Model-based Predictive Control Approach Research Articles

Robust data-driven predictive control of unknown nonlinear systems using reachability analysis

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

Improved Constrained Predictive Functional Control Using Extended Non-Minimal State Space Formulation for the Cement Production Process

In this article, an improved constraint dealing method and an extended state space model-based constrained predictive functional control (PFC) approach is developed for the denitration process in cement production. To improve the control effect of the presented strategy, first, an enhanced form-based state space model in which the changes of the output predictions can be regulated to achieve smoother system dynamics is constructed. Then, a modified constraint dealing scheme, which integrates the relaxation factors to improve the success probability of solving the relevant optimization, is also introduced for the proposed method. On the basis of the two merits, better ensemble control performance is anticipated. Simulations on the regulation of the NOx concentration in the denitration process prove the validity of the proposed constrained PFC strategy.

Simulation and optimal control of heating and cooling systems: A case study of a commercial building

This paper proposes a novel energy conservation measure that optimizes the planning of heating and cooling systems for tertiary sector buildings. It consists of a model-based predictive control approach that employs a grey-box model built from the building and weather data that predicts the building heat load and indoor temperature. Different from classical optimization approaches where the discretized differential algebraic equations are integrated into the optimization formulation, our model is calibrated using black-box multiobjective optimization, which allows for decoupling the predictive model from the optimization problem, thus having more flexibility and reducing the total computational time. Moreover, rather than requiring the angle of solar radiation, solar orientation and solar masks to calculate the radiation data, our approach requires only a simple model of the solar irradiance. The calibrated model is then used by heating and cooling optimization strategies that aim at reducing the energy consumption of the building in the next day while satisfying the indoor thermal constraints. The proposed approach was applied in a case study of a commercial building during heating and cooling seasons and the results show that it was able to yield up to 12% of energy savings while having a mean power forecast error of 8%.

Observer-based event triggering [formula omitted] LFC for multi-area power systems under DoS attacks

This paper investigates the observer-based predictive event-triggered load frequency control (LFC) for a multi-area power system in a smart grid incorporating electric vehicles (EVs) under denial-of-service (DoS) attacks. The DoS attacks were launched in control channels with long duration to block the data transmission process and interrupt the LFC command. This situation becomes even more severe with the integration of EVs in the multi-area power system. The control design formulates the LFC problem as a disturbance attenuation problem in the presence of both long-duration DoS attacks and external disturbances. First, an event-triggering scheme was developed based on the observer in the presence of DoS attacks. Second, a model-based predictive control approach was presented. Then, by using the Lyapunov theory, stability for the multi-area power system with EVs was derived under consideration of the long-duration DoS attacks. Moreover, two algorithms were provided to obtain the model predictive event-triggered control (MPETC) parameters, LFC gains, and control of DoS attacks simultaneously. Finally, a simulation of a two-area power system with EVs was studied to illustrate the effectiveness of the proposed method.

Output-Based Event-Triggered Predictive Control for Networked Control Systems

In this article, the output-based predictive control problem for networked control systems (NCSs) with communication delays is investigated by utilizing the event-triggered mechanism (ETM). First, the output-based ETM is introduced in order to save limited network resources effectively. Second, the system states are estimated from the received output measurements by the Luenberger observer. Third, the model-based networked predictive control (NPC) approach is developed to actively deal with the time delay and stabilize the system. Moreover, two different approaches including the augmented model and the piecewise linear model are adopted, and sufficient conditions that guarantee the closed-loop stability and performance are further provided, respectively. Finally, two simulation examples are given to demonstrate the effectiveness and applicability of the proposed methods for the addressed NCSs.

A synthesis approach of fast robust MPC with RBF-ARX model to nonlinear system with uncertain steady status information

The mechanical model of a plant in real industry is usually difficult to obtain. This paper integrates the data-driven RBF-ARX modeling method and a fast Robust Model Predictive Control (RMPC) approach to achieving output-tracking control of a nonlinear system with unknown steady status information. Considering the large online computational burden of online RMPC, this paper proposes a RBF-ARX model-based efficient robust predictive control (RBF-ARX-ERPC) approach. First, based on the RBF-ARX model, a polytopic uncertain linear parameter varying (LPV) state-space model is built to represent the dynamic behavior of the system; next, two convex polytopic sets are constructed to wrap the globally nonlinear behavior of the system. Then, an optimization problem including several linear matrix inequalities (LMIs) is formulated, which is solved offline to synthesize a sequence of explicit control laws corresponding to a sequence of asymptotically stable invariant ellipsoids in the state space, of which all the optimization results are stored in a look-up table. For the real-time control online, it only involves simple state-vector computation and bisection search. Two simulation examples, i.e. the modeling and control of a widely used continuously stirred tank reactor (CSTR) and a linear one-stage inverted pendulum (LOSIP) system, and the real-time control experiments on an actual LOSIP plant are provided to demonstrate the effectiveness of the proposed RBF-ARX model-based efficient RPC approach.

Integration of Model-Based Signal Control and Queue Spillover Control for Urban Oversaturated Signalized Intersection

An improved model-based predictive control approach integrating model-based signal control and queue spillover control is proposed in this paper, which includes three modules: model-based signal control, queue spillover identification, and spillover control to deal with the problem of traffic congestion for urban oversaturated signalized intersection. The main steps are as follows. First of all, according to the real-time traffic flow data, the green time splits for all intersections will be solved online by the model-based signal control controller whose optimization model is based on model-predictive control (MPC) strategy. Second, the queue spillover identification module will be used to detect the potential queue spillover. If potential queue spillover is detected, the spillover control module will be activated to prevent vehicles from the upstream link of the link with possible spillover from entering the downstream link to avoid traffic congestion. The experiment is performed on a simulated road network. The results verify that the proposed scheme can significantly decrease the delay which reflects the overall performance of the studied intersection.

A data-driven approach for the control of a daylight–artificial light integrated scheme

This paper presents a data analytics model-based predictive control approach for a daylight–artificial light integrated scheme. The essential data are collected from an automated test room with dimmable LED luminaires and motorized Venetian blinds. This study considered machine learning techniques to develop a novel control strategy for all the four-window orientations for maintaining comfort and energy conservation. The shades are operated one at a time, and the annual data collected were used to develop the predictive models. The irradiance, altitude, temperature and daylight on the window are the predictors, and the blind position is the response variable to establish the models for the windows on all four sides of the test room. The standard support vector regression, Bayesian support vector regression and Gaussian process regression models are analysed in comparison with the baseline model. The luminaire dimming control signals generated using the predicted optimum blind position and exterior illuminance based on a building information illuminance model is commissioned for a given room. This approach mainly concentrated on the implementation of an industrial-level product by reducing the computational complexity of the rule-based blind positioning system. At present, the models are in a reconfigurable embedded WiFi-enabled operating system.

RBF-ARX model-based fast robust MPC approach to an inverted pendulum

In general, the online computation burden of robust model predictive control (RMPC) is very heavy, and the mechanical model of a plant, which is used in RMPC, is hard to obtain precisely in real industry. These issues may largely restrict the applicability of RMPC in real applications. This paper proposes a RBF-ARX (state-dependent Auto-Regressive model with eXogenous input and Radial Basis Function network type coefficients) model-based efficient robust predictive control (RBF-ARX-ERPC) approach to an inverted pendulum system, which is a complete and systematic method for designing robust MPC controller because it integrates the RBF-ARX modeling method and a fast RMPC approach. First, based on the offline identified RBF-ARX model without offset term, two convex polytopic sets are constructed to wrap the globally nonlinear behavior of the system. Then, the optimization problem of implementing a quasi-min–max MPC algorithm including several linear matrix inequalities (LMIs) is formulated, and it is solved offline to synthesize a sequence of explicit control laws that correspond to a sequence of asymptotically stable invariant ellipsoids, of which all the optimization results are stored in a look-up table. During the online real-time control, the controller only needs to carry out a simple state-vector computation and bisection search. The proposed approach is applied to an actual linear one-stage inverted pendulum (LOSIP), which is a fast-responding and nonlinear plant. The real-time control experiments demonstrate the effectiveness of the proposed RBF-ARX model-based efficient RMPC approach.

MPC-based approach for online demand side and storage system management in market based wind integrated power systems

Ever-increasing rate of wind power generation as an uncertain and variable source of energy, face new challenges in power system operation. For this reason, optimal real-time operation of wind integrated power systems in profit based markets considering the effects of probabilistic future variations of wind speed is one of the main concerns of system operators. For this purpose, some solutions such as demand response (DR) programs and energy storage systems (ESS) are widely being used to cover and manage wind generation uncertainty. But some of them such as DR programs can add some extra sources of uncertainty due to unpredictable customer’s behavior. To this end, this paper proposes an online model-based predictive control approach for optimal real-time operation of wind integrated power systems including DR and ESS facilities. Discrete-time manner, re-optimization characteristic, and adaptability are the main features of the proposed MPC method which make it well-suited to address high uncertainties regarding wind power generation and customer’s behavior. Besides, MPC considers all interactive effects of the control facilities in accordance with the expected wind farm output power in the future prediction horizon to maximize wind power utilization and so enhance social welfare. In addition, the uncertain nature of wind power is modeled using Markov chain Monte Carlo method. For efficiency evaluation of the proposed approach, simulation is implemented in MATLAB software using YALMIP optimization toolbox for the 8-bus test system. Results confirm the acceptable performance of the proposed approach in reducing operation cost through optimal uncertainties management.

Improved Finite Control-Set Model-Based Direct Power Control of BLDC Motor With Reduced Torque Ripple

This paper deals with active torque ripple compensation based on direct power control of permanent magnet brushless DC (BLDC) motor drive. The torque undulations caused by the mismatch between stator current and its nonsinusoidal back electromotive force (back EMF) waveforms is a well-known issue on BLDC motor drives. In this paper, to remedy this problem, a simple and comprehensive direct power control based approach is proposed. Instead of using conventional field-oriented control or direct torque control schemes, which are widely used in ac machines with sinusoidal flux distribution, an alternative approach is presented to control the mutual torque production through an active and reactive rotor power control loop. As a result, the proposed approach provides a simple way to reduce torque ripple of a permanent magnet synchronous motor with nonsinusoidal back EMF without requiring rotor orientation or back EMF harmonic content estimation method. In order to synthesize the proper input voltages in the context of torque ripple minimization, high bandwidth controllers are required to cope the torque ripple frequencies. In this sense, the power control loop is implemented based on two-vector finite control-set model-based predictive control approach, which provides fast torque response and good steady-state performance. The validity of the proposed control scheme for low torque ripple BLDC motor drive is verified through experimental results, achieving good steady-state performance while maintaining quick dynamic response.

Development, implementation and performance of a model predictive controller for packaged air conditioners in small and medium-sized commercial building applications

Small and medium sized commercial buildings, such as retail stores, restaurants and factories, often utilize multiple packaged air conditioners, i.e. roof top units (RTUs), to provide cooling and heating for open spaces. A conventional control approach for these buildings relies on local feedback control, where each unit is cycled on and off using its own thermostat. The lack of coordination between RTUs represents a missed opportunity for operating more efficient units when there is strong coupling between the spaces they serve and can lead to unnecessarily high electrical demand due to the inherent randomness of unit cycling. This paper presents an overall model-based predictive control (MPC) approach for RTU coordination that includes a description of the control architecture, modeling approach, implementation, and assessment. We provide results of laboratory and field tests that demonstrate the short-term and long-term performance of the MPC solution in terms of energy and demand savings.

Suppression Research Regarding Low-Frequency Oscillation in the Vehicle-Grid Coupling System Using Model-Based Predictive Current Control

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.

A Model-Based Predictive Direct Power Control for Traction Line-Side Converter in High-Speed Railway

With the rapid development of China high-speed railway, the low frequency oscillation (LFO) of electrical quantities appears more often recently and leads to some severe problems of train operation. To improve the traction line-side converter control and suppress the phenomenon economically and effectively, a model-based predictive direct power control (MPDPC) approach is proposed in this paper. The approach adopts a discrete-time model of traction line-side converter in d-q reference frame to predict the future values of the input active and reactive power. The optimal switching state is selected by minimizing a cost function of power to evaluate the power errors at the next sampling time. Through the theoretical analysis and simulations, the performance of MPDPC is compared with traditional transient direct current control (TDCC) that is widely adopted in China Railway High-Speed 3 electric-multiple-unit. The real-time online simulations based on Real-Time Laboratory (RT-LAB) are also realized to further validate the results. Moreover, two vehicle-grid cascade simulation systems are constructed, and the LFO suppressing capability of MPDPC and TDCC is compared. Finally, the steady-state characteristic, dynamic characteristic, and LFO suppressing capability of MPDPC are demonstrated through the analysis of key performance indexes.

Calculating the profits of an economic MPC applied to CSP plants with thermal storage system

Abstract Electricity producers participating in a day-ahead energy market aim to maximize profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain moment in time. The development of an economically-optimal generation schedule is the core of the generation scheduling problem. To solve this problem, renewable energy plant owners need, besides energy prices forecast, weather prediction. Among renewable energy sources, concentrated solar power (CSP) plants with thermal energy storage (TES) may find it easier to participate in electricity markets due to their semi-dispatchable generation. In any case, the limited accuracy of forecasting solar resource brings about the risk of penalties that may be imposed to CSP plants for deviation from the submitted schedule. This paper proposes a model-based predictive control (MPC) approach with an economic objective function to tackle the scheduling problem in CSP plants with TES. By this approach, the most recent forecast and the current status of plant can be used by the proposed economic MPC approach to reschedule the generation conveniently at regular time intervals. On the other hand, a more feasible generation schedule for the next day is performed at the appropriate time thanks to the use of short-term forecast. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP plant with TES under the assumptions of perfect price forecasts and participation in the Spanish day-ahead energy market. A case study based on a half-year period to test several meteorological conditions is performed. In this study, an economic analysis is carried out using actual values of energy price, penalty cost, solar resource data and its day-ahead forecast. Results show an economic improvement in comparison with a traditional day-ahead scheduling strategy, especially in periods with a bad weather forecast. To overcome the lack of short-term weather forecast data for this study, a synthetic short-term predictor, whose accuracy level can be tuned by means of a parameter, is used. Sweeping this accuracy level between the situation with no forecast improvement and perfect short-term forecast, the MPC strategy reaches an improvement in total profits during the six months period between 13.9% and 33.3% of the maximum room for improvement. This maximum ideal improvement is defined as the difference in profits between the MPC strategy with perfect forecasts and the day-ahead scheduling strategy.

Economic MPC applied to generation scheduling in CSP plants

This paper proposes a model-based predictive control (MPC) approach with economic objective function to face the scheduling problem in concentrating solar power (CSP) plants with thermal energy storage (TES) in a day-ahead energy market context. By this approach, the most recent energy prices, weather forecast and the current plant’s state can be used by the proposed economic MPC approach to reschedule the generation conveniently from time to time. The proposed approach is applied, in a simulation context, to a 50 MW parabolic trough collector-based CSP with TES under the assumption of perfect price forecast and participation in the Spanish day-ahead energy market. A case study based on a four-month period to test several meteorological conditions is performed. In this study, a complete economic analysis is carried out using actual values of energy price, penalty cost, solar resource data and its forecast. Results show an economic improvement in comparison with a traditional day-ahead scheduling strategy.

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

This paper presents a regulation technique based on the model-based predictive control (MPC) for a subway traffic system. The role of passengers in the traffic system has been modeled analytically by considering passenger flow arriving at platforms, passenger demand and the number of passengers at the platforms and in the trains. Three major reasons for passenger discomfort are introduced (waiting and traveling, congestion and jitter discomfort) to develop an appropriate objective function. The MPC approach is used to develop control actions to minimize the objective function by conforming to safety and operational constraints. A simulation was carried out using the specifications of Tehran Metro Line 2, the results of which demonstrate the effectiveness of the proposed method.

Two-Level Hierarchical Model-Based Predictive Control for Large-Scale Urban Traffic Networks

Network-wide control of large-scale urban traffic networks using a hierarchical framework can be more efficient and flexible than centralized strategies for reducing the traffic congestion in big cities, because it can adequately address some problems that occur in controlling such large systems, e.g., computational complexity, multiple control objectives, weak robustness to uncertainties, and so on. In this paper, we propose a two-level hierarchical control framework for large-scale urban traffic networks. At the upper level, based on decomposing a heterogeneous traffic network into several homogeneous subnetworks, a higher level optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand-balance problem. At the lower level, the controller with a more detailed traffic flow model for each subnetwork determines the optimal signal timing within the given region under the guidance of the upper-level controller through communication. For the application of this architecture in real time, the model-based predictive control approach is utilized so as to obtain the best solutions for both levels. Moreover, in order to decrease the computational complexity, a distributed control scheme within each subnetwork is developed at the lower level. The proposed approach is evaluated by simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with that of other control strategies.

A MPC approach for optimal generation scheduling in CSP plants

Thermal energy storage (TES) systems allow concentrated solar power (CSP) producers to participate in a day-ahead market. Therefore, the optimal power scheduling problem can be posed, whose objective is the maximization of profits derived from electricity sales. The daily generation schedule has to be offered in advance, usually the previous day before a certain time, thus an electricity price and weather forecast must be carried out. This paper proposes a model-based predictive control (MPC) approach for optimal scheduling in CSP plants. This approach has a dual purpose: (1) the periodic update of the generation schedule to track the schedule that has been committed to by means of the most recent electricity price and weather forecast and information about the plant state and (2) the generation of the optimal schedule for the next day. As these two tasks are related, they are performed simultaneously. Therefore, the MPC sliding window is composed of a first time interval to track the committed schedule and a second time interval to generate the next schedule for the following hours. This is then offered as the generation schedule for the next day at the appropriate time. The proposed approach is applied, in a simulation context, to a 50MW parabolic trough collector-based CSP plant with molten-salt-based TES. The chosen criterion to track the committed schedule is the even distribution of the possible generation error within the first interval. A case-study with overestimated initial DNI forecast is undertaken. The results show that the MPC control with short-term DNI forecast significantly improves the above-mentioned objective and allows for a reduction of the deviation from the scheduled generation, when compared with the case without short-term DNI forecast.

Model-Based Predictive Control Using Differential Evolution Applied to a Pressure System

Model-Based Predictive Control techniques have been applied industrially since the 1970s, presenting favorable characteristics. Among them, it can be mentioned the treatment of process constraints and optimization considering the output error with respect to the reference and the used energy. Usually, formulations based on linear models of the plant are used. However, the use of linear models for nonlinear plants can result in control loops with limited or even reduced performance. In this paper, the use of a Model-Based Predictive Control approach using a Differential Evolution algorithm for optimization applied in a pressure control system is presented. The results show the advantages found by using such nonlinear formulation. The methodology used, with comments about the obtained results and conclusions on this line of research are presented.