Model Predictive Control Research Papers (original) (raw)
Keywords: MIMO antenna UWB antenna Model predictive Control Non-uniform microstrip line Printed monopole antenna a b s t r a c t A novel ultra wideband (UWB) printed monopole multiple-input multiple-output (MIMO) antenna with non-uniform... more
Keywords: MIMO antenna UWB antenna Model predictive Control Non-uniform microstrip line Printed monopole antenna a b s t r a c t A novel ultra wideband (UWB) printed monopole multiple-input multiple-output (MIMO) antenna with non-uniform transmission line using nonlinear model predictive control (NMPC) is presented. The proposed antenna is superior to conventional antennas in terms of dimensions, gain, and efficiency while maintaining the impedance bandwidth. In order to improve the results, a non-uniform transmission line has been used for impedance matching between the radiated patch element and the coaxial cable. For designing the non-uniform transmission line, it has been expanded using cosine terms. Regarding the presence of differential equation for the variation in the impedance of the transmission line and its transformation to the state-space equation, NMPC has been employed to design the transmission line and determine the cosine expansion coefficients. Two base antennas, as MIMO, were simulated configuration and fabricated. The surface area of the proposed MIMO antenna is 0.99 k 2 g , the wavelength has been obtained for the center frequency of the 3.16 GHz to 10.6 GHz range, and its mutual coupling, peak gain, channel capacity loss (CCL), total active reflection coefficient (TARC), mean effective gain (MEG) and diversity gain (DG), envelope correlation (ECC) are acceptable. The simulation and measurement results are in good agreement, and the proposed antenna is suitable for MIMO applications. Ó 2020 The ''Authors". Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Abstract: In this paper, the direct differentiation method ͑DDM͒ for finite-element ͑FE͒ response sensitivity analysis is extended to linear and nonlinear FE models with multi-point constraints ͑MPCs͒. The analytical developments are... more
Abstract: In this paper, the direct differentiation method ͑DDM͒ for finite-element ͑FE͒ response sensitivity analysis is extended to linear and nonlinear FE models with multi-point constraints ͑MPCs͒. The analytical developments are provided for three different constraint handling methods, namely: ͑1͒ the transformation equation method; ͑2͒ the Lagrange multiplier method; and ͑3͒ the penalty function method. Two nonlinear benchmark applications are presented: ͑1͒ a two-dimensional soil-foundation-structure interaction system and ͑2͒ a three-dimensional, one-bay by one-bay, three-story reinforced concrete building with floor slabs modeled as rigid diaphragms, both subjected to seismic excitation. Time histories of response parameters and their sensitivities to material constitutive parameters are computed and discussed, with emphasis on the relative importance of these parameters in affecting the structural response. The DDMbased response sensitivity results are compared with corres...
Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to... more
Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results show that MPC can be effectively used for control in wastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.
This paper presents a model predictive control (MPC) design for the unified power quality conditioner (UPQC), an integration of series and shunt active filters to improve power quality in a power distribution system. The control strategy... more
This paper presents a model predictive control (MPC) design for the unified power quality conditioner (UPQC), an integration of series and shunt active filters to improve power quality in a power distribution system. The control strategy aims to regulate the load voltage and the source current to the desired references in spite of the existence of harmonic components in the supply voltage and the load current, possible sag or swell in the supply voltage, and non-unity power factor of the supply side. Kalman filters are used to extract the fundamental as well as the harmonic components abovementioned, which will then be used to formulate the desired references and regarded as measurable disturbances, respectively. Based on a state-space model developed for the UPQC, an MPC controller is designed. Simulation studies on a single-phase power distribution system are also presented to verify the performance.
Fuel Cell Hybrid Electric Vehicles (FCHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. Hybridization can greatly... more
Fuel Cell Hybrid Electric Vehicles (FCHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. Hybridization can greatly benefit fuel cell technology. There are many potential ad vantages such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle. This work presents a control system that fulfils the power demanded by the electric motor making use of two power sources: a primary source (fuel cell) and a battery pack. Both power sources, independently or together, supply power to the vehicle in order to satisfy driver's demand. The real-time control computes the power distribution between the primary energy source and its associated Energy Storage System (ESS) to optimize the global hydrogen consumption while maintaining drivability. The coordination between the various power sources requires a high level of control in the vehicle. Model Predictive Control (MPC) is used in order to minimize the overall energy use in the presence of several constraints that appear due to drivability requirements and the characteristic of the components. The proposed control strategy has been tested on a simulated model of a SUV (Sport Utility Vehicle), showing that a good control strategy can fulfil the power requested by the driver with the minimum fuel consumption.
Model Predictive Control (MPC) was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast... more
Model Predictive Control (MPC) was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast dynamics. In this paper a novel method called Sam- pling Based Model Predictive Control (SBMPC) is proposed as a "fast" MPC algorithm to generate control
This article discusses the existing linear model predictive control concepts in a unified theoretical framework based on a stabilizing, infinite horizon, linear quadratic regulator. In order to represent unstable as well as stable... more
This article discusses the existing linear model predictive control concepts in a unified theoretical framework based on a stabilizing, infinite horizon, linear quadratic regulator. In order to represent unstable as well as stable multivariable systems, the standard state-space formulation is used for the plant model. The incorporation of a nominally stabilizing constrained regulator eliminates the current requirement of tuning for nominal stability. Output feedback is addressed in the well-established framework of the linear quadratic state-estimation problem. This framework allows the flexibility to handle nonsquare systems, noisy inputs and outputs, and nonzero input, output, and state disturbances. This formulation subsumes the integral control schemes designed to remove steady-state offset currently in industrial use. The online implementation of the controller requires the solution of a standard quadratic program that is no more computationally intensive than existing algorithms.
Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive... more
Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control (MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.
The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support... more
The Modelica language, targeted at modeling of complex physical systems, has gained increased attention during the last decade. Modelica is about to establish itself as a de facto standard in the modeling community with strong support both within academia and industry. While there are several tools, both commercial and free, supporting simulation of Modelica models few efforts have been made