Proposed Model Predictive Control Research Articles

Model Predictive Control of a Semi-Active Vehicle-Mounted Vibration Isolation Platform.

When conventional delivery vehicles are driven over complex terrain, large vibrations can seriously affect vehicle-loaded equipment and cargo. Semi-active vehicle-mounted vibration isolation control based on road preview can improve the stability of loaded cargo and instruments by enabling them to have lower vertical acceleration. A combined dynamic model including a vehicle and platform is developed first. In order to obtain a non-linear relationship between damping force and input current, a continuous damping control damper model is developed, and the corresponding external characteristic tests are carried out. Because some conventional control algorithms cannot handle complex constraints and preview information, a model predictive control algorithm based on forward road preview and input constraints is designed. Finally, simulations and real tests of the whole vehicle vibration environment are carried out. The results show that the proposed model predictive control based on road preview can effectively improve vibration isolation performance of the vehicle-mounted platform.

Model Predictive Control in buildings with thermal and visual comfort constraints

Model Predictive Control can cope with conflicting control objectives in building energy managements. In terms of user satisfaction, visual comfort has been proven in several studies to be a crucial factor, however thermal comfort is typically considered the only important aspect. Besides human well-being, visual comfort strongly impacts the productivity of the occupants in offices. Therefore, from an economic point of view, it is essential to include visual comfort in Model Predictive Control for buildings. In this paper semi-linear support vector machine is applied to learn suitable models for visual comfort measured by Daylight Glare Probability. The resulting model is incorporated into a Model Predictive Control framework, together with an autoregressive exogenous model accounting for the thermal dynamics of the building. The approach is validated through an extensive numerical case study, and the benefits of including visual comfort and blind control in the Model Predictive Control problem are evaluated. We observe that the proposed Model Predictive Control scheme ensures both the thermal and visual comfort constraints at the expense of 2.2% to 7.2% higher energy consumption compared to the benchmark Model Predictive Control configuration, which considers only the thermal comfort constraints.

Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals

Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops.

Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data.

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control (MPC) algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems. Economic MPC can be used in a power system to ensure frequency stability, real-time economic optimization, control of the system and optimal load dispatch from it. A generalized terminal penalty term was used, and the finite-time convergence of the system was guaranteed. The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system, which had two areas connected by an AC tie line. The simulation results demonstrated the effectiveness of the algorithm.

An integrated thermal management strategy for cabin and battery heating in range-extended electric vehicles under low-temperature conditions

The integrated thermal management strategy under low-temperature conditions can effectively alleviate mileage anxiety associated with electric vehicles and improve the thermal comfort of the cabin. In this study, an integrated thermal management topology for range-extended electric vehicles is proposed, which recovers the waste heat from the range extender and the electric drive system for the heating of the battery and the cabin to improve energy utilization. Considering the driving modes and heating demands, the working modes and switching rules are designed. Especially, a control-oriented coupling system model is established for pure-electric mode. Taking cabin heat load demand, battery heat generation, and electric drive system heat generation as disturbances, a model predictive controller is designed for the heating power of the heater. An integrated thermal management system model verified by experiments is established in AMEsim. The initial temperature of the environment and vehicle is −20 °C, and the target temperatures of the cabin and the battery are set at 20 °C and 25 °C respectively. The feasibility of the strategy is verified by the co-simulation of Simulink and AMEsim under 4 WLTC cycles. The results show that in the range-extended mode, the integrated system shortens the battery heating time and reduces fuel consumption by more than 10.2 % compared with the independent system. In pure-electric mode, the heating speed of the proposed model predictive control strategy is slightly faster than that of the on–off strategy, the heating energy consumption is reduced by 20.95 %, and the total energy consumption for propulsion and thermal management decreased by 2.84 %.

MPC-Based Flux Weakening Control for Induction Motor Drive With DTC for Electric Vehicles

Induction motors require high speed (over the rated speed) in working areas such as electric vehicles, which is achieved through flux weakening control. A new flux weakening control approach for a direct torque controlled induction motor operating at high speeds is recommended in this work. The proposed model predictive control (MPC) based flux weakening strategy enhanced criteria such as time to reach steady state, ripple, and so on, in addition to operating the induction motor over its nominal speed. The induction motor has been driven with direct torque control (DTC) in the flux weakening study because it has the least dependence on the parameters and can react more quickly to torque/speed changes. DTC controlled induction motor includes features such as being able to adapt quickly to changes in torque/speed references, low dependency on system parameters, reaching high speeds, time to reach steady-state, and low steady-state error using the proposed MPC-based flux weakening control.

Model predictive control with implicit terminal ingredients

This paper proposes a new formulation of model predictive control for linear discrete time systems under state and control constraints. The main novelty of the proposed formulation is the introduction, and a computationally adequate utilization, of the implicit terminal constraint set and the implicit terminal cost function. From a numerical point of view, the deployment of the implicit terminal ingredients enables a computationally tractable use of model predictive control for higher dimensional control problems. From a practical point of view, the design of the proposed model predictive control algorithm is very constructive for a class of linear systems with polyhedral constraints. From a structural point of view, the proposed model predictive control retains a priori guarantees of the highly desirable positive invariance and exponential stability properties.

Pseudo-spectral optimization and model predictive control of vehicle shift process with dual clutch transmission

To improve the shift quality of dual clutch transmission (DCT) vehicles and reduce the jerk and sliding friction in the shift process, a real-time planning of clutch optimal engagement trajectory and control method in the shift process is proposed. This method combines pseudo-spectral optimization, machine learning, and model predictive control. First, considering the jerk, sliding friction work, and shift time as the optimization indices, the adaptive pseudo-spectral method is employed to optimize the engagement trajectory of the clutch during the shift process. Based on the optimal trajectory data set, a gradient boosting decision tree-based real-time planning method for the clutch target engagement trajectory is proposed. Second, a model predictive control strategy for the shift process is proposed based on the DCT system model to track the optimal target trajectory in real-time. Simulations and experiments reveal that the proposed target engagement trajectory planning method can plan the clutch target engagement trajectory for various accelerator pedal openings and initial clutch speed states in real-time, and the proposed model predictive control method can accurately track the target trajectory. Compared with the original vehicle strategy, under 35% and 65% accelerator pedal opening, the maximum absolute value of jerk produced by the proposed strategy was reduced by 31.06% and 31.46%, respectively, and the sliding friction work was reduced by 22.87% and 23.24%, respectively. The shift times of the proposed strategy under 35% and 65% accelerator pedal openings were 19.12% and 20.69% lower than that of the original vehicle strategy, respectively.

Autonomous cooperative formation control of underactuated USVs based on improved MPC in complex ocean environment

A dual model predictive control (DMPC) method based on virtual trajectory is proposed in this paper, in order to achieve autonomous cooperative formation control of underactuated unmanned surface vehicles (USVs) in complex ocean environment. Firstly, the formation tracking error model of the USVs is designed, and considering the large initial state tracking error of USVs, a virtual transition trajectory is put forward to guide the design of tracking controller. To solve the mutation problem caused by misalignment of the follower USV's desired trajectory in the early stage, an improved differential tracker is introduced into virtual leader USV to smooth the desired trajectory of the follower USV. In addition, a dual mode switching strategy is designed to decide when and how to quit the transition of the virtual USV. A nonlinear disturbance observer is introduced to compensate the complex marine environment interference. Finally, by introducing terminal penalty function and linear state feedback controller, the Lyapunov function is constructed to prove the control stability of the proposed model predictive control method in finite time domain, and the effectiveness and reliability of the proposed method are verified by simulation experiments.

Nonlinear model predictive trajectory following control with feedback compensation for autonomous four-wheel independent drive electric vehicles

Trajectory following is an important function of autonomous vehicles. To enable a four-wheel independent drive electric vehicle to precisely follow a predefined or real-time generated trajectory with good lateral stability and ride comfort at high velocity, a model predictive control (MPC) scheme with feedback compensation considering model mismatch is proposed in this paper to coordinate the direct yaw-moment control and active front steering. The system input signal computed by model predictive control is corrected by the feedback compensation to cope with the model mismatch existing between the controlled vehicle and the nominal model. Moreover, co-simulation is carried out between the software MATLAB/Simulink and Carsim to verify the proposed method. In comparison, the integration of the direct yaw-moment control and active front steering through model predictive control can overcome the non-smooth problem of vehicle dynamics while implementing the active front steering only. However, the value of the external yaw moment computed by the model predictive controller might be too large due to the predictive error in extreme situations, which could endanger the vehicle. The results reveal that the proposed model predictive control scheme with feedback compensation can effectively compensate the front steering angle and enhance the control effect. Therefore, it can significantly improve the trajectory following accuracy and yaw stability.

Design, Analysis and Implementation of Bidirectional DC–DC Converters for HESS in DC Microgrid Applications

This research proposes an enhanced converter for a hybrid energy storage system (HESS) for a multi-input bidirectional DC–DC power converter (MIPC). When batteries are used for energy storage, their charge and discharge rates are low, putting the battery under current stress and shortening its life. Because of their increased power density, supercapacitors (SCs) can react quickly to abrupt fluctuations and solve this problem. SCs, on the other hand, cannot be utilized for storage since they cannot provide power for prolonged periods of time. Batteries and supercapacitors are employed together in HESSs because their opposing characteristics make them an ideal pair for energy storage. An MIPC is used to connect the HESS to the DC microgrid. The MIPC allows for decoupled battery and SC power regulation, as well as energy transfer across storage devices inside the system. A controller has been developed to regulate both HESS charging and discharging operations, making it a unified controller for DC microgrid applications. The proposed model predictive control (MPC) provided better DC grid voltage restoration to step change in PV generation and load demand over the traditional proportional integral (PI) control scheme. The MPC method minimizes current strains, extends battery life and enhances overall system performance in response to a step change in PV power and load demand as well as providing quicker DC grid voltage control. Simulation and experimental data for the proposed controller were created by varying PV generation and load demand, resulting in faster DC link voltage regulation.

Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.

Broad Learning Aided Model Predictive Control With Application to Continuous Stirred Tank Heater

This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.

Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control

With the global increase in food demand, closed and controlled greenhouses are an essential source for year-round crop production. Maintaining the optimum conditions inside the greenhouse throughout the year is critical to improving crop quality and yield. However, greenhouses consume more resources than other commercial buildings due to their inefficient operation and structure. Therefore, a data-driven model predictive control approach for a semi-closed greenhouse is proposed for temperature control and reducing energy consumption in this study. The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. The greenhouse model's performance is evaluated under varying scenarios, such as increasing the prediction time step and changing the number of samples in the training data set. Results illustrated that the MPC approach had a better temperature control than the greenhouse adaptive control system for winter and summer with an RMSE value of 0.33 °C and 0.36 °C, respectively. Similarly, model predictive control resulted in an energy reduction of 7.70% for winter and 16.57% for the summer season. The proposed model predictive control framework is flexible and can be applied to other greenhouse systems by tuning the model on the new data set.

Probabilistic model predictive control for extended prediction horizons

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

MPC based optimization applied to treatment of HCV infections

Background and Objective: The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection.Methods: A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation.Results: Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction.Conclusions: The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems.

Model predictive control for energy-efficient optimization of radiant ceiling cooling systems

State-of-the-art model predictive control (MPC) applications have been performed in various heating and cooling systems of buildings, such as fan coil unit systems and radiant floor heating systems. However, there is also a significant potential for improved zone air temperature control for better thermal comfort and efficient energy savings in increasingly prosperous radiant ceiling cooling systems. In this research, the physics-based model was developed by applying the building envelope configuration and material property information. The reduced-order model of the original radiant ceiling cooling system model was then simplified using the balanced truncation method for reducing computational cost and maintaining a comparative model accuracy. The model predictive control of radiant ceiling cooling systems was proposed for zone air temperature tracking, allowing the system to be more robust and adaptive to external thermal disturbances such as solar radiation and ambient temperature. The superior performance of model predictive control in terms of accurate zone air temperature tracking and energy efficiency was evaluated by a simulation compared to PID control and conventional bang-bang control in both continuous and intermittent operation. The proposed model predictive control can achieve 21%–27% and 6% energy saving efficiency, compared with PID control and conventional bang-bang control, respectively. And there is rarely any overshoot or steady-state error presence for the zone air temperature, which demonstrated the significant potential of the robust model predictive control for better thermal comfort and efficient energy savings in growing prosperous radiant ceiling cooling systems.

Model predictive control for a heat booster substation in ultra low temperature district heating systems

District heating systems may support an increased penetration of stochastic renewable energy technologies and a reduction in centralized combined heat and power plants to reduce carbon dioxide emissions. Ultra low temperature district heating minimizes transport heat losses while enabling the utilization of low-grade surplus heat. Local heat booster substations can heat water to useable temperatures using a heat pump and a hot water tank for storage and flexible operation. This paper proposes a hybrid model predictive control strategy in which an existing heat booster substation is modelled and its charging schedule optimized in real-time over a 24-h forecasted prediction horizon. This enables load shifting whereby scheduling of the heat pump minimizes operation costs. The realisation of energy flexibility can support greater utilization of renewable energy sources and surplus heat in energy supply systems to reduce primary energy consumption. The linear hybrid model predictive controller was successfully implemented in a real 22-flat multifamily building in Copenhagen to verify the control strategy. A comparison of the proposed model predictive control scheduling to the standard rule-based control showed average savings of 23 % on the electricity costs.

Improved nonlinear MPC for aircraft gas turbine engine based on semi-alternative optimization strategy

The development of advanced control systems is motivated to achieve the complicated aircraft engine control, so a novel semi-alternative optimization strategy based model predictive control is proposed. In this proposed controller, a nonlinear state-space model is generated as the predictive model on the basis of a baseline engine model that estimates the current operating state of engine with an extended Kalman filter every sampling instant, and a quadratic constrained problem rather than a higher order nonlinear problem is constructed based on this model. Moreover, a novel control sequence to be optimized based on semi-alternative optimization strategy is proposed, which includes two parts. The first part represents that all the control variables of the engine are optimized for the current sampling instant while the second part represents that different control variables are optimized alternatively for different future sampling instants over the control horizon. The proposed model predictive control method is implemented to a twin-spool turbofan engine. Simulation results demonstrate not only that the proposed method can achieve a smaller control error than the standard model predictive control algorithm does, but also that the proposed controller is more time-saving than the standard model predictive control, which can save up to about 61% optimization time when control horizon increases to nine.