Subway Traffic Regulation Using Model-Based Predictive Control by Considering the Passengers Dynamic Effect

Due to increasing environmental concerns the focus of traffic management and control is shifting towards optimizing the traffic control measures to also reduce traffic emissions and fuel consumption. In this context we propose a model-based predictive traffic control approach for the balanced reduction of travel times, emissions, and fuel consumption for freeway networks, where not only the local emissions are taken into account but also the dispersion of the emissions to various target zones near the freeways. The core of the approach is a new efficient model for describing the area-wide dispersion of the emissions that is much faster than the models we have proposed in earlier papers. We present a detailed description of the new so-called expanding grid-based model and we embed it in a model-based predictive traffic control approach using variable speed limits.

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Nonlinear model predictive control for the space heating system of a university building in Norway

The distillation is a common method with great energy expenditure used for separation in the oil, food, chemical, etc. However, the technology currently used today in the distillation process is not very different from that used in the first distillation columns in the 19th century. The requirements of thermal energy in the distillation process are enormous. The thermodynamic efficiency of the distillation process is less than 10%. It is estimated that 8% of all energy used by U.S. industries is consumed in the distillation process. Energy is responsible for 50 to 60% of the operating costs of refineries while in chemical that proportion varies from 30 to 40%. These data shows the potential of savings that the distillation process can be achieved when the process is subjected to better control and optimized. The Model Based Predictive Control (MPC) is an advanced control technique with features that solve operational problems present in distillation columns. The MPC can deal with of multivariable systems with interactions and considerable dead times, nonlinearities and restrictions on the variables. One of the most important steps in the MPC is the minimization of an objective function. Different types of objective functions can be used in the MPC control algorithm with specific parameter settings for each type of objective function. In this work, the Wood-Berry model for distillation columns will be used. One MPC control strategy for column using a objective function with economic cost will be implemented. Finally, they will be made adjustments to the parameters of the objective function in order to see how these settings influence the response of the MPC controller.

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.

This paper demonstrates model-based predictive optimal control of active and passive building thermal storage inventory in a test facility in real time using time-of-use differentiated electricity prices without demand charges. A novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. All primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real time while observing comfort and operational constraints. It was determined that even when the optimal controller is given imperfect weather forecasts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility cost savings relative to conventional building operation can be substantial. Central requirements are a facility that lends itself to passive storage utilization and a building model that includes a realistic plant representation. Savings associated with passive building thermal storage inventory proved to be small in this case because the test facility is not an ideal candidate for the investigated control technology. Moreover, the facility's central plant revealed the idiosyncratic behavior that the chiller operation in the ice-making mode was more energy efficient than in the chilled-water mode. Field experimentation is now required in a suitable commercial building with sufficient thermal mass, an active TES system, and a climate conducive to passive storage utilization over a longer testing period to support the laboratory findings presented in this study.

This paper describes an algorithm for solving the optimization problem which occurs in a model-based predictive control (MPC) algorithm for discrete time bilinear models. This optimization problem is nonlinear in general, because the model acts as a nonlinear equality constraint. Common approaches of performing such a nonlinear optimization problem boil down to (successively) approximating the nonlinear objective function, followed by performing a line search. In this paper it is demonstrated that the structural properties of the bilinear state space model enable to formulate the nonlinear optimization problem as a sequence of quadratic programming problems which exactly represent the original objective function, implying that no additional line search is needed. The proposed optimization algorithm is compared to one that is based on linearization around an input trajectory. To benefit from the advantages of both algorithms, a hybrid algorithm is proposed, which outperforms the other two in most cases.

Recently, low-frequency oscillation (LFO) has occurred many times in high-speed railways and has led to traction blockades. Some of the literature has found that the stability of the vehicle-grid coupling system could be improved by optimizing the control strategy of the traction line-side converter (LSC) to some extent. In this paper, a model-based predictive current control (MBPCC) approach based on continuous control set in the dq reference frame for the traction LSC for electric multiple units (EMUs) is proposed. First, the mathematical predictive model of one traction LSC is deduced by discretizing the state equation on the alternating current (AC) side. Then, the optimal control variables are calculated by solving the performance function, which involves the difference between the predicted and reference value of the current, as well as the variations of the control voltage. Finally, combined with bipolar sinusoidal pulse width modulation (SPWM), the whole control algorithm based on MBPCC is formed. The simulation models of EMUs’ dual traction LSCs are built in MATLAB/SIMULINK to verify the superior dynamic and static performance, by comparing them with traditional transient direct current control (TDCC). A whole dSPACE semi-physical platform is established to demonstrate the feasibility and effectiveness of MBPCC in real applications. In addition, the simulations of multi-EMUs accessed in the vehicle-grid coupling system are carried out to verify the suppressing effect on LFO. Finally, to find the impact of external parameters (the equivalent leakage inductance of vehicle transformer, the distance to the power supply, and load resistance) on MBPCC’s performance, the sensitivity analysis of these parameters is performed. Results indicate that these three parameters have a tiny impact on the proposed method but a significant influence on the performance of TDCC. Both oscillation pattern and oscillation peak under TDCC can be easily influenced when these parameters change.

Fuzzy predictive control integrates conventional model-based predictive control with techniques from fuzzy multicriteria decision making, and translates the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the fuzzy set theory. This paper investigates the use of fuzzy decision making in predictive control and compares the results to those obtained from conventional model-based predictive control. Experiments on a nonminimum phase, unstable linear system and air conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model-based predictive controller can be improved by the use of fuzzy goals and criteria with fuzzy decision-making techniques.

Autonomous reactor control using model based predictive control for space propulsion applications

The following paper presents an improved self-tuned Model-based Predictive Controller (MPC) with constraints through an Extended Kalman Filter (EKF). The algorithm is tested in a Continuous Stirred Tank Reactor (CSTR) process for temperature and molar concentration regulations. The proposed method was developed from a previous work [1], where the MPC was tuned from error and control weights of equal value. A posterior analysis demonstrated that the MPC tuning was not robust. Sometimes it generated negative weight values due to the EKF convergence and the algorithm cannot always assure a global minimum value at performance index. Therefore, this work considers this problem and always generates positive weights which assures a minimum global value in the cost function of the MPC.

This paper presents an optimal control scheme based on model-based predictive control (MPC) for a wheeled mobile robot (WMR) with nonholonomic constraints. It is shown that, by using MPC, some advantages can be obtained, such as the ability to handle constraints due to state or input limitations and performance improvement. To solve some problems with other MPCs for WMRs, this paper proposes to formulate a cost function in polar coordinates. Considerations regarding the computational effort of the MPC are developed with the purpose of analysing the viability of the proposed technique in real-time.

The growth of distributed solar photovoltaic (PV) generation in low-voltage grids in recent years has raised concerns about power quality disruptions introduced by solar variability. In response, some electricity utilities have introduced restrictions to new PV installations, such as imposing limits to power ramp rates and exports to the grid. These restrictions effectively require PV installations to locally compensate rapid variations in power output, such as those arising from passing clouds, by using batteries, for example. The direct control of responsive loads to follow variations in solar capacity is a cost-effective alternative to batteries to mitigate impacts to the grid. This paper explores the application of model-based predictive control (MPC) of a population of air conditioners (ACs) to achieve optimal tracking of PV generation to compensate solar variability at a substation or distribution transformer aggregation level. The proposed approach exploits an existing model for aggregate AC demand to design an MPC scheme to modulate the demand of clusters of ACs to track fluctuations in PV capacity. The effectivity of the proposed MPC scheme to achieve AC load following and regulation at different levels of PV penetration is analysed in a numerical simulation study, which shows that solar variability can be reduced by 80 to 95% at PV capacities between 25 and 10% of the peak AC demand with minimal impact to end-user comfort levels.

The class of model-based predictive controllers (MBPCs) has been quite successful at overcoming many of the well-known difficulties inherent in nonminimum phase (NMP) systems, e.g., flexible structures, and among the various MBPCs, Unified Predictive Control (UPC) was selected for the control tasks in this paper. Two methods are used to model the tip force of a flexible manipulator. Modal expansion (summation) is a pole but not zero preserving approach, while Infinite Product Expansion preserves both poles and zeros of a system. It is shown that in a flexible structure framework the difference between the exact and modal expansion zeros is significant. The effect of transmission zero mismatch will be observed in a closed loop system, especially when a model based controller is used. Simulation results show that when a UPC is designed based on the modal expansion model, it destabilizes the closed loop system when an exact model is used as the plant. This suggests a severe problem in implementing the designed UPC experimentally. The use of an exact model in a model-based controller design procedure, such as UPC, is therefore suggested. (Author)

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control