Simplified Model Predictive Control Research Articles

A Simplified Model Predictive Current Control Strategy for Six-phase H-bridge Inverters

Background: The double-end H-bridge inverters can realize independent control of the stator voltage in each phase and have flexible advantages of modulation and fault tolerance; thus, they are more suitable for multi-phase fault-tolerant motor drives. However, due to the increase of voltage vectors and the coupling between the electromagnetic and non-electromagnetic quantities, the normal control strategies for traditional three-phase motor drives do not work anymore. Objective: This paper aimed to propose a simplified model predictive current control strategy based on the vector space decomposition (VSD) and the vectors’ gradual simplification for the three-level six-phase H-bridge inverters. Methods: Firstly, the 729 physical variables were decomposed and mapped onto the fundamental αβ subspace, harmonic xy subspace, and the zero-sequence o1o2 subspace based on the VSD. Then, to eliminate the influence of the harmonic and zero-sequence components on the control performance and make an easy digital implementation, vectors’ simplification has been proposed based on the in-depth analysis of the relationship between the voltage vectors mapped onto different subspaces and vectors’ stratification. With the simplification method, the number of voltage vectors was simplified from 729 to 12, and then the selected voltage vectors were used in the rolling optimization of the model predictive current control (MPCC) to choose the optimal one. Finally, sufficient experiments were carried out including static and dynamic conditions, different modulation index and power factor, etc., to verify the feasibility of the proposed strategy. Results: The simulation and experimental results show that with the simplified MPCC strategy, both the static and dynamic performances are relatively good, and the THDs of the phase current under different modulations and power factors are relatively low. Conclusion: The proposed MPCC algorithm for the three-level six-phase H-bridge inverters has shown obvious improvement in solving the control problems of multi-vectors and complex redundancy issues.

Simplified robust two-step model predictive control for permanent magnet synchronous motor

Traditional finite control set model predictive control (FCS-MPC) selects an optimal voltage vector in one control cycle. However, considering multiple control cycles, it cannot be proved that the voltage vector is optimal, and the uncertainty of PMSM parameters seriously affects the prediction accuracy. In order to balance the relationship between switching frequency, steady-state performance, and robustness, a multi-step robust FCS-MPC is proposed. Firstly, an incremental prediction model is established to eliminate the flux linkage of permanent magnets. The inductance parameters in the incremental model are identified online based on the observer and inductance extraction algorithm. Then, based on the principle of no-beat, the candidate voltage vector in two control periods is simplified to get the candidate voltage vector, and the cost function is used to determine the optimal voltage vector again. Finally, the proposed FCS-MPC method is compared with the traditional FCS-MPC method. The experimental results show the effectiveness of FCS-MPC strategy.

Simplified Reactive Torque Model Predictive Control of Induction Motor with Common Mode Voltage Suppression

Simplified Reactive Torque Model Predictive Control of Induction Motor with Common Mode Voltage Suppression

Improved Model Predictive Speed Control of a PMSM via Laguerre Functions

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response.

Simplified Phase Model Predictive Voltage Control for Half-Centralized Open-End Winding Permanent-Magnet Motor Systems

In order to obtain a wider speed range for the permanent-magnet linear motor drive system, a half-centralized open-end winding (OEW) topology is studied. In this topology, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> movers are supplied by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> +1 voltage-source-inverters (VSIs), and all these VSIs share a common dc bus voltage. In order to reduce the computation burden, a simplified phase model predictive voltage control (S-PMPVC) is proposed in this paper. For S-PMPVC, the optimal and suboptimal base levels are defined. When the optimal base level cannot be implemented, the suboptimal base level is selected. As a result, S-PMPVC requires smaller computation burden while the performances are not degraded. By taking the advantage of the smaller computation burden, S-PMPVC can adopt higher control frequency to improve performances. The effectiveness of S-PMPVC has been verified by simulation and experimental results.

Simplified Three-vector Selection Model Predictive Current Control for PMSM Considering Fixed Switching Frequency

Simplified Three-vector Selection Model Predictive Current Control for PMSM Considering Fixed Switching Frequency

Economic Model-Predictive Control of Building Heating Systems Using Backbone Energy System Modelling Framework

Accessing the demand-side management potential of the residential heating sector requires sophisticated control capable of predicting buildings’ response to changes in heating and cooling power, e.g., model-predictive control. However, while studies exploring its impacts both for individual buildings as well as energy markets exist, building-level control in large-scale energy system models has not been properly examined. In this work, we demonstrate the feasibility of the open-source energy system modelling framework Backbone for simplified model-predictive control of buildings, helping address the above-mentioned research gap. Hourly rolling horizon optimisations were performed to minimise the costs of flexible heating and cooling electricity consumption for a modern Finnish detached house and an apartment block with ground-to-water heat pump systems for the years 2015–2022. Compared to a baseline using a constant electricity price signal, optimisation with hourly spot electricity market prices resulted in 3.1–17.5% yearly cost savings depending on the simulated year, agreeing with comparable literature. Furthermore, the length of the optimisation horizon was not found to have a significant impact on the results beyond 36 h. Overall, the simplified model-predictive control was observed to behave rationally, lending credence to the integration of simplified building models within large-scale energy system modelling frameworks.

A Novel Simplified Finite Control Set Repeat Model Predictive Control for Grid-Connected Inverters

A Novel Simplified Finite Control Set Repeat Model Predictive Control for Grid-Connected Inverters

Implementation of simplified sequential stochastic model predictive control for operation of hydropower system under uncertainty

Sequential stochastic model predictive control (MPC) is a control framework that sequentially employs two optimizers. The high-level optimizer generates a coarse long-term plan based on nominal uncertainty values, while the low-level optimizer refines the short-term plan by considering all possible realizations of these uncertainties. This paper highlights the advantages of the sequential stochastic MPC framework over the traditional multistage MPC approach. It offers faster computation times, improved feasibility of the optimal control problem (OCP), less conservative solutions, and enhanced flexibility in framework tuning. These advantages of sequential stochastic MPC are demonstrated through a case study on flood control in a hydropower station. Additionally, computational efficiency is further boosted with the simplified method which was proposed previously. The results reveal a remarkable performance improvement with an approximately 85 times faster computation time than the standard multistage MPC. These findings establish sequential stochastic MPC framework with the potential for practical implementation.

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.

Simplified Model Predictive Current Control for Surface- Mounted Permanent-Magnet Synchronous Motor Drives with Adaptive Duty Modulation

Parameter control and cost minimization are among the significant aspects of model predictive current controllers. However, with the conventional control scheme of fixed switching vector actuation, susceptibility to uncontrolled current ripples remains a primary concern. This paper presents a simplified approach of model predictive current controller baed on adaptive duty modulation for the surface-mounted permanent-magnet synchronous motor (SPMSM). In this method, the implementation of two successive synthesized voltage vectors adopts the adpative soft-switching combination in each control period. Experimental results validate performance improvement and optimize current predictive accuracy.

Advanced process automation of a pharmaceutical continuous wet granulation line: Perspectives on the application of a model predictive control from solid feeders to dryer

Pharmaceutical continuous manufacturing provides the appropriate tools (e.g. the understanding of process dynamics and appropriate and adaptable control strategy) in order to deal with Quality-by-Design expectations and even to the future smart manufacturing described by Quality-by-Control. Those tools form part of the given framework of the regulatory agencies led by an effective quality risk management. Soft sensors and control algorithms such as model predictive control are stepping stones for more agile processes and increased robustness by keeping the quality attributes of the final drug product in their acceptable ranges and by mitigating undesired events. The implementation of a model predictive control (MPC) system on a pharmaceutical continuous manufacturing plant for the wet granulation process is described. The control objectives and strategy are presented as well as the selected variables, the process dynamics identification, the MPC performance and its specific tuning where a commercial software has been used, setting the framework of this study. MPCs have been applied successfully on two pharmaceutical drug products (Diclofenac and Paracetamol): an accurate control of the API content and of the LOD was achieved in order to produce a constant quality of tablets on both drug products. In addition, some of the process parameters have been identified as mandatory to be step tested for each change of drug product, leading to a simplified MPC implementation.

Reduced sensor based single stage charger with four quadrant capabilities using predictive control

Single stage battery chargers eliminate the DC link capacitor stage between two stage power conversion. In this work, a reduced sensor based four quadrant charger with single stage power conversion is realized with AC–DC matrix converter topology. Although, a small capacitor filter on DC side is used in this configuration, it is only required to supply or absorb high frequency ripple component of the battery current, to prevent the battery from being damaged. The grid current estimation is done using the converter switching model and AC side filter model, leading to the elimination of grid current sensor. The simple model predictive control used here in conjunction with instantaneous reactive power theory, avoids the use of any proportional integral controller, thus making the control scheme simpler. The single stage design increases power density, and the charger’s dependability is increased by the absence of a large electrolytic capacitor. While the battery charges and receives power from the grid, the AC–DC matrix converter used here, performs a voltage bucking function. The simulation results and experimental findings from a lab-developed hardware prototype are used to validate the design and control of the proposed charger.

A simplified economic model predictive control for a gas lifted oil production network based on modifier adaptation

We propose a novel simplified economic model predictive controller (EMPC) for supervisory control of an offshore petroleum production network composed of two wells, a riser, and a separator. The proposed controller uses a gradient-and-modifier-based methodology to mimic a nonlinear EMPC (it considers a nonlinear process model and cost function), while only solving quadratic programming. We alter the modifier adaptation to depend on an estimator and employ slack variables to improve feasibility. Tips for appropriate design, to improve stability and robustness, are presented. Since the tested facility is subject to multiple disturbances, we also use a regulatory controller to stabilize the system. Finally, we compare our method to a linear EMPC and self-optimizing control in various scenarios considering disturbances, noise, and model inconsistencies. The proposed controller always tends towards the economic optimal point, presenting similar nominal performance as the others advanced controllers and better robustness.

Multi-objective Design Optimization and Control Strategy for Digital Hydraulically Driven Knee Exoskeleton

This article presents a multi-objective design optimization strategy to determine an optimal design of digital hydraulically driven knee exoskeleton. To satisfy the overall goal of compact and lightweight design, four key design objectives are defined. Via genetic algorithm based multi-objective optimization technique, the pareto-optimal set of designs is determined and the trade-offs between the design objectives are analysed. Via decisions based on component availability and user-comfort, the dimensionality of the pareto-front is reduced to two and an exoskeleton design is selected that offers a good compromise between the design objectives. For the actuation of the exoskeleton, an energy efficient control strategy is proposed which consists of using passive control during the stance phase and simplified model predictive control during the swing phase. The operation of the chosen knee exoskeleton design and the control strategy is investigated via numerical simulations. The results indicate that the exoskeleton successfully tracks the desired knee motion and delivers the required knee torque.

Robust Stability Analysis of a Simple Data-Driven Model Predictive Control Approach

In this paper, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any terminal ingredients, thus allowing for a simple implementation without (potential) feasibility issues. The proposed approach relies on an implicit description of linear time-invariant systems based on behavioral systems theory, which only requires one input-output trajectory of an unknown system. For the nominal case with noise-free data, we prove that the data-driven MPC scheme ensures exponential stability for the closed loop if the prediction horizon is sufficiently long. Moreover, we analyze the robust data-driven MPC scheme for noisy output measurements for which we prove closed-loop practical exponential stability. The advantages of the presented approach are illustrated with a numerical example.

Investigation of a model predictive controller for use in a highly glazed house with hydronic floor heating and cooling

Presented are the results of an investigation into a model predictive controller (MPC) for hydronic floor heating and cooling in a highly glazed house in Ottawa, Canada. The goal of this investigation was to determine if a MPC would simultaneously result in reduced energy consumption and reduced indoor air temperature (Tia) violations in comparison to the incumbent reactive controller (RC). Shown both experimentally and via simulation, predictive control results in more hours with Tia between the acceptable limits (19.5–25 °C) for significantly less heating and cooling system operation hours. Experimentally, a “pseudo-predictive controller” (PPC) required 35% less cooling hours and 50% less heating hours than the RC baseline. The PPC also reduced overheating time by 88%. A simple MPC was then designed and compared to a RC using a MATLAB simulation. Even with a simple and non-optimized MPC, the simulation confirmed the superiority of said MPC over RC operation for the hydronic floor system. The simulated MPC required 28% less cooling hours and 27% less heating hours than the simulated RC. These results have confirmed the suspected promise of predictive control and next steps include actual implementation of the presented MPC in the highly glazed house long-term.

A Simplified Model Predictive Voltage Control for Three-Phase Four-Switch Inverter-Fed PMSM Drives With Capacitor Voltage Offset Suppression and Current Ripple Reduction

This article proposes a simplified model predictive voltage control (SMPVC) method without weighting factors for three-phase four-switch inverter-fed permanent magnet synchronous motor drives to suppress the capacitor voltage offset and reduce the current ripple. In this article, the control of electromagnetic torque and magnetic flux in the model predictive torque control is transformed into the control of the voltage vector, and the capacitor voltage offset is suppressed by injecting dc component obtained by an adaptive notch filter into the phase current. On the basis, the voltage vector is taken as the only control term in cost function without the weighting factors to suppress capacitor voltage offset, thus simplifying the calculation amount in cost function. Then the simplified voltage vector synthesis method is presented to synthesize the effective voltage vector to reduce the current ripple. Using the simple sector division, the switching sequence of the voltage vector can be determined quickly. The effectiveness of the proposed SMPVC method is validated by the simulation and experiment.

Simplified Model Predictive Current Control of Four-Level Nested Neutral Point Clamped Converter

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

Design and Control of Novel Single-Phase Multilevel Voltage Inverter Using MPC Controller

In this article, a single-phase five-level voltage inverter topology with six switches is suggested for renewable energy applications. Control inverters that are low-cost, highly efficient, and resilient are required for modern renewable energy grids. The basic goal is to collect as much power as possible from the sources and feed the current into the grid with as little loss and harmonic distortion as possible. The suggested inverter’s low switch count (six switches) with only two switches switched ON in a single state greatly decreases switching and conduction losses. Furthermore, a multi-error feedback controller with modified sinusoidal pulse width modulation (SPWM) is presented to manage the switching operation and maintain the output voltage in the appropriate range under different input and output voltage conditions. Furthermore, the lack of a clamping diode and bulky capacitors in the topology allows it to be controlled using a simple model predictive controller, lowering the cost of the circuit and improving the overall efficiency of the five-level inverter. Furthermore, for the variable input waveform, the total harmonic distortion (THD) evaluated by FFT analysis is within the allowed range of 1.8–4.5%. To validate the originality of this study, simulation and hardware results are provided and compared with current topologies.