path Planning of AGVs: Mixed Logical Dynamical Formulation and Distributed Coordination

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

Practical hybrid model predictive control for electric pneumatic braking system with on-off solenoid valves

Accurate and rapid braking pressure regulation in electric pneumatic braking systems (EPBS) is vital to vehicle safety. Due to the switching behaviors of the on-off solenoid valves, the operation of the EPBS shows a hybrid nature with both continuous variables and discrete events, which raises the hybrid control problem. One of the possible solutions is to employ the hybrid model predictive controller with the mixed logical dynamical (MLD) model based on the linear approximation of the system dynamics. However, the nonlinearity and complexity of the EPBS make the MLD model obtained by linearizing the system equations directly require high storage and computing capacity. To address these issues, this article presents a practical hybrid model predictive controller based on the system dynamics simplified expressions considering the EPBS pressure variations caused by on-off solenoid valve states at the current sampling time and the last sampling time. The relationship between the pressure variations and the on-off solenoid valve states is first studied by the system mathematical model, followed by applying the mixed logic dynamical modeling approach to establish the hybrid model of the pressure continuous dynamics with discrete features of on-off solenoid valves. Based on these, a hybrid model predictive controller is formulated to solve the EPBS pressure control problem. The simulations and bench experiments are carried out to verify the controller. Besides, an existing model predictive control (MPC) controller is compared with the proposed controller. All the results demonstrate the effectiveness of the hybrid model predictive controller.

Piecewise affine model identification and predictive control for ultra-supercritical circulating fluidized bed boiler unit

In this paper, fully considering the form of the nonlinear model after linearization at the steady-state point, an improved subspace identification method based on the steady-state points' deviations data was proposed to identify piecewise affine model. Besides, the construction of the excitation signal in practical applications is fully considered. The identification results demonstrate that the method has better adaptability to strong nonlinear systems. The identification normalized root mean square error of each working condition is almost all less than 10%. On this basis, a framework that is widely applicable to complex system control is established combining with the mixed logic dynamic model. The canonical form realization is performed to transfer the local models into the same state basis. Predictive control was carried out on the boiler-turbine system of the 660-MW ultra-supercritical circulating fluidized bed unit based on the above framework. The results indicate the effectiveness of the framework.

Torsional oscillation suppression-oriented torque compensate control for regenerative braking of electric powertrain based on mixed logic dynamic model

This paper presents a novel torsional oscillation suppression-oriented torque compensate control for regenerative braking (TS-RTC) of electric powertrain, which is based on the gear-pair backlash and half-shaft flexibility-considered dynamic model of regenerative braking in electric powertrain (REP). According to the characteristics of transition from driving mode to regenerative braking mode (DM-to-RM), the dynamic models under different stages are transmitted into an equivalent mixed-logical dynamical (MLD) hybrid model. MLD-based hybrid braking system model and hybrid model predictive control are adopted to design the TS-RTC strategy, in which the torsional oscillation suppression-oriented cost function proved efficient in improving the regenerative braking performance during DM-to-RM process. Simulations and REP hardware in the loop test show that the fluctuation of half shaft torque are efficiently suppressed and the vehicle drivability is improved.

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 standard MPC one that doesn’t consider the hybrid aspect of the system. The paper also shows a behavioral test of the MLD-MPC 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 Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems: Application to Optimized Interventions for Physical Activity.

Hybrid Model Predictive Control (HMPC) is presented as a decision-making tool for novel behavioral interventions to increase physical activity in sedentary adults, such as Just Walk. A broad-based HMPC formulation for mixed logical dynamical (MLD) systems relevant to problems in behavioral medicine is developed and illustrated on a representative participant model arising from the Just Walk study. The MLD model is developed based on the requirement of granting points for meeting daily step goals and categorical input variables. The algorithm features three degrees-of-freedom tuning for setpoint tracking, measured and unmeasured disturbance rejection that facilitates controller robustness; disturbance anticipation further improves performance for upcoming events such as weekends and weather forecasts. To avoid the corresponding mixed-integer quadratic problem (MIQP) from becoming infeasible, slack variables are introduced in the objective function. Simulation results indicate that the proposed HMPC scheme effectively manages hybrid dynamics, setpoint tracking, disturbance rejection, and the transition between the two phases of the intervention (initiation and maintenance) and is suitable for evaluation in clinical trials.

Mixed logical dynamical modeling and model predictive control of piecewise affine systems with dead zone constraints

Mixed logical dynamical modeling and model predictive control of piecewise affine systems with dead zone constraints

A novel preview control for MLD models and its neural network approximation for real‐time implementation: Application to semi‐active vibration control of a vehicle suspension

Advances in image processing technology have made it possible to measure the surface shape of the road ahead while driving. A new semi-active suspension control method considering the forward road surface shape is proposed. A vehicle model equipped with a semi-active suspension can be expressed as an mixed logical dynamical model. When the shape of the road ahead can be measured, the information on future disturbances is available before the vehicle undergoes. In this paper, the finite time optimization problem for the mixed logical dynamical model is formulated to consider the future disturbances as a mixed integer quadratic programming problem in the same way as the conventional control problem without future disturbance. However, the mixed integer quadratic programming problem is hard to obtain the control action within the control cycle period required in the real-time vibration control with general computers for vehicles. In this paper, the reduction of the computational load is achieved by constructing an approximation function of the designed controller. A neural network is adopted for the approximation. The performance evaluation of the proposed method is evaluated by simulations. In the simulation study, the proposed method achieves better ride comfort with the equivalent suspension stroke compared to the traditional methods.

MLD–MPC for Ultra-Supercritical Circulating Fluidized Bed Boiler Unit Using Subspace Identification

Before carbon capture and storage technologies can truly be promoted and applied, and nuclear or renewable energy power generation can become predominant, it is important to further develop more efficient and ultra-low emission USC units on the basis of leveraging the strengths of CFB technology. In view of this complex system with strong nonlinearity such as the boiler-turbine unit of a thermal power unit, the establishment of a model that is suitable for control is indispensable for the operation and the economics of the process. In this study the form of the nonlinear model after linearization at the steady-state point has been fully considered and an improved subspace identification method, which is based on the steady-state point deviations data, was proposed in order to identify a piecewise affine model. In addition, the construction of the excitation signal in practical applications has been fully considered. The identification results demonstrate that this method has a better adaptability to strong nonlinear systems. The identification normalized root mean square errors of each working condition were almost all less than 10%. On this basis, a framework that is widely applicable to complex system control has been established by combining with the mixed logic dynamic (MLD) model. The canonical form realization was performed in order to transfer the local models into the same state basis. The predictive control was carried out on the boiler-turbine system of a 660-MW ultra-supercritical circulating fluidized bed unit that was based on the above framework. The results indicate that the predictive control performance is closely related to the setting value of the ramp rate and, therefore, prove the effectiveness of the framework.

Fault diagnosis for indirect matrix converter based on mixed logical dynamical model

Fault diagnosis for indirect matrix converter based on mixed logical dynamical model

A Hybrid Model-Based Diagnosis Approach for Open-Switch Faults in PMSM Drives

In this letter, a hybrid model-based diagnosis method is proposed for open-switch faults in permanent magnet synchronous motor drives by combining the advantages of both model predictive control (MPC) and mixed logical dynamic (MLD) models. This idea is based on the receding optimization of the MPC and the estimating capability of the MLD. The predictive current model and the MLD model are established to predict and estimate phase currents, respectively. Besides, the logical vectors are theoretically investigated under various faulty conditions. Derived from the cost function, a diagnosis function is designed to evaluate the consistency between the actual and estimated currents. Consequently, the logical vector minimizing the diagnosis function is selected to locate faulty switches. Finally, experiments are conducted to verify the effectiveness of the proposed method.

Online Fault Diagnosis Method for Grid-Connected Inverters Based on Finite-Set Mixed Logical Dynamical Model Prediction

The grid-connected inverter is a key device in the renewable energy power generation system and large-scale energy storage system, which the operational stability and reliability are the basis for the efficient and safe application of electrical energy. A real-time fault diagnosis method of a three-phase for grid-connected application combining a mixed logic dynamic (MLD) model and finite control set model predictive control (FCS-MPC) is proposed. This paper not only realizes the open circuit fault diagnosis and location of the switching devices in the main power circuit, but also discusses the threshold issues and post-fault operations. The advantage of the proposed method is that it directly uses the control data and measurement signals of the controller without extra sensors and calculation, which will shorter the fault diagnosis time and occupy less calculation resource of the main processor. Simulation results illustrate the quickness of the fault identification and accurate position with robustness to the interference of the diagnosis method. Finally, the effectiveness of the diagnosis method was verified by a 1500W experimental prototype in a laboratory.

Mixed Logical Dynamical Modelling of a Stratified Storage with Phase-Change Material

Thermal storages that use phase-change materials are increasingly applied in heating systems with renewable energy sources. Their main advantage is storing energy of a high density while keeping the storage at lower temperatures. To exploit the full potential, optimal control is applied, which requires an appropriately designed model of the heating system. This paper proposes a model of the latent stratified storage in a mixed logical dynamical form. The proposed model is developed and verified on a real heating pilot system with a latent heat storage. Thermodynamic behaviour of this system is simulated, both by using mixed logical dynamical model and a non-linear thermodynamical model. Obtained results show high accuracy of the models.

Vibration control of semi-active suspension by the neural network that learned the optimal preview control of MLD model

Advances in image processing technology have made it possible to measure the surface shape of the road ahead while driving. We propose a new semi-active suspension control method that considers the forward road surface shape. A vehicle model equipped with a semi-active suspension can be expressed as an MLD (Mixed Logical Dynamical) model. When the shape of the road ahead can be measured accurately, that the information on the future disturbances is available before the vehicle undergoes. In this paper, we formulate the finite time optimization problem of the MLD model in consideration of the future disturbances as a MIQP (Mixed Integer Quadratic Programming) problem in the same way as the conventional optimal control problem without future disturbance. The solution to the MIQP problem can be obtained by a commonly available solver software. However, because the MIQP problem is classified as NP-hard, it is hard to obtain the control action within the control cycle period required by the vibration control problem with general computers for vehicles. In this paper, we achieve this reduction in computational load by constructing an approximation function for the designed controller. A multilayer neural network is adopted for the approximation. The performance evaluation of the proposed control method was evaluated by a simulation. In the simulation study, the proposed method was able to achieve better ride comfort with the equivalent suspension stroke compared to the traditional MLD predictive control and the Skyhook approximation methods. Moreover, the proposed method enables to generate the control signal within the control cycle period.

Adaptive Fast Fault Location for Open-Switch Faults of Voltage Source Inverter

In this paper, the issue of open-switch faults of voltage source inverter (VSI) is studied, an adaptive fast voltage-based real-time open-switch fault location method is proposed. Firstly, for each switching period, the voltage deviation is calculated by the expected voltage and estimated actual voltage, they are obtained via the mixed logical dynamic model (MLDM) and measured currents, respectively. Then, the voltage deviation of each phase is employed as a fault variable. By this way, avoiding the use of additional hardware, and embedding easily in system. Furthermore, considering the impact of sampling error, parameter error, dead time, delay time, and transition time, the adaptive threshold is designed. Finally, simulations and experiments are executed to verify the effectiveness of the proposed method.

Timetabling for a congested urban rail transit network based on mixed logic dynamic model

This paper focuses on the timetabling problem for a congested urban rail transit network. During peak hours, the situation of premature full load of trains and passenger stranded on platforms is serious. To minimize the total passenger’s travelling time and optimize resource allocation from a planning and management perspective, we develop an optimization model considering dwelling time as one of the decision variables to control the number of boarding passengers. In the proposed model, the network internal passenger flow is quantitatively characterized by three types of flows according to its origin and destination under the distributed structure. And the mixed logic dynamic model describes the operation of network, especially the interaction between lines. Furthermore, the number of time-varying transfer passengers is calculated which has never been studied before. At last, we adopt a simulation-based approach to solve the model and prove the effectiveness through two cases.