Logical Dynamical Model Research Articles

Research on Hybrid Logic Dynamic Model and Voltage Predictive Control of Photovoltaic Storage System

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit’s charging and discharging processes during system operation.

Dynamic task allocation of hybrid flow shop for machines in parallel with different speeds based on an MLD prediction model

ABSTRACT The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method.

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.

Active Magnetic Bearing Three-level Modulation Strategy Based on Mixed Logical Dynamical Model Prediction Controller

Active Magnetic Bearing Three-level Modulation Strategy Based on Mixed Logical Dynamical Model Prediction Controller

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.

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 multi-inverter 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 on MATLAB/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.

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.

Influence of Inverter Open Circuit Fault on Multiple Physical Quantities in the PMSM

The fault of the drive system mainly occurs in the power transistor of the inverter. Compared with the short-circuit fault, open-circuit fault is not easy to perceive and accurately detect, and it will not cause instantaneously devastating damage to the system. In the case of the open-circuit fault, the motor will be in limp operation mode. At present, most of the research results are focused on fault detection methods, and have not analyzed the situation of multiple physical quantities in the limp mode. In this article, the system hybrid logic dynamic model in different combinations of open-circuit faults under vector control mode is established, and the transient characterization of the current is presented. Furthermore, the system-level analysis model of inverter and motor coupling for fault analysis is proposed, and the change rule of electrical parameters, magnetic field, speed, torque, and loss of PMSM under four fault modes is comprehensively studied. Based on the theory of transient temperature field and fluid dynamics, the three-dimensional global temperature analysis model with electromagnetic-temperature bidirectional coupling is established, and the thermal evolution law and limit temperature rise of permanent magnet synchronous motor (PMSM) are revealed. A lot of experiments are done to verify the analysis correctness, combining with the theoretical research results, the fault conditions, and working range of the motor that can maintain the limp operation are given.

Mean-square stability of discrete-time switched systems under modeled random switching

This paper studies the mean-square stability of discrete-time linear switched systems with random switching, where the switching signal is the output of a logic dynamical switching model driven by an independent and identically distributed (i.i.d.) process. This class of switching is referred to as the modeled random switching. By regarding the switching model as a part of the system, a combined switched system with i.i.d. switching is obtained, which is of a hybrid nature, that is, the augmented state vector has both logic and continuous components. The semi-tensor product of matrices and the vector representation of logic are applied to merge the logic and the continuous components of the state. The equivalence between the mean-square stability of the original switched system and that of the merged switched system under i.i.d. switching is proved. By deriving the dynamics of the column-stacking form of the covariance matrix, necessary and sufficient conditions of mean-square stability for discrete-time linear switched systems with modeled random switching are obtained. An illustrative example is provided to demonstrate the usage of the proposed results.

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

Optimizing behavioral interventions to regulate gestational weight gain with sequential decision policies using hybrid model predictive control

Excessive gestational weight gain is a significant public health concern that has been the recent focus of control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually-tailored and “intensively adaptive” intervention to manage weight gain for pregnant women with overweight or obesity using control engineering approaches. This paper presents how Hybrid Model Predictive Control (HMPC) can be used to assign intervention dosages and consequently generate a prescribed intervention with dosages unique to each individuals needs. A Mixed Logical Dynamical (MLD) model enforces the requirements for categorical (discrete-level) doses of intervention components and their sequential assignment into mixed-integer linear constraints. A comprehensive system model that integrates energy balance and behavior change theory, using data from one HMZ participant, is used to illustrate the workings of the HMPC-based control system for the HMZ intervention. Simulations demonstrate the utility of HMPC as a means for enabling optimized complex interventions in behavioral medicine, and the benefits of a HMPC framework in contrast to conventional interventions relying on “IF–THEN” decision rules.