Constrained Model Predictive Control Strategy Research Articles

Guaranteed H∞ Performance of Switched Systems with State Delays: A Novel Low-Conservative Constrained Model Predictive Control Strategy

In this paper, for the first time, a simultaneous design of a model predictive control plan and persistent dwell-time switching signal utilizing the conventional multiple Lyapunov–Krasovskii functional is proposed for linear delayed switched systems that are affected by physical constraints and exogenous disturbances. The conventional multiple Lyapunov–Krasovskii functional with a ‘jump high’ condition is used as a step forward to reduce the strictness of constraints on controller design compared with the switched Lyapunov–Krasovskii functional. However, a dwell-time constraint is inflicted on the switching signal by the ‘jump-high’ condition. Therefore, to decrease the dwell-time limit, the persistent dwell-time structure is used and compared with other structures. Also, a new online framework is proposed to reduce the number of constraints on controller design at each time step. Moreover, for the first time, exogenous disturbances are considered in the procedure of MPC design for delayed switched systems, and non-weighted H∞ performance is ensured. The simulation outcome demonstrates the great performance of the suggested plan and its ability to asymptotically stabilize the switched system.

Advanced Constrained Model Predictive Control of Vapor Pressure Deficit in Agricultural Greenhouses

The agricultural greenhouse is an open system to the external environment. The challenge is to track the reference trajectory of relevant climatic parameters and regulate the internal climate, despite the strong meteorological disturbances. This study focuses on the modelling and control of Vapor Pressure Deficit (VPD) coupled with temperature and hygrometry in greenhouses. The importance of this parameter is related to plant growth and its sensitivity to external climate disturbances. The VPD is a primordial parameter for preventing plant diseases caused by an inappropriate interior climate. This work provides a methodology for efficient control of VPD with a Multi-setting parameterization of the controller according to the needs of the plant and the actuators’ power available in the greenhouse. For this purpose, the VPD is indirectly evaluated through the ambient air temperature and humidity using the psychometric chart embedded in a fuzzy inference algorithm. For dynamic modelling of VPD, a discrete state-space model is obtained by considering an agricultural greenhouse as a black-box system. Numerical algorithms for Subspace State Space System Identification (N4SID) of Matlab is applied to parametric identification based on experimental measurements. A Constrained Model Predictive Control (CMPC) method is applied for VPD control design, considering physical and operational constraints on Input/output variables. Here, the CMPC strategy is improved by information on the greenhouse’s outside climatic conditions. Promising numerical simulation results show the feasibility of the proposed modelling and control strategy. They show good performances, despite the climatic exchange phenomenon between inside and outside the greenhouse and strong external weather disturbances. The inside VPD tracks nearly reference trajectories with a small overshooting as desired.

Temperature Control for Proton Exchange Membrane Fuel Cell based on Current Constraint with Consideration of Limited Cooling Capacity

AbstractTemperature is one of the key factors to ensure reliable and efficient performance of proton exchange membrane fuel cells. Whereas, due to the limited cooling capacity in automotive application, conventional strategies may fail to remove the excessive heat when the cooling actuator saturates. Hence, three temperature control strategies based on current constraint are developed in this study. Firstly, a dynamic thermal model based on energy conservation is presented. Then a duty ratio split control strategy is proposed to calculate the reduced current from the excess of duty ratio of radiator, yet spike behavior occurs. Hence, a current governor control strategy and a constrained model predictive control strategy are derived to trade off the power demand and cooling capacity, where the maximum load current is calculated by current governor in real time and optimized under the physical constraints of duty ratio and current change rate, respectively. The performances of all the strategies are demonstrated. The current governor control strategy is easy to implement with less computational burden. The constrained model predictive control strategy has fast response and can take into account various constraints explicitly. Both of them have satisfying control performances regardless of limited cooling capacity.

Constrained model predictive control strategy for single-phase switch-mode rectifiers

This study presents a new approach to the control problem of single-phase switch-mode rectifiers. The proposed approach is based on a particular model predictive control scheme, incorporated with soft constraints for the errors of the key control targets. This control system is free of portional integral (PI) controllers, which are responsible for poor dynamic performance. Control objectives of sinusoidal input current, approximately unity input power factor, and output voltage regulation are successfully met under both steady state and load or setpoint changes. Considering the significant advantages of multilevel operation, the reflected AC side voltage exhibits an appropriate three-level modulation. The developed control strategy involves improved performance compared with a model-based sliding mode one. The theoretical analysis is validated by measurements on a laboratory prototype, illustrating the feasibility and suitability of the proposed controller for this type of rectifiers.

Constrained Model Predictive Control for a Pilot Hydrotreating Plant

The main goal of this work is to investigate the application of constrained model predictive control (CMPC) for a pilot hydrotreating plant. The pilot plant used in this study is a catalyst testing unit operated for desulphurization of atmospheric residue feedstock and using the same catalyst and operating conditions of actual refinery hydrotreating processes. The operational goal in such reactors is to maintain the sulphur content of the product at a desired limit. Degree of desulphurization is highly sensitive to reaction temperature and is affected by a number of parameters such as H2/oil ratio and catalyst deactivation. A CMPC controller has been experimentally implemented to optimize the reaction zone temperatures that would result in achieving the desired degree of desulphurization. Selected manipulated variables are system pressure and three reactor skin temperatures. A significant improvement in controller performance is obtained with the CMPC strategy, when compared with the existing PID-based control structure. This study highlighted the main factors affecting the operation and dynamics of such processes. The pilot plant can be considered as a test bed for implementing online optimal control and nonlinear control strategies for the catalytic hydrotreating units in refineries.

Control of a LDPE reactive extrusion process

A constrained model predictive control strategy is implemented to control the weight-average molecular weight and the high molecular weight end of the molecular weight distribution of a low-density polyethylene (LDPE) in a reactive extrusion process. To achieve simultaneous regulation of these properties, the amplitude and width of square wave-shaped pulses of a peroxide solution injected to the extruder are independently manipulated. The two polymer properties are inferred on-line from consistency and power-law index measurements obtained with an in-line wedge rheometer. Simulations and experimental results show that independent control of the polymer properties is feasible by this method, but only in a narrow range of operating conditions.