Mixed Logical Dynamical Model Research Articles

Short switch fault diagnosis method for power converter using a model‐based approach in switched reluctance motor drives

Switched reluctance motor (SRM) is a competitive solution for aircraft and automotive applications due to its fault-tolerant capabilities and robust configuration. Power converter is a key component in SRM drives, but it prone to faults. In this study, a new and fast diagnosis method for short-circuit fault of the SRM converter is proposed based on the mixed logical dynamic (MLD) model and residual generation. Effects of short-circuit fault on SRM drives are analysed in detail. The MLD model of an asymmetrical half-bridge converter of SRM drives is presented to evaluate the currents of the power converter. The short-circuit faulty switch is identified by analysing the system residual, achieved by comparing the outputs of the MLD model and the sensor measurements. In contrast to other methods, the presented method requires no additional hardware but only measured phase currents and command signals, which are available in the control system. Simulation results confirm the effectiveness of the proposed diagnosis method. It is shown that such diagnosis method can identify the faulty switch of the power converter in a few milliseconds, which is important to avoid catastrophic consequences. It is fast, reliable and easy for industrial applications due the low-computational requirement.

Lyapunov‐based hybrid model predictive control for energy management of microgrids

This study presents an advanced control structure aimed at the optimal economic energy management of a renewable energy-based microgrid. This control scheme is applied to energy optimisation in a microgrid with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as distributed generators, hybrid storage systems compound by battery bank, supercapacitors, hydrogen storage unit, and one electric vehicle charging station. The proposed controller consists of a Lyapunov-based hybrid model predictive control based on mixed logical dynamical (MLD) framework. The main contribution of the proposed technique is the assurance of the closed-loop stability and recursive feasibility, by a novel approach focused on MLD models, using ellipsoidal terminal constraints and the Lyapunov decreasing condition. Finally, simulation tests under different operational conditions are performed and the attained results have shown the safe and reliable operation of the proposed control algorithm compared to existing and well-known energy management techniques.

Current residual‐based method for open‐circuit fault diagnosis in single‐phase PWM converter

This study presents an open-circuit fault diagnosis method based on residual changing rate instead of the specific switching signals injection for single insulated gate bipolar transistor in the single-phase pulse-width modulation (PWM) converter. Firstly, the mixed logical dynamic model of the single-phase PWM converter is established to obtain the residual of grid-side current. Then, the characteristics of residual changing rate are obtained through the combination of theoretical analysis and simulation results. Furthermore, the diagnostic variables based on the residual changing rate are defined by the isolation of switching signals. Also compared with the threshold, fault detection and location can be accomplished. The proposed diagnosis method does not need extra sensors and injection of special switching signals, which can reduce the diagnosis complexity and does not affect operating state of the system. Furthermore, the diagnostic method has fast diagnosis speed and robustness against false alarms under different operation conditions. Experimental results have illustrated the validity and feasibility of the proposed diagnostic method.

Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model

In this paper, the problem of hybrid model predictive control (HMPC) strategy based on fuzzy supervisor for piecewise autoregressive with exogenous input (PWARX) models is addressed. We first represent the nonlinear behavior of the system with a PWARX model. Then, we transform the obtained PWARX model into a mixed logical dynamic (MLD) model in order to apply the proposed predictive control which is able to stabilize such systems along desired reference trajectories while satisfying operating constraints. Finally, we propose to introduce a fuzzy supervisor allowing the readjustment of the HMPC tuning parameters in order to maintain the desired performance. Simulation and experimental results are presented to illustrate the effectiveness of the proposed approach.

Formal controller synthesis for wastewater systems with signal temporal logic constraints: The Barcelona case study

We present an approach for formal controller synthesis of the Barcelona wastewater system. The goal of the controller is to minimize overflow in the system and to reduce environmental contamination (pollution). Due to the influence of sudden and unpredictable weather changes within the Mediterranean climate, we propose robust model predictive control strategy. This approach synthesizes control inputs (i.e., flows through network actuators) that make the system robust to uncertainties in the weather forecast; control inputs are updated in an online fashion to incorporate the newly available measurements from the system and the disturbances. We employ signal temporal logic as a formal mechanism to express the desired behavior of the system. The quantitative semantics of the logic is then used to encode the desired behavior in both the set of constraints and the objective function of the optimization problem. We propose a solution approach for the obtained worst-case optimization, which is based on transforming the nonlinear dynamics of the system into a mixed logical dynamical model. Then, we employ Monte Carlo sampling and dual reformulation to get a mixed integer linear or quadratic programming problem.The proposed approach is applied to a catchment of the Barcelona wastewater system to illustrate its effectiveness.

On the complexity and dynamical properties of mixed logical dynamical systems via an automaton‐based realization of discrete‐time hybrid automaton

SummaryModeling of hybrid systems using mixed logical dynamical (MLD) systems is an art. The MLD framework often introduces numerous constraints and auxiliary binary and continuous variables, which, in turn, increase the computational complexity of the optimization problems. This paper presents an automaton‐based realization for discrete‐time hybrid automaton (DHA) with both controlled and uncontrolled switching phenomena by which it is attempted to develop efficient translation techniques to MLD systems and reduce the total number of decision variables in the MLD model. Based on this DHA model, a modified version of MLD systems, which is called extended MLD (EMLD) is formally defined and represented. EMLD is derived based on the concept of forward evolution in which the one‐step delay between the change of discrete states and continuous dynamics of the existing evolution in the conventional MLD systems is eliminated. The result is that the size of EMLD model in terms of the number of variables, which determines the complexity of the synthesis problems on MLD systems, is reduced. The dynamical properties such as the nonblocking, determinism, and well‐posedness of the proposed DHA and its equivalent MLD models are investigated, and the necessary and sufficient conditions are derived. The effectiveness of the proposed ideas is shown by the numerical examples.

An Improved P-DPC for A New Inverter Based on MLD Model

Building an accurate mathematical model for inverter is the key to achieving a precise control. Contrasted to the traditional switching function model of power electronic circuits, this paper built the mixed logical dynamic(MLD) model for a new inverter, and the MLD model was used as a prediction model, then a predictive direct power control(P-DPC) method was researched for the new inverter. A symmetrical 4+4 voltage vector sequence was employed to obtain constant switching frequency and lower THD of output voltage, the action time of vector sequence was calculated by minimizing objective function. The feasibility and effectiveness were proved by simulations.

A Hybrid Dynamical Approach for Allocating Materials in a Dry Bulk Terminal

This paper proposes a new modeling and control methodology for allocating materials in a dry bulk terminal with a finite storage capacity. The dynamical process of material storage allocation in the terminal is modeled using a hybrid system perspective that combines both discrete-event and continuous-time dynamics. The stockyard space is partitioned into a number of slots for exchanging incoming and outgoing material flows in the terminal, leading to a so-called mixed logical dynamical (MLD) model with the maximal storage capacity. Based on the MLD model, a model predictive controller is then proposed for maximizing the economic profit in a rolling horizon manner. A number of Monte Carlo simulations have been performed involving a real case study for analyzing the effects of different slot volumes on the economic performance and the computational performance of the controller. Simulations also demonstrate the potential of the proposed methodology. Note to Practitioners —This paper is motivated by the problem of allocating dry bulk materials in the stockyard of a small import terminal. In current approaches for allocating materials, the storage capacity cannot be considered, and this could lower the terminal profit resulting from delaying the vessel in the terminal when a finite storage capacity is considered. This paper suggests a new approach from a hybrid system perspective that captures the dry bulk terminal operation dynamics. In this paper, we mathematically describe the process of allocating materials into different slots in the stockyard using a hybrid dynamical model and propose a model predictive controller to maximize the terminal operation profit. We then show how the slot size influences the economic performance and analyze its associated computations by conducting simulation experiments involving a case study. Simulation experiments also suggest that this approach achieves significantly more economic benefits compared with current approaches. In future research, a large intermodal terminal will be investigated whereby incoming and outgoing materials of different transport modalities need to be coordinated properly.

Mixed logical dynamical modeling and hybrid model predictive control of public transport operations

Bus transport systems cannot retain scheduled headways without feedback control due to their unstable nature, leading to irregularities such as bus bunching, and ultimately to increased service times and decreased bus service quality. Traditional anti-bunching methods considering only regularization of spacings might unnecessarily slow down buses en route. In this work a detailed but computationally lightweight dynamical model of a single line bus transport system involving both continuous and binary states is developed. Furthermore a hybrid model predictive control (MPC) scheme is proposed, with a dual objective of regularizing spacings and improving speed of bus service operations. Performance of the predictive controller is compared with I- and PI-controllers via extensive simulations using the proposed model. Results indicate the potential of the hybrid MPC in avoiding bus bunching and decreasing passenger delays inside and outside the buses.

Control of Droplet Detachment Frequency in a GMAW Process by a Hybrid Model Predictive Control

Efficient control of a gas metal arc welding (GMAW) process enables one to obtain high quality products as a consequence of achieving a high quality weld. Although control of the droplet detachment frequency in the welding process would play a great role in improving the welding quality, measuring this variable is difficult and expensive. In this paper, we attempt to control the frequency of droplet detachments without directly measuring it. To this end, we utilize the hybrid property of the GMAW process to indirectly control the frequency. Specifically, a mixed logical dynamical (MLD) model is obtained by considering the hybrid act of the process during droplet detachment. Then, a nonlinear model predictive controller with variable control and prediction horizons is designed incorporating the hybrid behavior of the process. The controller regulates the droplet detachment frequency without measuring this variable directly. Computer simulation results show that the proposed controller leads to a higher quality weld compared to the present approaches.

An Open-Circuit Fault Diagnosis Approach for Single-Phase Three-Level Neutral-Point-Clamped Converters

This paper presents a model-based open-circuit fault diagnosis approach for single-phase three-level neutral-point-clamped (3LNPC) converters in electric railway application. The diagnosis algorithm, which only requires the signals existing in the control system, not only detects open-circuit faults but also identifies the faulty device among the transistors and clamping diodes. The mixed logical dynamic (MLD) model of the converter is built to estimate the grid current. The residual generated from the measured current subtracting the estimated one is analyzed under different open-circuit faults. According to the characteristics of the residual changing rate, the proposed approach allows fault localization. The proposed method is effective both in traction and regenerative braking operation and has fast diagnosis speed. Hardware-in-the-loop (HIL) experiments are carried out to verify the effectiveness of the proposed fault diagnosis algorithm.

Formulation and Simulation of Receding Horizon Control over Mixed Logical Dynamical System

Often the modeling of optimal predictive distributed and/or event-triggered control involve combination of linear dynamical models and logical variables. The most suitable tool for modeling such systems is the Mixed Logical Dynamical (MLD) framework. The aim of this paper is to present mathematical formulation and of such model under Receding Horizon Control (RHC) and a method of implementing such model and control in Matlab environment. Also an illustrative example of an MLD model formulation and simulation is shown in the second part of the paper.

Piecewise affine approximations for quality modeling and control of perishable foods

SummaryThis paper proposes a new methodology for modeling and controlling quality degradation of perishable foods when zero‐order kinetics are considered. This methodology approximates the nonlinear model of the zero‐order quality kinetics using the piecewise affine (PWA) modeling representation. For obtaining a proper PWA model, two state‐of‐the‐art methods are discussed, and eventually, a hybrid identification‐based PWA model is considered after the comparison. This PWA model is then transformed into a computational mixed logical dynamical model, based on which an optimal control strategy is proposed that balances food quality and associated energy consumption. Furthermore, a model predictive control is proposed for improving energy efficiency when a dynamical weather environment is considered. Simulation experiments illustrate the potentials of the proposed optimal controller and the model predictive controller in a case study involving the bighead carp.

Optimal strategy for supplier selection problem integrated with optimal control problem of single product inventory system with piecewise holding cost

In this paper, we formulate a hybrid mathematical model of supplier selection problem integrated with inventory control problem of a single product inventory system with piecewise holding cost. This model will be formulated in a piecewise affine (PWA) form that can be converted into mixed logical dynamic (MLD) form. By using this MLD model, we solve the supplier selection problem and control this inventory system so that the stock level tracks a desired level as the reference trajectory as closed as possible with minimal total cost. We use model predictive control for hybrid system to solve the problem. From the numerical experiment results, the optimal supplier was selected at each time period and the evolution of the stock level tracks the desired level well.

Dynamical Modeling and Predictive Control of Bus Transport Systems: A Hybrid Systems Approach

Bus operations, due to their unstable nature, are inefficient when left uncontrolled with respect to retaining headways. Irregularities such as bus bunching lead to loss of time and decrease bus service quality. Development of bus transport system management schemes to avoid bus bunching and improve performance are of high importance, and has thus been the focus of many works in the public transport systems literature. Motivated by the importance of developing bus control strategies for improving performance, and specifically by the lack of a detailed but computationally efficient mathematical model describing bus transport system dynamics in the literature (which can facilitate model-based control design), we propose a mixed logical dynamical model of a single loop bus transport system, which involves both continuous (e.g., bus positions) and binary (e.g., the state of a bus regarding whether it is holding at a certain stop or not) states. Furthermore, we develop a hybrid model predictive control scheme with actuation via bus speeds, which can regularize headways and improve bus service quality. Performance of the predictive controller is evaluated via simulation experiments using the proposed model, where the passenger demands and maximum bus speeds are extracted from data collected from the bus network of the city of Fribourg. Results indicate the potential of the proposed controller in avoiding bus bunching and decreasing passenger travel times.

Hybrid model predictive control of damping multi-mode switching damper for vehicle suspensions

This paper investigates the design and verification of a hybrid model predictive controller of a damping multi-mode switching damper for application in vehicle suspensions. Since the damping mode switches induce different modes of operation, the vehicle suspension system including this damper poses challenging hybrid control problem. To solve this problem, a novel approach to the modelling and controller design problem is proposed based on hybrid modelling and model predictive control techniques. The vehicle suspension system with the damping multi-mode switching damper is formulated as a mixed logical dynamical model comprising continuous and discrete system inputs. Based on this model, a constrained optimal control problem is solved to manage the switching sequences of the damping mode with respect to the suspension performance requirements. Numerical simulation results demonstrate the effectiveness of the proposed control methodology finally.

Approximate MLD System Model of Switched Linear Systems for Model Predictive Control

In this paper, to reduce the computation time for solving the finite-time optimal control problem (i.e., the model predictive control (MPC) problem), an approximate model of switched linear systems is proposed. A switched linear system is one of the typical subclasses of hybrid systems, and has several applications such as networked control systems. First, an approximation of the state variable in switched linear systems is derived. Next, using auxiliary binary and continuous variables, an approximate model given by a mixed logical dynamical system model is derived. Using this approximate model, the dimension of the auxiliary continuous variable can be reduced. As a result, the online computation time in MPC can decrease. Finally, the effectiveness of the proposed method is presented by a numerical example.

A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)

Plug-in hybrid electric vehicles (PHEVs) can be considered as a hybrid system (HS) which includes the continuous state variable, discrete event, and operation constraint. Thus, a model predictive control (MPC) strategy for PHEVs based on the mixed logical dynamical (MLD) model and short-term vehicle speed prediction is proposed in this paper. Firstly, the mathematical model of the controlled PHEV is set-up to evaluate the energy consumption using the linearized models of core power components. Then, based on the recognition of driving intention and the past vehicle speed data, a nonlinear auto-regressive (NAR) neural network structure is designed to predict the vehicle speed for known driving profiles of city buses and the predicted vehicle speed is used to calculate the total required torque. Next, a MLD model is established with appropriate constraints for six possible driving modes. By solving the objective function with the Mixed Integer Linear Programming (MILP) algorithm, the optimal motor torque and the corresponding driving mode sequence within the speed prediction horizon can be obtained. Finally, the proposed energy control strategy shows substantial improvement in fuel economy in the simulation results.

Dynamic Pallet Routing in a Manufacturing Transport Line With Model Predictive Control

This brief describes the application of model predictive control to a manufacturing multipallet multitarget transport line. The mathematical representation of the plant is based on a mixed logical dynamical model. The performance index to be minimized weights the distance of the pallets from their final target, the pallet residence time on the line, and the control inputs. The resulting mixed integer linear programming problem is recursively solved to compute the control action according to the receding horizon approach. Both simulation and experimental results on the real system are reported and discussed to witness the performances of the control algorithm and its ability to manage even pallet route conflicts and target dynamic rescheduling.

Design of a hybrid model predictive controller for the vehicle height adjustment system of an electronic air suspension

The vehicle height adjustment system of an electronically controlled air suspension poses challenging hybrid control problems, since it can operate in several distinct discrete modes (the gas-charging mode, the gas-discharging mode and the no-action) by switching the on–off solenoid valves. This paper describes the development and experimental validation of a new vehicle height adjustment controller for an electronically controlled air suspension based on the theory of hybrid systems. The mixed logical dynamic modelling approach, which is an effective model structure for hybrid systems, is chosen to obtain the hybrid dynamic behaviours of the vehicle height adjustment system. On the basis of some reasonable assumptions and a linear approximation for the non-linearities of the components, the mixed logical dynamic model of the system is constructed by using the hybrid systems description language, which is a high-level hybrid modelling language. Using this model, a constrained optimal control problem is formulated and solved by tuning a hybrid model predictive controller, which can track the desired vehicle height through controlling the on–off statuses of the solenoid valves directly. Simulations and experimental results are presented finally to show how the hybrid framework and the optimization-based control strategy can be successfully applied to solve the vehicle height control problem of an electronically controlled air suspension in a systematic way.