Model predictive control of continuous stirred tank reactor based on mixed logical dynamical model and its improved algorithm

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

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.

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.

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.

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

In this paper we investigate the stability of discrete-time PWA systems in closed-loop with quadratic cost based model predictive controllers (MPC) and we derive a priori sufficient conditions for Lyapunov asymptotic stability. We prove that Lyapunov stability can be achieved for the closed-loop system even though the considered Lyapunov function and the system dynamics may he discontinuous. The stabilization conditions are derived using a terminal cost and constraint set method. An S-procedure technique is employed to reduce conservativeness of the stabilization conditions and a linear matrix inequalities set-up is developed in order to calculate the terminal cost. A new algorithm for computing piecewise polyhedral positively invariant sets for PWA systems is also presented. In this manner, the on-line optimization problem associated with MPC leads to a mixed integer quadratic programming problem, which can be solved by standard optimization tools.

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.

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

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

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance.

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.

Linear switched reluctance motor (LSRM) has the advantages of simple structure, strong robustness, high efficiency, low cost, and low maintenance. They are used in many fields. This paper mainly studies the position tracking control of linear switched reluctance motor and its model predictive controller design. First, the physical system of the linear switched reluctance motor is analyzed and a discrete equation of its state space model is constructed. In order to better eliminate the static error of the motor during position tracking, the increment form of the state equation of the linear switched reluctance motor is further given. By constructing the prediction equation of the motor and predicting the system output in a given time domain. The problem of solving the optimal control sequence online at each sampling time is further transformed into a standard quadratic programming problem. So the optimal control sequence with state constraints and control quantity constraints can be further obtained. To further optimize the design of the model predictive controller, the effects are compared among different prediction horizon, control horizon, weighting factors, and sampling time on the control performance of the model predictive controller to achieve the optimal control effect. Finally, simulation results prove that the model predictive controller has a good control effect on the position tracking of the linear switched reluctance motor.

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.

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.