Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

Model-based network-wide optimizations in real-time emergency control of power systems are largely bottlenecked by the fast computational requirements. Furthermore, the operational aspects of modern power and energy systems are going through transformative changes to tackle global renewable energy expansion-related issues. Decentralized operations in power systems are becoming essential with the influx of distributed energy resources (DERs), dynamic loads, and electric vehicles (EVs). This research considers a well-known model-based strategy, Model Predictive Control (MPC), for real-time operation and controls of power systems and proposes novel methods utilizing machine learning, data-driven methods, and operator theory to solve computational challenges by making those real-time for the first time. Additionally, we develop a distributed MPC framework to address the recent needs of power systems' operation and control. In Part I, we first present a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. This method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times. Next, we present an alternative data-learned linear Koopman embedding approach for model predictive emergency voltage control in a power grid. This approach involves a "basis-dictionary free" method to lift the nonlinear power system dynamics to a higher dimensional linear space where MPC is exercised in a real-time fashion. We propose a novel data-driven encoder-decoder architecture, Koopman-inspired deep neural network (KDNN), which supports end-to-end learning from trajectory data. The MPC optimization in linear space makes the problem scalable and rapid for real-time implementations. Part II of this research focuses on distributed MPC design for quasi-static power flow-based voltage control problems under short-term (15-minute) variations of load and generation profiles. To realize the proposed distributed control, first, a centralized voltage control problem assuming grid observability is formulated and next transferred to the distributed versions based on both bus-wise and area-wise decomposition of the network. Distributed versions are solved via the alternating direction method of multipliers (ADMM), utilizing only neighbor-to-neighbor communications to attain optimal solutions. The implementation, efficacy, performance, and robustness of proposed research approaches are validated with IEEE test system benchmarks.

The research object of this paper is single-phase PWM rectifier, the purpose is to reduce the total harmonic distortion (THD) of the grid-side current. A model predictive current control (MPCC) with fixed switching frequency and dead-time compensation is proposed. First, a combination of an effective vector and two zero vectors is used to fix the switching frequency, and a current prediction equation based on the effective vector’s optimal action time is derived. The optimal action time is resolved from the cost function. Furthermore, in order to perfect the established prediction model and suppress the current waveform distortion as a consequence of the dead-time effect, the dead-time’s influence on the switching vector’s action time is analyzed, and the current prediction equation is revised. According to the experimental results, the conclusion is that, firstly, compared with finite-control-set model predictive control, proportional-integral-based instantaneous current control (PI-ICC) scheme and model predictive direct power control (MP-DPC), the proposed MPCC has the lowest current THD. In addition, the proposed MPCC has a shorter execution time than MP-DPC and has fewer adjusted parameters than PI-ICC. In addition, the dead-time compensation scheme successfully suppresses the zero-current clamping effects, and reduce the current THD.

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.

Model predictive control (MPC) as a nonlinear control method, is being studied for a wide variety of power electronics applications targeting various power ranges. The fast dynamic response, robustness under different load conditions, multi-objective control, and simplicity of implementation are the main features of MPC. However, the implementation of finite control set MPC (FCS-MPC) or direct MPC in its basic form for multilevel converters (MLCs) demonstrates the inability to generate the staircase multilevel output voltage waveform in a voltage sourced inverter (VSI) application. Adding a reference evaluation block to the basic FCS-MPC method can significantly improve its multilevel voltage generation capability. In this paper, an enhanced model predictive nearest level control (EMP-NLC) is proposed and implemented on a 5-Level flying capacitor multilevel converter (FCMC). The proposed method is simulated under different load conditions, while a comparison against existing MPC techniques is also presented. Experimental results verified the efficacy of the proposed controller.

Model predictive control (MPC) presents important advantages in the control of the power converter and drives such as, fast dynamic response and capability to include nonlinear constrains. These have positioned MPC as a powerful and realistic control strategy, however, it also has disadvantages such as variable switching frequency and parameter sensitivity. This paper applied a modulated model predictive speed control that guarantees a fix switching frequency and, thanks to disturbance compensation, robustness to parameters variation. The strategy is validated and compared to finite set model predictive speed control through simulation results.

Under unbalanced dc links, the control of a three-phase Vienna rectifier is challenging. To deal with the unbalanced dc links, the optimal switching sequence model predictive control (MPC) regulates the neutral-point (NP) voltage through preselecting a redundant vector and grid current by reconstructing duty cycles of the optimal switching sequence. However, its one-redundant-vector-per-control-interval characteristic leads to large NP voltage ripple. To solve this problem, in this article, a model predictive duty cycle control method is proposed for three-phase Vienna rectifiers under unbalanced dc links. First, we study the impacts of duty cycles on the variations of instantaneous current, and then, the three-phase duty cycles are derived by predefined objective function minimization. Second, considering both the operational characteristics and unbalanced dc links of the Vienna rectifier, a new modification method for duty cycles is presented. This method ensures the grid current quality and decouples the control of grid current and NP voltage. Finally, an MPC-based NP voltage control strategy is proposed to reduce the NP voltage ripple. Experimental results demonstrate that the presented method can effectively reduce the NP voltage ripple.

In this article, sensorless finite control set model predictive speed control of induction motor using adaptive full-order observer is proposed. Firstly, finite control set model predictive speed control is introduced. It is composed of direct speed control and model predictive control, which gets over the limitations of cascaded structure and obtains high dynamics response. Due to lack of speed loop, stator flux linkage, rotor speed and stator current are taken as the control targets in the cost function. Then, adaptive full-order observer is used to estimate rotor speed and stator flux linkage to improve the reliability and keep the system cost down. Finally, the simulation results indicate that the proposed method has well dynamic performance in the wide speed range.

The objective of this paper is to introduce with a quaternion orientation based quadrotor that can be controlled by Model Predictive Control (MPC). As MPC offers promising performance in different industrial applications, quadrotor can be another suitable platform for the application of MPC. The present study consistently adopts unit quaternion approach for quadrotor orientation in order to avoid any axes overlapping problem, widely known as singularity problem whereas Euler angle orientation approach is unable to resolve so. MPC works based on the minimal cost function that includes the attitude error and consequently, the cost function requires quaternion error in order to proceed with process of MPC. Therefore, the main contribution of this study is to introduce a newly developed cost function for MPC because by definition, quaternion error is remarkably different from the attitude error of Euler angle. As a result, a unit quaternion based quadrotor with MPC can ascertain a smooth singularity-free flight that is influenced by model uncertainty. MATLAB and Simulink environment has been used to validate the cost function for quaternion by simulating several trajectories.

In this study, model predictive control (MPC) of the three‐phase split capacitor (TPSC) distribution static compensator (DSTATCOM) using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is proposed. Power quality improvement using MPC involves the selection of switching state which minimises the difference between reference and actual DSTATCOM currents. However, the limitation of MPC is higher switching frequency and limitation of TPSC DSTATCOM is voltage divergence of dc‐link capacitors. To alleviate these problems, one constraint is to suppress the voltage divergence and the other is to reduce the switching frequency is included in the cost function using weighting factors. Optimal selection of switching state for minimisation of a multi‐constraint cost function is depending on the weighting factor; however, its tuning is a challenging task. In this study, the simplification of weighting factor tuning is achieved using the VIKOR method and it further selects the optimal switching state based on a specific measure of closeness to the ideal solution and multi‐criteria ranking index. The advantages of the proposed work are compensation of power quality issues, balance the dc‐link voltages, switching frequency reduction of inverter and simplification of weighting factor tuning. Finally, the proposed work is evaluated using simulation and experimental studies.

This paper compares two torque control methodologies, namely Field Oriented and Model Predictive ones, for monitoring Interior Permanent Magnet Motor (IPMM) over a wide speed range. IPMM magnetic saturation and cross coupling effects are taken into consideration, increasing significantly the precision of the developed controllers. Maximum Torque per Ampere (MTPA) and Field Weakening (FW) operating modes below and above the rated speed, respectively, are considered for an Electric Vehicle (EV) application. In the Field Oriented Control (FOC) case, this is achieved by implementing convenient Look-Up Tables (LUT) and PI controllers, while in the Model Predictive Control (MPC) case it is obtained via a particular cost function. Moreover, in the MPC case, an additional power loss tracking factor is introduced in the cost function, in order to minimize the total motor losses over the entire drive cycle. The proposed control techniques are evaluated under both steady state and dynamic conditions, illustrating the relative advantages of MPC in terms of robustness and dynamic behavior.

The need for cost-effective fuel economy improvements has driven the introduction of automatic transmissions with an increasing number of gear ratios. Incorporation of interlocking dog clutches in these transmissions decreases package space and increases efficiency, as compared to conventional dry or wet clutches. Unlike friction based clutches, interlocking dog clutches require very precise rotational speed matching prior to engagement. Precise engine speed control is therefore critical to maintaining high shift quality. This research focuses on controlling the engine speed during a gearshift period by manipulating throttle position and combustion phasing. Model predictive control (MPC) is advantageous in this application since the speed profile of a future prediction horizon is known with relatively high confidence. The MPC can find the optimal control actions to achieve the designated speed target without invoking unnecessary actuator manipulation and violating hardware and combustion constraints. This research utilizes linear parameter varying (LPV) MPC to control the engine speed during the gearshift period. Combustion stability constraints are considered with a control oriented covariance of indicated mean effective pressure model (COV of IMEP). The proposed MPC engine speed controller is validated with a high-fidelity 0-dimensional engine model with crank angle resolution. Four case studies, based on simulation, investigate the impact of different MPC design parameters. They also demonstrate that the proposed MPC engine controller successfully achieves the speed reference tracking objective while considering combustion variation constraints.

To improve the unit load following capacity of a large-capacity coal-fired supercritical (SC) power unit under automatic generation control (AGC), an intelligent model predictive optimal control (MPOC) scheme is proposed. It employs a nonlinear autoregressive moving average (NARMA) neural network model as the prediction model of the energy conversion unit and a more efficient simplified particle swarm optimization (PSO) algorithm as the optimal solution search approach. The effectiveness of the proposed MPOC scheme is evaluated by extensive control simulation tests on a commercial-grade simulator of a 600MW SC power unit. It is shown that the proposed approach can greatly improve the load following speed of the supercritical energy conversion unit, and at the same time keeps main steam pressure within safety limits.

A model predictive vertical motion control of a passenger ship

Fifty-seven per cent of all water supply systems in the Netherlands are controlled by model predictive flow control; the other 43% are controlled by conventional level-based flow control. The differences between conventional level-based flow control and model predictive control were investigated in experiments at five full-scale water supply systems in the first half of 2011. Quality parameters of the drinking water and energy consumption of the treatment and distribution processes were measured and analysed. The experiments showed that the turbidity values are 12–28% lower, and particle volume values 12–42% lower for the systems which are controlled by model predictive flow control. The overall energy consumption of water supply systems controlled by predictive flow control is 1.0–5.3% lower than conventional level-based flow controlled systems, and the overall energy costs are 1.7–7.4% lower.