Decision-Making-Based Multiobjective Direct Speed Predictive Control Without Weighting Factors

Since more than 20 years the so-called "field oriented control" is state-of-the-art for electrical drives. Strategies based on this principle fulfil nearly all demands of drive technology today. Due to its basic ideas, the performance of field oriented control cannot be improved without limitations. Furthermore, linear controllers always produce a continuously valued output. Hence, for controlling an inverter additional components, which discretise the output of the controller, have to be inserted. Predictive controllers, which allow to overcome this drawback, are known in drive control for quite a long time. Nevertheless all predictive control schemes proposed for controlling electrical drives until today only precalculate the system behaviour for one single future sampling cycle. Powerful control strategies like model predictive control, well-known in chemical engineering, allow higher prediction horizons, but these strategies have not yet been used for drive control. This paper proposes a new predictive controller for electrical drives based on model predictive control, which allows optimal control of the inverter directly, i.e. without using any kind of modulator. In contrast to all predictive strategies published so far for drive control, the new controller optimises the control action not only for the next sampling period, but for a longer prediction horizon

Conventional finite control set model predictive direct power control for grid-connected inverter causes high current harmonics and power fluctuations, during per control cycle, only one voltage vector is used. To address this issue, this paper introduces a hybrid vector model predictive direct power control (HV-MPDPC) strategy. A power prediction model is established in accordance with the instantaneous power theory. Then, eighteen alternative hybrid voltage vector combinations are formed by integrating double-vector and triple-vector combinations. Through cost function-driven selection of the optimal voltage vector combination, the power control performance and power quality are enhanced. The proposed strategy's effectiveness is validated by simulation results.

This paper proposes a self-tuning model predictive direct power control (MPDPC) strategy for power flow control and power quality improvement in grid-connected power converters. At each sampling instant, a fuzzy logic controller is used to determine online the best weighting factor values for a given operating point. These values are then used to solve the multi-objective optimal control problem associated to the MPDPC. The optimal solution that minimizes the multi-objective cost function is chosen as the input (power switch state). The proposed method is examined through a case study and verified numerically via MAT LAB SIMULINK. A comparative study is conducted to demonstrate the effective performance of this approach. As a result of the proposed weighting factor online tuning, an improved performance in terms of total harmonic distortion and average switching frequency is attained when compared with fixed weighting factors.

This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance.

In this paper, a direction-of-arrival (DOA)-based factor graph (FG) technique is proposed for three-dimensional (3D) tracking. Multiple sensors are utilized in this system, which could measure both azimuth and elevation DOAs emitted from an anonymous target. To realize non-linear tracking, a modified extended Kalman filter (EKF) is proposed. Specifically, on the one hand, the proposed EKF observer is no longer independent of the EKF predictor, but instead takes the predicted target location into its operation. With this technique, the accuracy of detection is improved while the computational complexity is dramatically reduced. On the other hand, the variance of the EKF observer error is estimated in real time, based on the predicted Cramer-Rao bound (PCRB). Therefore, the robustness of detection can be guaranteed even with an unstable sensing environment. In this sense, the EKF observer and the EKF predictor are operated in an integrated FG framework. The advantages of the proposed technique are verified by both complexity analyses and simulation results.

This paper presents a novel hybrid model predictive multilayer control (HMPMC) strategy for modular multilevel converters (M2LCs). The proposed hybrid strategy uses a modified model predictive direct slope control (MPDSC) concept to regulate the load current while concurrently minimizing the switching frequency, circulating current, and capacitor voltage variations through a new MPMC concept. The multilayer philosophy uses dedicated control layers to independently evaluate its control variables. As the main advantage, the hybrid strategy enables operation of M2LC at low switching frequencies. Moreover, this paper proposes a hierarchical screening algorithm, which estimates the minimum number of switching states required to be evaluated for each control variable, reducing the computational burden. An execution order for each control layer, in which the variables are assigned to control layers as appropriate, is also incorporated to further reduce the computational burden. Instead of using conventional weighting factors, this paper uses tuning factors for each layer, which is based on the number of switching states of the lowest cost to make the tuning process efficient and adaptable to any operating conditions. To demonstrate the validity, a prototype single-phase, five-level, 380-VA M2LC is designed, built, and controlled through the proposed strategies. The performance of prototype is investigated under both the steady-state and transient conditions, and the experimental results are presented in comparison with simulations and benchmarked against both the conventional finite control set model predictive control (FCS-MPC) and an improved MPC strategy. The results clearly indicate that the proposed strategy enables efficient operation of the converter at both significantly lower switching frequency and reduced computational burden, which is preferred for high-power applications.

To address the complexity of vector selection in the conventional predictive current control strategy and the reduction of control system robustness when the motor parameters change, this paper proposes a finite-set model-free three-vector predictive current control method for permanent magnet synchronous motor based on the improved sliding-mode perturbation observer. According to the mathematical model of a permanent magnet synchronous motor under perturbation, a new hyperlocal model is established, and the conventional vector traversal optimization method is optimized to obtain the necessary basic voltage vector by analyzing the function with one judgment. Meanwhile, the sliding mode perturbation observer is designed using the fast exponential convergence law to observe the unknown part. Finally, by experimentally contrasting the proposed technique with the conventional three-vector model predictive current control, the method suggested in this paper can successfully reduce the current pulsation and inhibit the system perturbation caused by the parameter change, ensuring the steady state performance of the motor, as demonstrated by the experimental comparison with the conventional three-vector model predictive current control method.

In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive optimal control of discrete-time nonlinear systems under stochastic disturbances. The proposed LPC scheme is different from conventional model predictive control (MPC), which uses open-loop optimization or simplified closed-loop optimal control techniques in each horizon. In LPC, the control task in each horizon is formulated as a closed-loop nonlinear optimal control problem and a finite-horizon iterative reinforcement learning (RL) algorithm is developed to obtain the closed-loop optimal/suboptimal solutions. Therefore, in LPC, RL and adaptive dynamic programming (ADP) are used as a new class of closed-loop learning-based optimization techniques for nonlinear predictive control with stochastic disturbances. Moreover, LPC also decomposes the infinite-horizon optimal control problem in previous RL and ADP methods into a series of finite horizon problems, so that the computational costs are reduced and the learning efficiency can be improved. Convergence of the finite-horizon iterative RL algorithm in each prediction horizon and the Lyapunov stability of the closed-loop control system are proved. Moreover, by using successive policy updates between adjoint time horizons, LPC also has lower computational costs than conventional MPC which has independent optimization procedures between two different prediction horizons. Simulation results illustrate that compared with conventional nonlinear MPC as well as ADP, the proposed LPC scheme can obtain a better performance both in terms of policy optimality and computational efficiency.

Introduction: With the increase of large-scale wind power grid integration requirements on power system stability, the problem of insufficient high and low voltage ride-through performance of Doubly-Fed Induction Generator (DFIG) is becoming more prominent. Improving the high- and low-voltage ride-through performance of the DFIG by enhancing its control strategy has become a key technology path to enhance the grid adaptability of wind power systems. Therefore, this article proposes two novel control strategies for the Parallel Grid-Side Converter (PGSC), designed to enhance the high- and low-voltage ride-through capabilities of the DFIG. Method: This article establishes a mathematical model of PGSC, introduces model predictive control and direct current control theory, and proposes two predictive control strategies: one is a predictive control scheme based on PGSC topology, and the other is a predictive model based on direct current control principle. Results: Through MATLAB/Simulink simulation analysis, the above two control schemes were compared with conventional PI control for high and low voltage ride-through, and an effective predictive control scheme was derived. Discussion: During three-phase voltage sags, the PGSC topology-based predictive control scheme reduces DC bus voltage fluctuation by 81.8% and 86.2% compared to direct current control-based and conventional PI methods, respectively, while improving transient acceleration performance by 58.4% and 60.9% over the same counterparts. Conclusion: The prediction scheme based on the PGSC topology structure can effectively track the reference value, suppress the phenomenon of DC bus overvoltage, and accelerate the transient process of the grid-side voltage and current.

Good vehicle handling characteristics are essential to ensure traffic safety. In the integrated system of electric power steering (EPS) and active front steering (AFS), the steering wheel torque may change abruptly with the interference of active steering. The additional front road wheel angle generated by AFS gives rise to the change of tyre lateral force, which results in the change of steering wheel torque finally. Although the tyre forces are difficult to measure, the extended Kalman filter (EKF) algorithm can be used to estimate it. The problem of steering wheel torque change can be resolved by tyre forces estimate based on the EKF observer in nonlinear system. The vehicle nonlinear mathematical model is presented and a nonlinear tyre lateral force estimator is also established basing on EKF. The estimated tyre lateral forces can be applied to compensate the abrupt change of steering wheel torque, which realises the combination of vehicle states estimation and vehicle control. The simulation results in MATLAB/Simulink show the effectiveness of proposed control strategy.

A long-horizon hybrid data-model predictive direct power control (HD-MPDPC) scheme is proposed. The hybrid control scheme is for grid-side power electronic converters (PEC) in grid-connected renewable energy sources (RES). Computational burden of conventional MPDPC is alleviated by reducing the number of candidate voltage vectors to be examined in the cost function. This is achieved by employing data-driven forecast of RES output power to identify and eliminate the voltage vectors that do not counteract active and reactive power errors appropriately. RES power is forecasted using recurrent neural networks. The method for eliminating voltage vector candidates is inspired from direct power control, where output of active and reactive power hysteresis controllers along with the sector in which grid voltage vector lies are used to determine the switching states of the grid-side PEC. Thanks to reduced computational complexity, the hybrid predictive control scheme dispatches RES power more reliably over long horizons. This in turn enables RES as a regulating reserves service provider in power systems. Hardware-in-the-loop studies of a grid-connected wave energy conversion system verify reduced computational complexity of HD-MPDPC and its effectiveness in controlling RES output power over long horizons.

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.

The recent use of hybrid renewable energy systems (HRESs) is considered one of the most reliable ways to improve energy access to decentralized communities because of their techno-economic and environmental benefits. Many distant locales, such as camps in war-torn nations, lack basic necessities like power. This study proposes a remedy for power outages in these areas; by designing an HRES and a control system for monitoring, distributing, and managing the electrical power from sustainable energy sources to supply the load. Hence, providing affordable, reliable, and clean energy for all (Sustainable Development Goal 7). In this study, the feasibility and techno-economic performance of an HRES for a refugee camp was evaluated under load following (LF), cycle charging (CC), and predictive control strategy (PS). The optimization results revealed that the PS was the most suitable, as it had the lowest cost and was more eco-friendly and energy-efficient. The predictive control strategy had a 48-h foresight of the load demand and resource potential and hence could effectively manage the HRES. The total net present cost (NPC) for the electrification of this refugee camp was $3,809,822.54, and the cost of electricity generated for every kWh is $0.2018. Additionally, 991,240.32 kg of emissions can be avoided annually through the hybridization of the diesel generator under the PS.

Model-based predictive direct control methods are advanced control strategies in the field of power electronics. To control an induction machine (IM), the predictive torque control (PTC) method evaluates the electromagnetic torque and stator flux in the cost function. The switching vector selected for the use in the insulated gate bipolar transistors (IGBTs) minimizes the error between references and the predicted values. The system constraints can be easily included. The predictive current control (PCC) strategy assesses the stator current in the cost function. The weighting factor is not necessary. Both the PTC and PCC methods are very useful direct control methods that do not require the use of a modulator. In this paper, the PTC and PCC methods are carried out experimentally for an IM on the same test bench. The behaviors and the robustness in steady state and the performances in transient state are evaluated.

Extended Kalman filter (EKF) has been widely applied for sensorless direct torque control (DTC) in induction machines (IMs). One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF) based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF) which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. Simulation and experimental results verify the performance of the proposed TEKF estimator for DTC of IMs.