Hybrid system analysis and control via mixed integer optimization

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.

Piecewise affine modeling and hybrid optimal control of intelligent vehicle longitudinal dynamics for velocity regulation

Aiming at the hybrid properties of greenhouse temperature-humidity control system, predictive control based on mixed logical dynamical (MLD) model was researched. Firstly, hybrid system model of greenhouse temperature-humidity system was built based on MLD, and two subsystems of greenhouse temperature-humidity system were identified by forgetting factor recursive least squares (FFRLS) under the conditions of open ventilating window and closed ventilating window, respectively. Secondly, predictive control problem was described as mixed integer quadratic problem (MIQP), in which there were measurable but uncontrollable outside disturbance inputs including outside temperature, humidity, solar radiation and wind speed, etc. Time-series models were adopted to predict the disturbance inputs, then by branch & bound algorithm, MIQP was solved to obtain an optimal switching control sequences, based on which finite-time stability of the MLD system was analyzed and simulation control results were compared between the optimal switching signal and two given ones to verify the effectiveness of the methods achieved in this paper.

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.

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.

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

This paper addresses model reduction for discrete time hybrid systems that are described by a Mixed Logical Dynamical (MLD) model. The goal is to simplify the MLD model while preserving its input/output behavior. This is useful when considering a reachability property that depends on the output and should be enforced by appropriately setting the input. The proposed procedure for model reduction rests on the analysis of the structure of the MLD system and on its observability properties. It is also applicable to PieceWise Affine (PWA) systems that can be equivalently represented as MLD systems. In the case of PWA systems, mode merging can be adopted to further simplify the model.

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 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.

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 proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include linear hybrid systems, finite state machines, some classes of discrete event systems, constrained linear systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through Mixed Integer Quadratic Programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported.

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

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.

For linear hybrid systems consisting of linear dynamic equations interacting with linear threshold events, automata, and logic propositions, provided in mixed-logical dynamical (MLD) form, this paper describes an efficient technique for transforming such systems into an equivalent piecewise affine (PWA) form, where equivalent means that for the same initial conditions and input sequences the trajectories of the system are identical.

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.