Greenhouse humidity system modeling and controlling based on mixed logical dynamical

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.

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.

Model predictive control based on mixed logical dynamical model can deal with the nolinear effectively,avoid oscillation of subsystem switching, get better control effect. But the mixed integer quadratic programming problem caused by the logic variable leads to computation complexity and long resolving time, it is hard to meet the requirement of real-time. In this paper, logic variable in the optimal variable is handled properly, A parabolic function is added in the performance index. Resolving time is greatly reduced and controller performance is improved. Finally the improved algorithm is applied into the continuous stirred tank reactor, Showing that the method has good control effect.

Founding an accurate mathematical model for power electronic circuit is the primary work in order to design a superior controller. Considering that conventional switching function model only describes controlled transition, while ignored the conditional transition of the circuit, this paper founds a more accurate mixed logical dynamic (MLD) model for a new inverter and takes the MLD model as prediction model to research the finite control set model predictive control (FCS-MPC) for new inverter. FCS-MPC takes full account of distribution properties of inverter, the switch state that minimises objective function in FCS is selected as the control of inverter, without any control loop and modulator, which solves the optimisation problem of MPC. In addition, this paper designs load current observer for inverter based on least square estimation, which enhanced the robust of controller; its validity is verified by the simulation and experiment.

In view of the features of superheated steam temperature system in the power plant, with the models of controlled object under different circumstances, a multiple models modeling method based on mixed logical dynamical (MLD) model is given, and the GPC for the MLD is put forward, its stability is also discussed. The control algorithm is simple and more adaptable for the change of circumstance, also can avoid the disturbance of controllers' switch while using the common method. Considering the main steam temperature plant of a supercritical once-through 600MW boiler, the simulation is done to validate the control strategy. The result show the method has a strong robustness and better tracking performance, the satisfying control of the total operating conditions is also realized.

Human being can decide his walking to auto-adapt to environment changes. Comparing with this, the autonomy of biped robots is inefficient or remains unsolved. One basic reason for this is the less of a unified modeling framework for various types of biped motion in varying environment which allows on-line deriving of the control input In this paper, we propose to recast types of biped motion in the framework of hybrid system, express it as a mixed logic dynamical model. Such a point of view possesses the advantage that it encompasses all the motions (such as single support motion and impact phenomenon) into a unique one, thus allow the control system to be design optimally even though the impact occurs as the interior point of the optimization horizon

Compared with rule-based and optimization energy management strategies, online optimal energy management control strategies for a dual-mode power-split hybrid electric vehicles (PSHEVs) are able to achieve better fuel economy and real-time performance. Global online optimization of a finite time domain energy management strategy based on the hybrid model predictive control (HMPC) algorithm is proposed in this study. To reduce the computing time, a linearized predictive model is built; because dual-mode PSHEVs can be considered hybrid systems that include continuous and discrete states, the hybrid states can be expressed uniformly. Therefore, a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory, and an HMPC energy management strategy is proposed based on the MLD predictive model. To solve the optimal control problem online to obtain the optimal control sequence, the optimal control problem is converted into a mixed-integer linear programming (MILP) problem. The HMPC-based energy management strategy is compared with dynamic programming (DP)-based and rule-based energy management strategies over two different driving cycles. Simulation results indicate that the HMPC-based EMS achieves 80.60% and 83.79% of the fuel economy performance obtained by the DP-based EMS. In comparison, the rule-based EMS only achieves 66.46% and 70.51% of the DP-based control performance. Therefore, the HMPC-based energy management strategy is favorable for real-time control while effectively improving fuel economy.

In this paper, by applying interval methods, a new framework for analysis and control of discrete-time hybrid systems with parameter uncertainty is proposed. In analysis and control of hybrid systems, there are problem formulations such that convex polyhedra are computed, but for high-dimensional systems, it is difficult to solve these problems within the practical computation time. In this paper, instead of computing convex polyhedra, an interval method, which is one of classical methods in verified numerical computation, is applied to analysis and control of hybrid systems. By applying an interval method, discrete-time piecewise systems with parameter uncertainty can be approximately transformed into a mixed logical dynamical model.

The Ministry of MSME administers the Micro, Small & Medium Enterprises (MSME) Mart, while the Ministry of Commerce and Industry administers the Government E-Market (GeM). A study was conducted to understand the performance of industrial clusters in India on these portals with the objective of studying the physical and financial performance of MSME Mart and GeM for MSMEs. The methodology of this study consists of the collection of secondary data from the MSME Mart website using input variables like No. of MSMEs, Micro (Mn), Small (Sn), Medium (Dn), Total Uydam Registration (Ut), No. of MSME Clusters in India (Cn), No. of Working Enterprises in India (Un), Employment in India (En), Original Value of P & M in India (INR crores; Pc), Market Value of Fixed Assets in India (INR crores; Mc), Award of Contract in India (Ac) and output variables like Gross Output in India (INR crores; Gc), which were analysed using descriptive analysis. It was found that there is an increase in the CAGR. The Award of Contract given to MSMEs should be increased as Karnataka and West Bengal are performing well in MSME Mart. The input variables collected from GeM like No. of Orders Placed (Op), No. of Unique Products Sold (Up), No. of Total Products Sold (Ps), No. of Organizations Registered (Cumulative) on GeM (Gn), No. of Sellers/Service Providers (Cumulative; Sn), Order Value (INR crores) by State/UT’s (Os), Order Value (INR crores) by Central Organizations (Co.) and No. of MSE Sellers (Cumulative; Mn) and output variables like Order Value (INR crores; Ove) were analysed using descriptive analysis; it was found that the CAGR is increasing. A correlation analysis was conducted; the findings reveal a strong relationship between the input and output variables. The trend and regression analyses reveal increases in output and input variables. The Government of India and the concerned state governments implement the MSME Mart and GeM for MSME clusters in India to reduce the costs of production and marketing and increase profits, which allows MSMEs to compete in the domestic and international markets through ecommerce.

The T‐type three‐level rectifier has garnered significant attention due to its ability to enhance the voltage waveform quality in power systems and reduce electromagnetic interference with other equipment. To ensure high reliability in high‐power wind and photovoltaic power generation systems, conducting fault diagnosis for T‐type three‐level rectifiers is crucial. This paper first analyzes the input current characteristics of both in‐phase and out‐of‐phase dual transistor open‐circuit faults. A current‐extended observer is developed using a hybrid logic dynamic model to estimate the current value during normal operation. The residual state equation is derived by comparing this estimated current with the actual fault current. A residual information table for fault currents is created through differential solutions of the residual state equation, enabling fault localisation by comparing the residual information against predefined threshold values. Finally, the feasibility and accuracy of the proposed fault diagnosis method are validated through simulations and experiments.

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.

We present a simplified Mixed Logical Dynamic (MLD) model for DC-DC Boost converter using less number of binary variables and continuous variables. A constrained optimal problem is formulated and solved based on Model Predictive Control (MPC) using the simplified MLD model. The optimized switching signal derived by the MPC regulates the output voltage. The MLD modeling of the converter is accurate in the sense of no approximation or linearization being used, therefore, the performance does not depend on the working point. The controller guarantees the optimal performance of the converter because of the constrained items in the cost function of MPC. These features are impossible for the conventional controller designed by the small signal linearized model and the linear system theory. Due to the current reference of the MPC depending on the uncertain parameters, a PI compensator is proposed to adjust the current reference to reduce steady state error of the output voltage. For comparison purpose, the traditional PI controller and the approach proposed in [12] are compared to the proposed method. All PI parameters in this paper are optimized using Genetic Algorithm. Simulation results show the effectiveness and superiority of the proposed method.

This paper presents a comparative study of two widely accepted model predictive control schemes based on mixed logical dynamical (MLD) and nonlinear modeling approaches with application to a continuous stirred tank reactor (CSTR) system. Specifically, we approximate the nonlinear behavior of a CSTR system with multiple local linear models in a MLD framework. The main benefit of such a scheme is the significant improvement in model accuracy when compared with a single linearized model. The benefits and trade-offs associated with predictive control laws synthesized using MLD and nonlinear modeling approaches are also compared.

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.

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.