Model-based Predictive Control Research Articles

Harmonics Compensation by Using a Multi-Modular H-Bridge-Based Multilevel Converter

This paper presents an active power filter based on a seven-level cascade H-bridge where the main contribution is a control strategy that combines model-based predictive control, the voltage vectors of the converter output levels, the phase shift PWM technique, and suboptimal DC-link voltage control. The proposed scheme greatly simplified the overall control system, making it well suited to compensate the current harmonics distortion at the grid side, generated by nonlinear loads connected to the point of common coupling. In addition, the proposed method achieved a balancing of the capacitor voltages of the seven-level cascade H-bridge converter by using the minimum DC-link voltage sensors. This feature significantly reduced the control system complexity and provided a low computational burden. Experimental results confirmed the feasibility and effectiveness of the proposed controller.

A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of ±10∘. Numerical simulations were carried out to validate the control strategy.

Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor

In this study, we propose a two-stage optimal control framework for a fed-batch bioreactor. The high-level controller aims to obtain the optimal feed trajectory that maximizes the final time productivity and yield using a nominal model. By contrast, the low-level controller maintains the high-level performance in the presence of the model-plant mismatch and real-time disturbances. This two-stage decomposition can perform the closed-loop operation with less online recomputation. To solve the high-level optimization, differential dynamic programming (DDP), a model-based reinforcement learning that employs the derivatives of the model is applied. Three types of low-level controllers are proposed: DDP controller, a model predictive control (MPC) that tracks the high-level trajectory, and an economic MPC. We first validate that DDP yields as good result as the direct method. Second, we compare the three low-level controllers and verify the necessity of the two-stage decomposition through the studies on a bioreactor.

Hierarchical model-based predictive controller for a hybrid UAV powertrain

The emerging trend of vehicle electrification is transforming the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design. A notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This article seeks to address this challenge through the design and validation of a model predictive controller for a hybrid unmanned aerial vehicle powertrain. A multi-domain extension of the graph-based modeling framework is formulated and used to model the multi-physics behavior of the air vehicle. An extensive model validation procedure is performed and the validated graph model is used to develop two control strategies: one baseline and one predictive controller. To coordinate multi-timescale system dynamics, the predictive controller leverages a hierarchical control architecture to plan a battery state of charge bound. The control strategies are experimentally validated and show that the advanced controller yields improvements in performance and reliability metrics while reducing fuel consumption by ∼10%.

Presenting an effective and easy method for designing and tuning predictive controller for boost and buck-boost converters in Continuous Conduction Mode

The main goal of this research is to introduce an easy algorithm for designing and tuning the model-based predictive controller in the ARMarkov-PFC Plus PID (APP) controller to control boost converter, which has been previously presented by the authors of this paper. The proposed algorithm was more straightforward than the previous one and does not need the predictive model. Simulation results confirmed their performance was very close to each other. Meanwhile, APP controller – tuned by the new method – was used to control buck-boost converter in this research. Simulation and experimental results proved the efficiency of the new presented method.

Experimental Validation of Wave Induced Disturbances for Predictive Station Keeping of a Remotely Operated Vehicle

Predictive control methods can substantially improve the performance of Unmanned Underwater Vehicles (UUVs), particularly in shallow water environments or near the free surface where wave induced disturbance are of magnitude comparable to the vehicle characteristic inertia. To facilitate the adoption of these methods, a fast estimation of the time evolution of hydrodynamic forces acting on a vehicle is required. To this end, we perform experiments in a wave tank with an ROV to validate the use of Linear Wave Theory (LWT) to capture the time history of surge, heave and pitch wave induced forces and moments. Validation is performed for various sea states, reconstructed with a mean correlation of 0.9138 in comparison to experimental measurements, displaying a maximum normalised mean error deviation between simulation and experimental data of 0.16 and 0.27 respectively for surge and heave forces, and 0.34 for pitch moment. The effectiveness of employing real-time wave disturbance forecasting for the purpose of anticipatory control is then assessed by incorporating the predicted loads within a Model Predictive Controller. Results display a mean RMS positional error reduction of 47.32% in comparison to a standard PD controller. This presents evidence that accurate, near real-time predictions of the wave-generated forces and moments on an ROV can be produced, laying the foundation for developing model-based predictive control strategies that better suit operation in harsh environments.

A lateral control strategy for unmanned ground vehicles with model predictive control and active disturbance rejection control

This paper presents a lateral control strategy with kinematic state error model-based predictive control and extended state observer for unmanned ground vehicles. Firstly, we propose a circular arc prediction technique to calculate the state of the reference system. Then, inspired by the idea of active disturbance rejection control, an extended state observer is utilized to estimate the value of the total disturbance caused by modeling uncertainties, external disturbance, and other factors in order to compensate model error. Finally, we propose a lateral controller that combines model-based prediction with extended state observer through state feedback to achieve precise trajectory tracking. The performance of the proposed control strategy is demonstrated by a co-simulation between CarSim and MATLAB/Simulink.

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

The development of new control techniques for multiphase induction machines (IMs) has become a point of great interest to exploit the advantages of these machines compared to three-phase topology, for example, the reduced phase currents and lower harmonic contents. One of the most analyzed techniques is the model-based predictive current control (MPC) with a finite control set. This technique presents high x–y currents because of the application of one switching state throughout the whole sampling period. Nevertheless, it is one of the most used due to its excellent dynamic response. To overcome the aforementioned drawbacks, new techniques called virtual vectors have been developed, but although there are several articles with experimental results, the algorithm for implementing the technique has not been appropriately described. This document provides a clear and detailed explanation for algorithm implementation of virtual vectors through two proposed variants VV4 and VV11, in a six-phase machine drive. The first entails lower computational cost and the second lower loss in the x–y plane. According to performance indicators such as the total harmonic distortion and the mean square error for both case studies, experimental tests were evaluated to determine the implementation’s behaviour.

Overview of MPC Applications in Smart Cities

: Starting from the early 2000s, the concept of Smart City emerged as a new paradigm for urban development, driven by the exponential progress in communication and information technology as well as by the needs of a constant growing population. The Smart City context requires flexible infrastructures and processes, capable of reacting proactively depending on the situation. In this paper, we seek to explore the role of model-based predictive control (MPC) in the development of Smart Cities. The applications of MPC in different scenarios proves that this optimal control strategy is well suited for the dynamic environment in which it operates. Nevertheless, the literature review reveals that real life implementation of MPC as a control strategy within urban setting is still limited and further work is needed to facilitate its adoption in a wider context

Energy scheduling for residential distributed energy resources with uncertainties using model-based predictive control

This paper proposes a reliable energy scheduling framework for distributed energy resources (DER) of a residential area to achieve an appropriate daily electricity consumption with the maximum affordable demand response. Renewable and non-renewable energy resources are available to respond to customers’ demands using different methods to manage energy. The optimal operation problem is a mixed-integer-linear-programming (MILP) investigated using model-based predictive control (MPC) to determine which dispatchable unit should be operated at what time and at what power level while satisfying practical constraints. Renewable energy sources (RES), particularly solar and wind energies, have recently expanded their electric power systems role. Although they are environment friendly and accessible, there are challenging issues regarding their performance, such as dealing with the variability and uncertainties concerned with them. This research investigates the energy management of these systems in three complementary scenarios. The first and second scenarios are respectively suitable for a market with a constant and inconstant price. Additionally, the third scenario is proposed to consider the role of uncertainties in RES, and it is designed to recompense the power shortage using non-renewable resources. The validity of methods is explored in a residential area for 24 h, and the results thoroughly demonstrate the competence of the proposed approach for decreasing the operation cost.

Central Non-Linear Model-Based Predictive Vehicle Dynamics Control

Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953×10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way.

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%.

Scheduling tank trucks at a fuel distribution terminal using max-plus model-based predictive control

This study addresses the problem of scheduling tank trucks at a fuel distribution terminal. The plant was modeled in the max-plus algebra, applying machine learning to determine process times. With this model, and based on a just-in-time approach, we have developed a predictive controller with two operation modes. This control system aims to prevent the excess of tank trucks inside the loading yard, thus achieving a better flow, efficiency, and safety in the process. Next, we have investigated the case study of a realistic and representative fuel distribution terminal, developing a simulator to enable a performance comparison between the proposed algorithms and the current heuristic. There was a 42.7% reduction in the work-in-progress (WIP) and 41.4% in the lead time, while productivity suffered a 2.8% loss. Bearing in mind, however, that there is flexibility in parametrization to mitigate this loss of productivity. In doing so, the reductions in WIP and lead time are slightly lower, at 34.7% for both metrics. The results show that the proposed control system can contribute significantly to improving the company’s performance indicators.

Model-Based Predictive Control and Dithering Control for an Integrated Gasoline Engine and Three-Way Catalytic Converter System

Abstract Controls of integrated gasoline engine and aftertreatment systems are critical for fuel efficiency improvement and emission regulation. This paper aims to develop novel model-based three-way catalytic converter (TWC) controls to reduce the fuel consumption and tailpipe emissions for a gasoline engine. A model-based dither control and a nonlinear model predictive control (MPC)-based control are presented, respectively. The proposed TWC dither control utilizes a systematically designed dither cycle configuration (including dithering amplitude, offset, and frequency) based on a control-oriented model, with the capability to adapt the dither cycle configuration to various engine operating conditions. The MPC control can optimize engine air–fuel ratio (AFR) to maintain the oxygen storage of TWC at a desired level and thus meet the tailpipe NOx, CO, and HC emission requirements. The efficacies of both model-based TWC controls are validated in simulation with MPC control improving CO emission conversion efficiencies by 8.42% and 4.85% in simplified US06 and urban dynamometer driving schedule (UDDS) driving cycles, when compared to a baseline dithering-based AFR control. Meanwhile, NOx emission conversion efficiency is maintained above the required limit of 95%, while the fuel efficiency remains at the same level as the baseline control methodology.

Multivector Model Predictive Power Control for Grid Connected Converters in Renewable Power Plants

Model-based predictive power control (MPPC) is a well-known and useful technique for control of electric drives and renewable energy generation systems. However, this strategy relies on the knowledge of accurate system models and parameter values, and would otherwise lead to tracking errors in applications because of inevitable parameter uncertainties. Also, step delays in MPPC implementations have to be compensated to eliminate errors. This article proposes a predictive control application to control active and reactive powers exchanged between the grid and the grid side converter (GSC) interfacing renewable energy sources such as wind farms or photovoltaic. The proposed MPPC minimizes the power tracking error based on a proposed cost function and the control system output is the voltage reference for the electronic converter that is converted into switching pulses by the modulation stage. The proposed control strategy is designed to eliminate tracking errors and has fixed switching frequency while featuring a fast-dynamic response, low current THD, and low computational burden. The method has been evaluated using MATLAB/Simulink environment and an experimental 5-kW grid-connected voltage source converter. Finally, the proposed MPPC method has been critically compared to the previous MPPC approaches.

Optimized Inverse Nonlinear Function-Based Wiener Model Predictive Control for Nonlinear Systems

In model predictive control applications, the static nonlinear function of the Wiener model is an obstacle to forming a quadratic cost function. That case results in suboptimal control actions. As is known, a Wiener model predictive controller makes predictions employing the linear block of the Wiener model, and so its control solution is optimal due to the quadratic cost function. If the actual process output is used as a parameter in the empirical model, the inverse of the nonlinear function cannot be obtained as the roots of the nonlinear function in an offline manner, and the Wiener model predictive controller cannot be developed. Having regard to those situations, this paper introduces an offset-free Wiener model-based predictive controller with optimized inverse nonlinear function for the perturbed neutralization processes. Here, to facilitate accounts in the prediction horizon, an autoregressive with an external input model is preferred as the linear element of the Wiener model, and the least-squares-based optimization method is proposed to get the inverse of the nonlinear function. In this way, the model correction term can be employed to achieve an offset-free controller just as employed in the linear model predictive controller. Finally, the control performance of the developed controller is confirmed via comparative MATLAB/Simulink studies.

Stability Analysis of Shunt Active Power Filter with Predictive Closed-Loop Control of Supply Current

This paper presents a shunt active power filter connected to the grid via an LCL coupling circuit with implemented closed-loop control. The proposed control system allows selective harmonic currents compensation up to the 50th harmonic with the utilization of a model-based predictive current controller. As the system is fully predictive, it provides high effectiveness of the harmonic reduction, which is proved by waveforms achieved in performed tests. On the other hand, the control system is prone to loss of stability. Therefore, the paper is focused on the stability analysis of the discussed control system with the additional outer control loop of the supply current with predictive control of this current. The conducted stability analysis encompasses the assessment of system stability as a function of the coupling circuit parameter identification accuracy, whose values are implemented in the current controller, as well as parameters such as the sampling frequency and proportional-integral (PI) controller coefficients. The obtained results show that the ranges of the LCL circuit parameter identification accuracy for which the system remains stable are relatively wide. However, the most effective compensation of the supply current distortion is achieved for the parameters identified correctly, and the greatest impact on the compensation quality has the value of L1 inductance.

Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage

This paper investigates the development and assessment of a model-based predictive control strategy for the district heating system at the Drake Landing Solar Community (DLSC), in Okotoks (Alberta, Canada). Thermal energy is collected by solar thermal collectors and stored seasonally by means of a borehole field. Two water tanks are used as short-term storage, acting as a central unit connecting solar collectors, long-term storage and a district loop. The DLSC has succeeded in using solar energy collected during the summer to provide nearly all the heating needs of this 52-home community in winter, with solar fractions consistently over 90%. The proposed predictive control strategy aims to minimize primary energy consumption while maintaining the same solar fraction. This simulation study –based on model calibrated with on-site measurements– focuses on the optimization of circulation pump speed to manage energy exchange between long-term and short-term storage systems. Minimizing pumping electricity use is a critical aspect of the community environmental impact, since fossil-fuel thermal plants are prevailing in Alberta. Simulation results indicate that the proposed strategy would save, on an annual basis, about 47% of total pump electricity use. This would result in savings in terms of cost (38%) and greenhouse gas emissions (32%).

Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation

In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems’ operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme’s sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%.

Practical Computational Approach for the Stability Analysis of Fuzzy Model-Based Predictive Control of Substrate and Biomass in Activated Sludge Processes

This paper presents a procedure for the closed-loop stability analysis of a certain variant of the strategy called Fuzzy Model-Based Predictive Control (FMBPC), with a model of the Takagi-Sugeno type, applied to the wastewater treatment process known as the Activated Sludge Process (ASP), with the aim of simultaneously controlling the substrate concentration in the effluent (one of the main variables that should be limited according to environmental legislations) and the biomass concentration in the reactor. This case study was chosen both for its environmental relevance and for special process characteristics that are of great interest in the field of nonlinear control, such as strong nonlinearity, multivariable nature, and its complex dynamics, a consequence of its biological nature. The stability analysis, both of fuzzy systems (FS) and the very diverse existing strategies of nonlinear predictive control (NLMPC), is in general a mathematically laborious task and difficult to generalize, especially for processes with complex dynamics. To try to minimize these difficulties, in this article, the focus was placed on the mathematical simplification of the problem, both with regard to the mathematical model of the process and the stability analysis procedures. Regarding the mathematical model, a state-space model of discrete linear time-varying (DLTV), equivalent to the starting fuzzy model (previously identified), was chosen as the base model. Furthermore, in a later step, the DLTV model was approximated to a local model of type discrete linear time-invariant (DLTI). As regards the stability analysis itself, a computational method was developed that greatly simplified this difficult task (in a local environment of an operating point), compared to other existing methods in the literature. The use of the proposed method provides useful conclusions for the closed-loop stability analysis of the considered FMBPC strategy, applied to an ASP process; at the same time, the possibility that the method may be useful in a more general way, for similar fuzzy and predictive strategies, and for other complex processes, was observed.