Multi-step Predictive Control Research Articles

Six-Step Operation With Multistep Predictive Control Using the Trapezoidal Method for Traction PMSM Drives

Six-Step Operation With Multistep Predictive Control Using the Trapezoidal Method for Traction PMSM Drives

Multistep Predictive Current Control for Electrical Drives With Adaptive Horizons

Since the number of candidate vectors is increased in the multi-step model predictive control (MMPC) strategy, its calculation burden becomes a serious problem in implementation. To make full use of the limited processor resources effectively and improve control performances, a MMPC with the adaptive prediction horizon (PH), control horizon (CH) and online selecting weighting factor is proposed in this paper and applied to a PMSM driving system as the current controller. A hysteresis principle is designed to judge the current operating state to online adjust PH and CH in the MMPC within the normal timescale, and a real-time branch and bound algorithm is designed to online select a suitable weighting factor based on the over-timescale caused by the long CH. More effective availability of the processor resources by the proposed method is analyzed and its advantages including improved current impact, reduced switching frequency, and decreased influences of some parameter mismatches with suitable stator current quality are demonstrated by the simulation and experimental results compared to some conventional control strategies.

Improved multistep model predictive control method with reduced finite set for MMC-UPQC

Multistep model predictive control (M-MPC) can improve control accuracy but leads to an increased computational burden. To reduce the computational burden and restore power quality, an improved M-MPC method with reduced finite set for unified power quality conditioner (UPQC) based on modular multilevel converter (MMC) is presented in this paper. First, a finite set of M-MPC is formed according to the inserted sub-modules (SMs) number calculated from cost functions. Second, we investigate the relationship between the inserted SMs number and the bridge arm output voltages and find that the inserted SMs number will increase as voltages rise. As such, by judging the change rate of the output voltages, switching states that cannot be realized within the finite set can be eliminated, thus the finite set can be reduced. In addition, circulating currents and capacitance voltage control methods are designed to ensure the internal stability of MMC. Finally, the MATLAB/Simulink simulation and the real time laboratory (RT-LAB) based hardware-in-the-loop (HIL) experimental results show effectiveness of the proposed M-MPC. The results show the proposed M-MPC exhibits better dynamic characteristics and fewer control options as compared to the single-step MPC.

Physics-Based Model Predictive Control for Power Capability Estimation of Lithium-Ion Batteries

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power, but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate.

Adaptive Integral Extended State Observer-Based Improved Multistep FCS-MPCC for PMSM

In this article, an improved multi-step finite control set model predictive current control (FCS-MPCC) based on adaptive integral extended state observer (AIESO) is proposed for permanent magnet synchronous motor (PMSM). The improved multi-step FCS-MPCC based on sector is introduced in the current loop. The elements of the voltage vector set are reduced with different sector division method, so it could reduce the computational burden to some extent. Meanwhile, considering that the high gain extended state observer (ESO) will obtain faster convergence, better tracking accuracy in theory and the system disturbance rejection can be enhanced, but the noise suppression performance will become poor. Thus, the adaptive extended state observer (AESO) is proposed to balance the disturbance rejection and noise suppression. When system subject to disturbance, the adaptive gain will increase to enhance the system disturbance rejection and become small to improve the noise suppression in steady-state. However, disturbed by time-varying disturbance, the small gain in the steady state will lead to poor steady-state tracking accuracy. To improve the steady-state tracking accuracy, AIESO is proposed by adding the integral term into AESO. Finally, in order to avoid the sector misjudgment caused by parameter mismatch, the strategy based on extended state observer is used for disturbance compensation. However the selection of inductance is closed related to the estimated burden of the observer. Thus, the inductance parameter estimation method is proposed to update the initial inductance value to reduce the estimated burden, which can help to suppress the parameter mismatches with a smaller estimated burden of the observer. The simulation and experimental results verify the effectiveness of the proposed method.

Adaptive multistep model predictive control for tubular grid-connected solid oxide fuel cells

In the research of renewable energy power generation, tubular grid-connected solid oxide fuel cells with the apparent advantage in voltage regulation have been widely applied in power systems. Recently, a model predictive control has been applied to consider the nonlinear constraints of tubular grid-connected solid oxide fuel cells, which cannot be considered by a proportional-integral-derivative controller. Both model predictive control and proportional-integral-derivative controller achieve only 80% fuel efficiency. An adaptive multistep model predictive control (AMMPC) is proposed to improve the fuel efficiency of tubular grid-connected solid oxide fuel cells and simultaneously consider systemic thermodynamics and electrochemistry constraints. The AMMPC contains the advantages of adaptive control and multistep model predictive control. Both adaptive two-step model predictive control and three-step model predictive control are designed for tubular grid-connected solid oxide fuel cells. With the more accurate prediction ability, the AMMPC improves the fuel efficiency of tubular grid-connected solid oxide fuel cells with higher fuel efficiency (86.5%) than model predictive control (80%) and proportional-integral-derivative (80%). Both feasibility and effectiveness of the AMMPC are verified with high fuel efficiency under simple and complex power demands cases.

Low-Complexity Multistep Sequential Model Predictive Current Control for Three-Level Inverter-Fed Linear Induction Machines

This paper proposes a low-complexity multistep sequential model predictive current control (M-SMPCC) for three-level inverter-fed linear induction machine (LIM) drives to further improve the steady-state drive performance at low switching frequency. In order to implement multistep MPC for performance improvement, the idea of reducing the search space to fewer voltage vectors (VVs) is adopted to reduce its dramatically increasing complexity. However, there are some problems of the existing simplified methods directly applied for long prediction horizons. First, the reference VV in existing methods cannot consider the performance of long prediction horizons, so a reference VV sequence considering current tracking performance and switching frequency is derived to guide the optimal VV search. Second, with the existing search methods, some VVs that yield better performance in long prediction horizons may be excluded in advance. Hence, a more detailed region division and search method is designed. Moreover, to address the inherent neutral point voltage (NPV) fluctuation in the topology, one sequential optimization strategy is proposed, in which the current tracking performance and NPV balance are guaranteed by two separate cost functions. Finally, the proposed method has validated through comprehensive simulation and experiments, showing lower current harmonics and thrust ripples at low switching frequency.

Multi-step depth model predictive control for photovoltaic maximum power point tracking under partial shading conditions

This work avoids the photovoltaic array operating point falling into local maximum power points (MPPs) and optimizes the dynamic process of photovoltaic array global MPP tracking under partial shading conditions (PSCs) by the proposed multi-step depth model predictive control (MDMPC). The MDMPC updates the dynamic conductance at different moments to achieve multi-step accurate predictions of the system state. Combining deep neural networks and the global MPP calculation model can rapidly estimate the global MPP under different topologies. The proposed two-stage optimization adaptively adjusts the optimization function according to the purpose of different stages. The simulated system response curves and performance indexes under different PSCs show that the MDMPC can avoid the operating point falling into local MPPs and accurately rapidly track to the global MPP with higher power generation efficiency. In addition, the hardware-in-the-loop experimental results verify that MDMPC can rapidly and accurately track the global MPP under three PSCs.

Sensitivity updates for linear-quadratic optimization problems in multi-step model predictive control

The paper discusses how parametric sensitivity analysis can be used in certain model predictive control (MPC) schemes. The sensitivity analysis will be performed with regard to the initial state measurement and update schemes will be derived that speed-up the computations. Throughout we restrict the discussion to linear-quadratic optimal control problems in discrete time, which frequently arise in tracking tasks with MPC. The derived tools from sensitivity analysis can be embedded into MPC schemes with a prediction step and multi-step MPC schemes with re-optimization and prediction step. Numerical experiments illustrating the sensitivity analysis are presented.

Model Predictive Variable Impedance Control of Manipulators for Adaptive Precision-Compliance Tradeoff

Impedance control (IC) is widely used in contact-rich manipulator tasks since it provides manipulators with both operation precision and contact compliance, and trades them off by impedance parameters. However, fixed impedance parameters limit the applicability of IC in complex tasks during which the focus on the operation precision or the contact compliance is variable, such as physical human–robot collaboration and complex assembly tasks. This article presents model predictive variable impedance control approaches for adaptive precision-compliance tradeoff to satisfy variable task requirements. Specifically, we establish a novel impedance model, which transforms the variable impedance law design problem into a control law design problem, and allows us to consider novel impedance constraints that can determine manipulators' extreme precision and compliance properties. According to whether state constraints are further considered, one-step (OS) and multistep (MS) model predictive control approaches are proposed to solve these transformed control problems, and the corresponding optimization problem of the OS MPC can be established as a QP problem due to the special form of the novel impedance model. A tank-based approach is further proposed to correct the variable impedance laws for the system passivity concern. Various comparative experiments are conducted to validate the effectiveness of the presented approaches.

MPC-based time synchronization method for V2V (vehicle-to-vehicle) communication

PurposeAs the strategy of 5G new infrastructure is deployed and advanced, 5G-R becomes the primary technical system for future mobile communication of China’s railway. V2V communication is also an important application scenario of 5G communication systems on high-speed railways, so time synchronization between vehicles is critical for train control systems to be real-time and safe. How to improve the time synchronization performance in V2V communication is crucial to ensure the operational safety and efficiency of high-speed railways.Design/methodology/approachThis paper proposed a time synchronization method based on model predictive control (MPC) for V2V communication. Firstly, a synchronous clock for V2V communication was modeled based on the fifth generation mobile communication-railway (5G-R) system. Secondly, an observation equation was introduced according to the phase and frequency offsets between synchronous clocks of two adjacent vehicles to construct an MPC-based space model of clock states of the adjacent vehicles. Finally, the optimal clock offset was solved through multistep prediction, rolling optimization and other control methods, and time synchronization in different V2V communication scenarios based on the 5G-R system was realized through negative feedback correction.FindingsThe results of simulation tests conducted with and without a repeater, respectively, show that the proposed method can realize time synchronization of V2V communication in both scenarios. Compared with other methods, the proposed method has faster convergence speed and higher synchronization precision regardless of whether there is a repeater or not.Originality/valueThis paper proposed an MPC-based time synchronization method for V2V communication under 5G-R. Through the construction of MPC controllers for clocks of adjacent vehicles, time synchronization was realized for V2V communication under 5G-R by using control means such as multistep prediction, rolling optimization, and feedback correction. In view of the problems of low synchronization precision and slow convergence speed caused by packet loss with existing synchronization methods, the observer equation was introduced to estimate the clock state of the adjacent vehicles in case of packet loss, which reduces the impact of clock error caused by packet loss in the synchronization process and improves the synchronization precision of V2V communication. The research results provide some theoretical references for V2V synchronous wireless communication under 5G-R technology.

Data-driven nonlinear predictive control for feedback linearizable systems

We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems. The scheme uses a non-parametric predictive model based only on input and noisy output data along with a set of basis functions that approximate the unknown nonlinearities. Despite the noisy output data as well as the mismatch caused by the use of basis functions, we show that the proposed multi-step robust data-driven nonlinear predictive control scheme is recursively feasible and renders the closed-loop system practically exponentially stable. We illustrate our results on a model of a fully-actuated double inverted pendulum.

On Designing the Event-Triggered Multistep Model Predictive Control for Nonlinear System Over Networks With Packet Dropouts and Cyber Attacks

In this article, the event-triggered multistep model predictive control for the discrete-time nonlinear system over communication networks under the influence of packet dropouts and cyber attacks is studied. First, the interval type-2 Takagi-Sugeno fuzzy model is applied to express the discrete-time nonlinear system and an event-triggered mode, which is capable of determining whether the sampled signal ought to be delivered into the unreliable network, is designed to economize communication resources. Second, two Bernoulli processes are introduced to represent the randomly happening packet dropouts in the unreliable network and the randomly occurring deception attacks on the actuator side from the adversaries. Third, under the assumption that the system states are unmeasurable, a multistep parameter-dependent model predictive controller is synthesized via optimizing one series of feedback laws for a given period of time, which leads to improved control performance than that of the one-step approach. Moreover, the results on the recursive feasibility and closed-loop stability related to the networked system are achieved, which explicitly consider the external disturbance and input constraint. Finally, simulation experiments on the mass-spring-damping system are carried out to illustrate the rationality and effectiveness of the provided control strategy.

Low-Cost Multistep FCS-MPCC for PMSM Drives Using a DC Link Single Current Sensor

In this article, a low-cost multistep finite control set model predictive current control (FCS-MPCC) using a dc link single current sensor is proposed for permanent magnet synchronous motor (PMSM). In order to reduce the system cost, the single dc link current sensor is used to reconstruct the three-phase current. Different from the current reconstruction technology of field-orientated control strategy, there are some problems of the existed current reconstruction technology applied for finite control set model predictive control (FCS-MPC). First, the optimal switching state of FCS-MPC in each cycle is not fixed, and only contains at most one phase current information for reconstruction, which will cause the reconstruction failure. Therefore, a multiperiod sampling method is proposed to obtain the different two-phase current information to reconstruction. Second, the uncertainty of the two-phase reconstruction current interval will lead to current error, so this article proposes a variable period compensation method to solve this problem. Moreover, in order to improve the system closed-loop performance without significantly increasing the computational cost and enhance the system reliability, a low-cost multistep FCS-MPCC strategy based on the reconstructed three-phase current is proposed for PMSM. Finally, simulation and experimental results validate the effectiveness of the low-cost multistep FCS-MPCC method using a dc link single current sensor reconstruction for PMSM.

Dynamic Prediction Model of Silicon Content in Molten Iron Based on Comprehensive Characterization of Furnace Temperature

The silicon content of the molten iron is an important indicator of the furnace temperature trend in blast furnace smelting. In view of the multi-scale, non-linear, large time lag and strong coupling characteristics of the blast furnace smelting process, a dynamic prediction model for the silicon content of molten iron is established based on the analysis of comprehensive furnace temperature characterization data. The isolated forest algorithm is used to detect anomalies and analyze the causes of the anomalies in conjunction with the blast furnace mechanism. The maximum correlation-minimum redundancy mutual information feature selection method is used to reduce the dimensionality of the furnace temperature characterization data. The grey correlation analysis with balanced proximity is used to obtain the correlation between the furnace temperature characterization parameters and the silicon content of the molten iron at different time lags and to integrate the furnace temperature characterization data set. The GRA-FCM model is used to divide the parameter set of the integrated furnace temperature characterization and construct a parameter directed network from multiple control parameters to multiple state parameters. The GWO-SVR model is used to predict the state parameters of each delay step by step to achieve dynamic prediction of the silicon content of the molten iron. Finally, the control parameters are adjusted backwards according to the prediction results of the state parameters and the silicon content of the molten iron and expert experience to achieve accurate control of the furnace temperature. Starting from the actual production situation of a blast furnace, the characteristic parameters are divided into control parameters and state parameters. This model establishes a multi-step dynamic prediction and closed-loop control model of “control parameters-state parameters-silicon content in hot metal-control parameters”.

An intelligent multi-step predictive control method of the Small Modular Reactor

As Small Modular Reactors (SMRs) feature great complexity and uncertainty in response and are varying over time during long-term operation, it is of difficulty to design a well-performed controller that can deal with different working conditions. In this paper, an intelligent multi-step predictive scheme is created to support the development of autonomous control for SMRs. In this scheme, an adaptive prediction model is constructed using T-S fuzzy identification with training data generated by cases of reactor system model, and a real-time adaptive algorithm is utilized to update model parameters to improve accuracy of prediction. Particle swarm optimization (PSO) has also been carried out to search for the optimal decision. The simulation results manifest the effectiveness and the good performance of the proposed approach.

Multistep Model Predictive Control for Electrical Drives—A Fast Quadratic Programming Solution

Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low.

On-Line Multistep Predictive Spectral Control of the Distortion in Switched-Mode Converters

In automotive power electronics, distortion and electromagnetic interference (EMI) generated by the switching action of power semiconductors can be a significant challenge for the design of a compact, lightweight vehicle. As semiconductor switching frequencies increase, e.g., through the introduction of new materials, such as gallium nitride and silicon carbide, this problem becomes more severe. We present a control scheme for an automotive dc-to-dc converter that reduces the EMI generated by shaping switching distortion predictively at the run time. The multistep model-predictive control scheme chooses the subsequent switching state that optimizes the output spectrum according to predefined criteria. To achieve real-time operation, it evaluates the possible switching state candidates for the next modulation step without explicitly solving a single Fourier transform. In addition, the switching rate and voltage ripple are controlled in a single unified control law. We present and experimentally validate that the control scheme can indeed run at real time already with currently available mid-range hardware. The results demonstrate that the largest spectral peak of the switching distortion can be decreased by 48 dB compared to conventional pulse-width modulation. Furthermore, spectral gaps can be implemented in the output distortion and altered in real-time, allowing certain frequency bands—e.g., bands used by other sensitive electronics such as sensors, communication busses, or tuners—to be kept free of EMI from the converter’s switching actions.

Modified multistep model predictive control for three-phase induction motor drive system considering the common-mode voltage minimization

It is acknowledged that the common-mode voltage may have detrimental effects on an induction motor (IM) drive system if not properly addressed. Therefore, in this paper, a modified multistep model predictive control method for IM drive system considering the common-mode voltage minimization is proposed. This research uses a multi-objective cost function, before applying the Sphere Decoding Algorithm to find the optimal control input. The results show that the proposed control method not only reduces the common-mode voltage significantly but also mitigates the computational burden of the microprocessor without affecting the system performance. The proposed control method is simulated by MATLAB-Simulink for an IM drive system with an 11-level cascaded H-bridge inverter.

A novel Multi-step Model Predictive Control design for three-phase T-Type inverter in grid-connected mode

A novel Multi-step Model Predictive Control design for three-phase T-Type inverter in grid-connected mode