Mixed Logic Dynamical Modeling and On Line Optimal Control of Biped Robot

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.

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.

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.

Greenhouse can provide a favorable growth environment, such as proper temperature, humidity and light intensity for plants. Due to the greenhouse of China having characteristics like its input and output variables being continuous and the states of control equipments being discrete, hybrid system theory is used to describe the greenhouse system. Based on the theory of mixed logical dynamical, the model of greenhouse humidity system is established in this paper, and the real-time greenhouse control can also be achieved by solving control sequence which depends on using optimization performance index. As the experiment result shows, the mixed logical dynamical model can simulate the greenhouse humidity system very well, and the optimal control based on this model can restrict the inside humidity index within a range of settings.

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 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.

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.

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.

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

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

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.

This paper presents a preliminar study to defocus solar collectors when the fluid output temperature is greater than the maximum temperature defined by the fluid manufacturer. The proposed defocusing technique is based on a Mixed Logical Dynamical (MLD) model used by a Practical Nonlinear Model Predictive Controller (PNMPC). The MLD model is used to predict the effect of the defocusing on the output temperature and based on this predictions the PNMPC calculates the optimal control decision solving a Mixed-Integer Quadratic Programming (MIQP). For illustrating the operability of the proposed algorithm a simulation in a study case compounded by a loop with four collectors is performed and the controller performance is analyzed.