An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study

— The objective of this paper is to investigate the Model Predictive Control (MPC) strategy, analyze and compare the control effects with Proportional-Integral-Derivative (PID) control strategy in maintaining a level of spherical tank system using VFD. An advanced control method, MPC has been widely used and well received in a wide variety of applications in process control, it utilizes an explicit process model to predict the future response of a process and solve an optimal control problem with a finite horizon at each sampling instant. In this paper, an algorithm is developed for controlling the speed of the pump using VFD to maintain the level of the spherical tank system. Next, the system is modeled and linearized for analysis and design. Model is implemented in a simulation environment based on LabVIEW. Both MPC and PID control methods are tried to design the controller for the water level system in a spherical tank and compared the results in terms of peak time, settling time, overshoot, and steady-state error under various operational conditions including time delays. The results showed the advantage of MPC for dealing with the system dynamic over PID and could be designed for more complex and fast system dynamics even in presence of constraints and Reduction of power consumption by the pump due to usage of VFD for controlling level by varying the speed of the pump based on the level of the tank. Keywords: Non Linear - Spherical Tank System – Level Control – Variable Frequency Drive – PID Controller – MPC Controller – Modeling - LabVIEW. Cite this Article R. Dhanasekar, S. Vijayachitra, M. Pravin Savaridass. Controlling of Level Process in Nonlinear System Using Variable Frequency Drive. Current Trends in Information Technology. 2019; 9(1): 24–33p.

This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints.

Nowadays, a model predictive control has gained a lot of attractions for power converter. To overcome the defects of the conventional model predictive control and achieve better performance, this article presents a multivector model predictive power control (MV-MPPC) with low computational burden for the grid-tied quasi-Z-source inverter (qZSI). Compared with the conventional model predictive power control (MPPC) that adopts only one vector in one control period, the proposed MV-MPPC applies several vectors including two active vectors, one null vector, and one shoot-through (ST) vector to fulfill the expected performance for the grid-tied qZSI. By calculating the optimal duty ratios of the ST vector, the inductor current ripple can be greatly reduced. To eliminate the weighting factor, a modified sliding-mode control method for simultaneously controlling the capacitor voltage and the inductor current to their setting points is proposed. Furthermore, a quick and effective optimal sector selection method is given, which chooses the optimal sector through a look-up table instead of a bunch of complex calculations. Hence, the computational burden of the proposed MV-MPPC is decreased with significance. Both simulation and experimental results are shown to validate the effectiveness and the advantages of the proposed method.

Many robust model predictive control (MPC) schemes require the online solution of a computationally demanding convex program. For deterministic MPC schemes, multiparametric programming was successfully applied to move offline most of the computation. In this paper, we adopt a general approximate multiparametric algorithm recently suggested for convex problems and propose to apply it to a classical robust MPC scheme. This approach enables one to implement a robust MPC controller in real time for systems with polytopic uncertainty, ensuring robust constraint satisfaction and robust convergence to a given bounded set

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.

This letter presents a novel model predictive control strategy without involving any proportional-integral-differential regulators for practical renewable energy based ac microgrids. The proposed method consists of a model predictive power control (MPPC) scheme and a model predictive voltage control (MPVC) scheme. By controlling the bidirectional buck-boost converters of the battery energy storage systems based on the MPPC algorithm, the fluctuating output from the renewable energy sources can be smoothed, while stable dc-bus voltages can be maintained as the inverters' inputs. Then, the parallel inverters are controlled by using a combination of the MPVC scheme and the droop method to ensure stable ac voltage output and proper power sharing. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation on MATLAB/Simulink and on real-time laboratory platform.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

In power system operation and control, automatic generation control (AGC) scheme is used to maintain the balance in between the net generation and the total load in a system by supplying sufficient power with good quality. Controller plays an important role in AGC scheme. This paper presents a control scheme based on model predictive control (MPC) concept for multiarea power system in deregulated scenario. For guaranteed stability each area controller is designed independently. MPC controller is designed in such a way that for any load perturbation, generators can regulate the real power output to settle down deviations in frequency and tie line power exchange at desired values. The proposed MPC control scheme has been implemented for deregulated two area thermal power system. The performance of MPC controller is also checked for 75 bus Indian power system. Various load conditions has been considered to evaluate the performance of controller. The results and performance analysis are presented to justify the robustness & effectiveness of control scheme. The comparative performance analysis of MPC scheme and conventional proportional integral controller (PID) control is also presented.

To solve the problems of slow response speed of the permanent magnet synchronous motor (PMSM) system when the external interference occurs and the poor anti-interference ability, this paper proposes a double model predictive control (MPC) strategy for PMSM to control speed and current. To be specific, firstly, the mathematical model of PMSM is established under the two-phase synchronous rotation coordinate system. Secondly, we propose the double MPC strategy where MPC controller is used in the speed outer loop and deadbeat prediction controller is used in the current inner loop. Finally, By comparing the traditional control strategy (speed outer loop PI controller and current inner loop deadbeat prediction controller) with the double MPC strategy, the simulation results show that the double MPC has no overshot, better steady-state performance, faster dynamic response and stronger anti-load disturbance capability, which proves the effectiveness of the proposed strategy.

Existing control strategies of modular multilevel converter (MMC) have many drawbacks, such as need of several parameter controllers, complex parameter tuning and huge computational burden. This paper proposes a novel control method based on the hybrid architecture of proportional integral (PI) control and model predictive control (MPC). The PI controller is used as external controllers to control the ac-side power and current, while the MPC controller is used to control the internal features of the MMC, i.e. suppressing the circulating current and balancing the capacitor voltages. The proposed hybrid method takes both advantages of PI and MPC strategies, which lightens the computational burden and improves dynamic performance and steady-state accuracy. The validity of the proposed method is verified by the simulation results of a 11-level MMC system using Matlab/Simulink.

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPC's solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

Two-level voltage source inverters (2L-VSIs) with output LC filters are widely used in uninterruptible power supplies, distributed power generation, and microgrids. This paper proposes a novel sequential modulated model predictive voltage control scheme to control the load voltages and inverter currents of the 2L-VSI in a decoupled manner without the need for weighting factors. In this proposed scheme, two cost functions, each targeting the load voltages and inverter currents of the 2L-VSI, are deployed sequentially, rather than integrated into a dual-objective cost function. This eliminates the need for a weighting factor to balance the two control objectives while retaining the benefits of a modulated model predictive voltage control scheme such as constant switching frequency, fast transient response, lower total harmonic distortion, and reduced steady-state error. The performance of the proposed control scheme is validated through MATLAB simulations on a 10 kW system with linear and nonlinear loads and compared with the classical modulated model predictive voltage control.

<div class="section abstract"><div class="htmlview paragraph">This paper presents the design, implementation, and performance evaluation of a reduced complexity algorithm for a predictive control which is based on our previously published SAE paper (2021-01-0225) titled, “Model Predictive Control for Engine Thermal Management System.” That paper presented a model predictive control (MPC) design concept and demonstrated energy efficiency improvements by enabling engine pre-cooling based on GPS/Navigation data to recognize future vehicle speed limit and road grade in anticipation of high engine load demand.</div><div class="htmlview paragraph">When compared to conventional control, the predictive control demonstrated considerable energy and fuel savings due to delayed timing of both knock mitigation and activation of radiator cooling fan during high engine load demand. However, this predictive control strategy is much more complicated due to its highly coupled nonlinear behavior. Also, in reality, the previous developed MPC strategies are limited to the computational resources in engine control units (ECU). Therefore, to address these challenges, a reduced-complexity MPC controller for the powertrain thermal system is developed in this paper where, by “reduced-complexity,” it is meant that the MPC controller achieves control objectives and to be executed on a modern ECU within a computation budget.</div><div class="htmlview paragraph">To maximize fuel economy, one of the key control logics in the previously published SAE paper is to use estimated radiator outlet coolant temperature as an indicator to determine when to lower the target engine coolant temperature. Since the heat transfer coefficients (HTC) of the thermal system are time varying, the computation load is high if the inputs to the plant models are physics-based. Therefore, lookup table (LUT)-based and mean value model (MVM) modeling approaches are developed and combined in order to reduce computational burden.</div><div class="htmlview paragraph">The complexity reduction is obtained via lookup tables and mean value models, which lightens the computational load of the algorithm with a minimal loss in precision. Experimental results showed that the proposed approach is able to deliver performance similar to originally proposed approaches. At the same time, the proposed algorithm is able to cut the computational complexity of the physics-based algorithm by an 18% factor.</div></div>

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control