Portfolio optimization and model predictive control: A kinetic approach

The purpose of this study was to elucidate whether there is a role in influencing patterns of distribution of dividend stock prices. Data processing methods used were descriptive and comparative analysis. The results of this study indicate that not all issuers listed on the Stock Exchange dividends and the distribution of the dividend were varied. Of the 285 listed companies there are 13 issuers that pay dividends above Rp500 per share and most large issuers that pay dividends (MLBI). Issuers who regularly distribute dividends from 2011-2014 as many as 122 listed companies, with issuers who have an average dividend yield and the standard deviation is SQBB largest and the smallest is the SMMA. Of the 122 listed companies that distribute dividends on a regular basis, issuers that have a relative risk (covariance) is lowest that ASDM. After compared with stock prices, issuers that have a positive correlation between the distribution of dividends and stock prices is larger, ie 75.41%. From this study we can conclude that the theory says that the dividend distribution will affect the stock price can not be generalized. The dividend distribution does not necessarily affect the movement of the stock price still due to the dividend distribution of listed companies is negatively correlated with stock prices. In investing stock investors need not sticking to the distribution of dividends, since not all issuers that pay dividends positively correlated to the stock price.Keywords: dividend, stock price, listed on the Stock Exchange

The impact of issuing warrant and debt on behavior of the firm's stock

In this paper two model predictive control (MPC) approaches, an on-line and an off-line MPC approach, for constrained uncertain continuous-time systems with piecewise constant control input are presented. Both MPC approaches guarantee robust asymptotic stability by taking into account the hybrid dynamics of the closed-loop system, resulting from the continuous-time system dynamics and the discrete-time model predictive controller dynamics. Examples are presented to demonstrate the applicability of the proposed MPC approaches.

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance.

The problem of path following for marine surface vessels using the rudder control is addressed in this paper. The need to enforce the roll constraints and the fact that the rudder actuation is limited in both amplitude and rate make the model predictive control (MPC) approach a natural choice. The MPC design is based on a linearized model for computational and implementation considerations, while the evaluation of the performance of MPC controller is performed on a nonlinear 4 degree of freedom surface vessel model. The simulation results are presented to verify the effectiveness of the resulting controller and a simulation based tuning process for the controller is also presented. Furthermore, the performance of the path following MPC control in wave fields is evaluated using an integrated maneuvering and seakeeping model, and the simulation confirms its robustness.

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

In this study, a supervisory controller for an ASU (Air Separations Unit) has been designed based on a MPC (Model Predictive Control) approach. This has been applied to a detailed pressure-driven Aspen Plus/Aspen Dynamics model to understand the performance of a multivariable MPC when adopted for such a complex process with critical targets. In addition, an effective PI-based secondary-layered controller design has been proposed for open-loop stability and maximizing oxygen yield. The rigorous Aspen model is interfaced with Matlab/Simulink and has been used as a “real plant” when performing the step tests for the identification of the simplified linear model and application of supervisory control. The effectiveness of the designed controller has been proved through the comparison between the linear MPC approach, that handles absolute and rate-of-change constraints, and a more conventional control configuration. The results show a significant reduction in overshoots and settling time during load variations. The paper clearly shows how the MPC approach for a supervisory control layer is reliable, easy to design and of real value for practical purposes.

An agent-based interaction model for Chinese personal income distribution

Preliminary study for motion sickness reduction in autonomous vehicles: an MPC approach

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.

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.

Model predictive control of feed rate for stabilizing and enhancing biogas production in anaerobic digestion under meteorological fluctuations

Model predictive control (MPC) has emerged as a promising approach to control a modular multilevel converter (MMC). With the help of a cost function, the control objectives of an MMC are achieved easily by using an MPC approach. However, the MPC has several technical challenges and issues including the need of accurate system models, computational complexity, and variable switching frequency operation and weighting factor selection, when it comes to the control of an MMC. In the past few years, several research studies are conducted to address some of the challenges and issues in an MPC and developed several model predictive algorithms for an MMC. In this paper, the importance of each challenge and its impact on the system performance is discussed. Also, the MMC mathematical models used in the implementation of MPC are presented. Furthermore, some of the popular MPC algorithms are discussed briefly, and their features and performance are highlighted through case studies. Finally, summary and future trends of MPC for an MMC are presented.

A novel Model Predictive Control (MPC) approach for Permanent Magnet Synchronous Machines (PMSMs) is proposed in this paper. It consists of choosing a number of MPC cost functions appropriately, in accordance with PMSM control strategy and torque requirements. These are then minimized following an appropriate order of priority, thus enabling enhanced MPC performances and flexibility compared to those achievable by employing weighting factors. Such a good result is achieved also by suitably managing PMSM operating constraints, over both dynamic and steady state operation. The effectiveness of the proposed MPC approach is validated through an experimental study, which refers to a Surface-Mounted Permanent Magnet Synchronous Machine (SPM) driven by means of a suitable control board. This consists mainly of Field Programmable Gate Arrays (FPGA), whose computational performances enable the real-time implementation of the proposed MPC algorithm.

A microgrid with an advanced energy management approach is a feasible solution for accommodating the development of distributed generators (DGs) and electric vehicles (EVs). At the primary stage of development, the total number of EVs in a microgrid is fairly small but increases promptly. Thus, it makes most prediction models for EV charging demand difficult to apply at present. To overcome the inadaptability, a novel robust approach is proposed to handle EV charging demand predictions along with demand-side management (DSM) on the condition of satisfying each EV user’s demand. Variables with stochastic forecast models join the objective function in the form of probability-constrained scenarios. This paper proposes a scenario-based model predictive control (MPC) approach combining both robust and stochastic models to minimize the total operational cost for energy management. To overcome the concern about the convergence time increasing from the combination of scenarios, the Benders decomposition (BD) technique is further adopted to improve computational efficiency. Simulation results on a combined heat and power microgrid indicate that the proposed scenario-based MPC approach achieves a better economic performance than a traditional deterministic MPC (DMPC) approach, while ensuring EV charging demands, as well as minimizing the trade-off between optimal solutions and computing times.