Performance Of Predictive Control Research Articles

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Influence of usage and model inaccuracies on the performance of smart hot water heaters: lessons learned from a demand response field test

Domestic hot water heaters are considered to be easily integrated as flexible loads for demand response. While literature grows on reproducible simulation and lab tests, real-world implementation in field tests considering state estimation and demand prediction-based model predictive control approaches is rare. This work reports the findings of a field test with 16 autonomous smart domestic hot water heaters. The heaters were equipped with a retrofittable sensor/actuator setup and a real-time price-driven model predictive control unit, which covers state estimation, demand prediction, and optimization of switching times. With the introduction of generic performance indicators (specific costs and thermal efficiency), the results achieved in the field are compared by simulations to standard control modes (instantaneous heating, hysteresis, night-only switching). To evaluate how model predictive control performance depends on the user demand prediction and state estimation accuracy, simulations assuming perfect predictions and state estimations are conducted based on the data measured in the field. Results prove the feasible benefit of RTP-based model predictive control in the field compared to a hysteresis-based standard control regarding cost reduction and efficiency increase but show a strong dependency on the degree of utilization.

Cooperative control strategy of trajectory tracking and driving stability for distributed-drive vehicles under extreme conditions

Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods.

An Impact of Switching Frequency and Model Accuracy on Model Predictive Current Control Performance for Reluctance Synchronous Motors

Abstract The present paper investigates the feasibility of utilizing the simplified prediction model for finite control set model predictive current control (FCS-MPCC) applied to reluctance synchronous motors (RSMs). The FCS-MPCC exhibits torque and current ripples, and a crucial consideration is the reduction of these ripples by increasing the switching frequency. The algorithm’s computational complexity is tied to the accuracy of the adopted model. Two approaches with varying levels of accuracy in predicting current dependencies concerning changes in the inductance of the RSM are investigated. The findings highlight the potential of employing simplified fixed inductance values for efficient control in drive systems, particularly those amenable to high switching frequencies.

Archetype Building Energy Models for Residential Sector: Importance of Uncontrolled Inputs in Predictive Control and Energy Flexibility

A 1st-order gray-box archetype is developed using high-quality measurements from an unoccupied experimental house to investigate the sensitivity of economic model-based predictive control performance under Québec’s dynamic electricity tariff for the residential sector, rate Flex-D, to uncertainty in the weather forecast. The archetype incorporates the outside air temperature and the solar gain as uncontrolled heat sources and has three parameters: the overall thermal capacitance, the thermal resistance to the outside air temperature, and the solar aperture. The performance of economic model-based predictive control is compared under the uncertain day-ahead forecast and the historical measurements of the same day. The results show that the most influential uncontrolled input directing the performance of economic model-based predictive control under rate Flex-D is the price signal, which makes the impact of uncertainty in the weather forecast almost negligible.

Linking dataset quality and MPC in buildings: impact of temporal resolution

This study aims at assessing the impact of dataset quality on the performance of Model Predictive Control (MPC). The dataset feature, which is the target of this study, is temporal resolution, which applies to both data logging and the controller time step. A high temporal resolution might result in a more accurate predictive model, but it increases the need for data storage as well as the computational load on the model training. From the controller side, increasing the temporal resolution might lead to better control performance, but it sabotages the real-time response of the system. First, predictive models are developed based on datasets with different temporal resolutions. Subsequently, these predictive models are implemented within an MPC. Results reveal that decreasing the time step lower than 1 hour does not significantly improve the performance of the MPC. However, increasing the time step of MPC above 1 hour deteriorates its performance. Real-time response of the controller is a crucial criterion which deteriorates as the time step shortens. Hence, a suitable choice of temporal resolution is essential for developing a predictive model and MPC. In our case, a resolution of 1 hour is enough to guarantee a good performance of the controller.

Maximizing performance of linear model predictive control of glycemia for T1DM subjects

Maximizing performance of linear model predictive control of glycemia for T1DM subjects

Model-Free Predictive Control of Power Converters with Multifrequency Extended State Observers

The performance of model-free predictive control (MFPC), which is based on the ultra-local model, is highly dependent on the effectiveness of the extended-state observer (ESO). The standard linear ESO has a high-gain which enhances disturbance rejection attributes. Nevertheless, this ESO has two main limitations: i) it amplifies high-frequency measurement noises, and ii) it deteriorates disturbance rejection performance when the noise immunity is improved. Therefore, this article introduces a new technique to improve both the disturbance rejection and noise suppression of ESO without increasing the bandwidth: the multi-frequency-based ESO (MF-ESO). Two novel ESO structures that operate on the MF-ESO principle are analyzed: parallel and cascade ESOs. Extensive frequency domain analyses unravel the comparative capabilities of the MF-ESO structures for improved disturbance rejection and/or high-frequency noise suppression. A novel adaptive gain is proposed to improve CESO's dynamic performance. The superior features of the proposed MF-ESO are experimentally validated by the model-free predictive control of a grid-connected power converter.

Experimental Validation for Distributed Control of Energy Hubs

As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory. However, most of these strategies have been tested either only in simulated environments or small prosumer units as opposed to larger energy hubs. This simulation reality gap has hindered large-scale implementation and practical application of these method. In this paper, we aim to experimentally validate the performance of a novel multi-horizon distributed model predictive controller for an energy hub network by implementing the controller on a complete network of hubs comprising of a real energy hub inter-faced with multiple virtual hubs. The experiments are done using two different network topologies and the controller shows promising results in both setups.

Degradation-aware data-enabled predictive control of energy hubs

Mitigating the energy use in buildings, together with satisfaction of comfort requirements are the main objectives of efficient building control systems. Augmenting building energy systems with batteries can improve the energy use of a building, while posing the challenge of considering battery degradation during control operation. We demonstrate the performance of a data-enabled predictive control (DeePC) approach applied to a single multi-zone building and an energy hub comprising an electric heat pump and a battery. In a comparison with a standard rule-based controller, results demonstrate that the performance of DeePC is superior in terms of satisfaction of comfort constraints without increasing grid power consumption. Moreover, DeePC achieved two-fold decrease in battery degradation over one year, as compared to a rule-based controller.

Enhancing the Performance and Efficiency of Two-Level Voltage Source Inverters: A Modified Model Predictive Control Approach for Common-Mode Voltage Suppression

In this paper, a new modified model predictive control is proposed to improve the performance of the model predictive control for two-level voltage source inverters by alleviating computational burden and the disadvantages associated with the conventional model predictive control strategy. The objective of the proposed method is to reduce the number of candidate voltage vectors in each sector, thereby improving the overall performance of the control system, as well as achieving common-mode voltage reduction for two-level voltage source inverters. Two strategies are introduced to achieve this objective. Firstly, an algorithm is developed based on statistical computational processes to pre-define the candidate voltage vectors. This strategy involves ranking and considering the most frequently used voltage vectors. Consequently, by reducing the computational burden, the search space for the optimal voltage vectors is reduced. Furthermore, based on statistical results, a strategy is proposed to divide the sectors into three sectors instead of the six sectors in the conventional method. This approach effectively reduces the number of candidate voltage vectors. The modified model predictive control strategy aims to improve the efficiency of the control system by reducing the computational burden, and suppressing the common-mode voltage. The simulation and experiments are carried out to verify the effectiveness of the proposed strategy under various operational conditions. The results demonstrate that the modified model predictive control approach significantly reduces the computational burden and complexity of the control system while effectively suppressing the common-mode voltage; this contributes to improving the performance of two-level voltage source inverters and enhancing their applicability for connecting the renewable energies to the grid.

Model-Free Predictive Control of Power Converters With Cascade-Parallel Extended State Observers

The sensitivity of conventional model predictive control to parametric uncertainties has made model-free predictive control (MFPC) necessary. MFPC with disturbance rejection features requires a high-gain extended state observer (ESO) for state and total disturbance estimation. The standard ESO is limited in performance when high-frequency measurement noise is fed into it. Hence, this article proposes a novel hybrid cascade-parallel ESO which has good measurement noise suppression and does not diminish the outstanding dynamic performance of predictive control. The proposed ESO also has better disturbance rejection than the cascade ESO. Comprehensive theoretical analyses of the cascade-parallel ESO and its alternative structures provide further insights for design and application. Finally, the practicality of the proposed method is demonstrated experimentally by the model-free predictive control of a grid-connected converter.

RBF-ARX model-based MPC approach to inverted pendulum: An event-triggered mechanism

The event-triggered predictive control based on RBF-ARX model (state-independent autoregressive with exogenous inputs constructed by radial basis function network) is designed in this paper, which is aiming at the issue of accumulating computational burden associated with solving the optimization problems at each sampling time. To alleviate the computational burden while the performance of model predictive control (MPC) cannot be simultaneously sacrificed, from the perspective of model building, the data-driven RBF-ARX model presented by a new state space form is combined into the event-triggered model predictive control (ETMPC) method instead of the mechanistic model. Therefore, we attempt to expand the results of ETMPC to the complex industrial processes rather than the restricted model described by mechanism in terms of proposing a new event-triggering condition (ETC) with a data-driven model. Additionally, the stability analysis of RBF-ARX model-based ETMPC is demonstrated with a focus on the boundedness of the model’s output. The simulation results presented in this paper serve to illustrate the effectiveness of the proposed method by acting on an inverted pendulum system in this paper, which can be showed that the computational burden is significantly reduced, and the predictive control performance of ETMPC is as well as the MPC roughly.

Robust virtual‐vector model predictive control of permanent‐magnet motor considering D‐Q axis inductance parameter uncertainty

AbstractTo suppress parameter mismatch and improve the output performance of model predictive control (MPC), a new robust virtual‐vector MPC strategy is proposed for a five‐phase permanent‐magnet motor in this study. Firstly, the incremental predictive model is applied to remove the impact of flux mismatch. Then the d‐q axis inductance parameter sensitivity of MPC is analysed, which produces the predictive current error between the normal parameter and mismatched parameter. Based on the current error, a new cost function is designed to select the voltage vector more accurately when the d‐q axis inductance parameter mismatch occurs. Thus, good robustness to the parameter's variation can be guaranteed. Afterwards, the duty cycle modulation technology is applied to allocate the duration time of two adjacent vector‐vectors and zero vector. So the motor current harmonics and torque ripple can be considerably suppressed. Finally, the experimental results are provided to show the effectiveness of this proposed method.

A robustly stabilizing fault‐tolerant MPC: A performance improvement‐based approach

AbstractIn terms of model predictive control (MPC) performance degradation caused by operational faults, in this article, a robust MPC strategy with active fault tolerance properties is proposed. The proposed strategy incorporates a fault supervision layer into the structure of conventional cost‐contracting formulation‐based robust MPC for the online update of the nominal controller model in the event of faults. The robust MPC is based on multiplant uncertainty, while the supervisory layer consists of a bank of unknown input observers and a decision‐making algorithm. Simulation results in a nonlinear polymerization reactor subject to process faults demonstrate that the proposed approach offers superior performance compared to the conventional strategy.

Modular Modeling and Statistical Validation for Grid-Connected FS-MPC-Controlled Matrix Converters

In recent publications statistical model checking (SMC) has been proposed as a method for verifying the performance of finite-set model predictive control (FS-MPC) algorithms applied to power electronics converters. One of the reasons the full potential of the method in the power electronics systems (PES) has not yet been explored is the time consuming modelling process. In this paper we propose a modular method of modelling the power electronics system components by providing simple building blocks, which can be connected to build different PES. The modelling method is here demonstrated on a direct matrix converter, which operates in a stochastic grid with different harmonic distortion levels and voltage sags. By applying the SMC, the performance of the control algorithm in terms of the output current distortion, effects of the weighting factor selection and grid distortions on the device utilization can be evaluated. The obtained results confirm, that high grid distortions and voltage sags will increase the stress of several devices. This information can be of great importance to identify the most stressed components and how the control algorithm can be adapted to extend the lifetime of the components and thereby the system during different grid conditions. The verified FS-MPC algorithm has also been implemented in an experimental set-up.

A Simplified Multivector-Based Model Predictive Current Control for PMSM With Enhanced Performance

To address the challenges of poor steady-state performance and large amount calculation of conventional model predictive current control (MPCC), a simplified multi-vector-based MPCC with enhanced performance is proposed in this paper. Firstly, an effective voltage vectors (VVs) selection method based on current-error is proposed, which can directly determine the optimal VVs without cost function enumeration. Compared with the standard method, the number of vectors that need to be evaluated is reduced from 7 to 1. Meanwhile, the duty cycle of each VV is calculated based on the current error to realize error-free control. In addition, to alleviate the deleterious effect of dead-time on the control performance, an improved MPCC scheme with optimal utilization of dead-time is proposed. The key is that the dead-time existing in MPCC is regarded as a dead-time voltage vector (DVV), which can be rationally utilized to improve the control performance. Both theoretical analysis and experimental results are given to verify the effectiveness of the proposed MPCC schemes.

Online model-based predictive control with smart thermostats: application to an experimental house in Québec

This paper tests the impact of model resolution and structure on the performance of Model Predictive Control (MPC) implementation in an unoccupied research house in Québec equipped with smart thermostats. Two low-order models and a high-order multi-zone model were calibrated with measured data, with the structure of the multi-zone model being generated automatically during the calibration procedure. The three models were used to apply real-time MPC to an experimental house in Québec using the established dynamic tariffs for morning and evening peaks. MPC with any of the three models successfully preheated the house before the demand-response events, outperforming the reference reactive controller, reducing cost and thermal discomfort. The high-order multi-zone model performed the best, reducing average cost of electricity by 55% and high-price energy consumption by 71%, compared to the low-order models, which achieved cost reductions of 40% and 44% and energy consumption reductions of 48% and 54% respectively.

Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

The performance of model predictive control has a strong correlation to the precision of the physical parameters of the plant, and these parameters are hard to determine since they are continuously changing during the operation process. To fully eliminate the influence of the physical parameters and enhance robustness, a model-free predictive control is proposed in this article to suit the electrical drive systems. The plant model is designed as several discrete-time transfer functions used to decouple the input and output signals and to describe their relationships, and the coefficients of these functions are online designed based on the recursive least square algorithm. An observer is designed to obtain accurately sampled current components considering the delays. The proposed method is applied to a permanent magnet synchronous motor speed control system as the stator current controller, and the simulation and experimental results show the advantages of the improved dynamics, stator current quality, and robustness compared with the conventional model-free predictive current control strategy.

A Comparative Study of Optimal Individual Pitch Control Methods

Wind turbines are subjected to asymmetric loads and fatigue with subsequent increases in their dimension and capacity, leading to a reduction in their lifetime. To address this problem, the individual pitch control (IPC) technique is quite familiar in the control of wind turbines. IPC is used to reduce the tilt and yaw moments, simultaneously alleviating the turbine blade-root bending moments (BRBMs). This study discusses the performance of model predictive control (MPC), H-infinity (H∞), and proportional and integral (PI)-based IPC strategies integrated with collective pitch control. The performance of the reported controllers has been validated using the National Renewable Energy Laboratory (NREL) 5 MW full nonlinear reference wind turbine. Simulation studies are conducted at varying wind speeds and turbulent intensities as per international electrotechnical commission (IEC) norms. Comparative results in the time and frequency domains indicate that the H∞ based IPC achieves enhanced control performance in terms of reduction in BRBMs and damage equivalent load compared to MPC and PI-based control strategies.