Efficiency Optimization Control Research Articles

An efficient parallel processing optimal control scheme for a class of nonlinear composite systems

This article presents an efficient parallel processing approach for solving the optimal control problem of nonlinear composite systems. In this approach, the original high-order coupled nonlinear two-point boundary value problem (TPBVP) derived from the Pontryagin's maximum principle is first transformed into a sequence of lower-order decoupled linear time-invariant TPBVPs. Then, an optimal control law which consists of both feedback and forward terms is achieved by using the modal series method for the derived sequence. The feedback term specified by local states of each subsystem is determined by solving a matrix Riccati differential equation. The forward term for each subsystem derived from its local information is an infinite sum of adjoint vectors. The convergence analysis and parallel processing capability of the proposed approach are also provided. To achieve an accurate feedforward-feedback suboptimal control, we apply a fast iterative algorithm with low computational effort. Finally, some comparative results are included to illustrate the effectiveness of the proposed approach.

First Principles Computational Study of Surface Reactions Toward Design Concepts of High Functional Electrocatalysts for Oxygen Reduction Reaction in a Fuel Cell System

Design of novel materials in renewable energy systems plays a key role in powering transportation vehicles and portable electronics. This review introduces the research work of first principles-based computational design for the materials over the last decade to accomplish the goal with less financial and temporal cost beyond the conventional approach, especially, focusing on electrocatalyst toward a proton exchange membrane fuel cell (PEMFC). It is proposed that the new method combined with experimental validation, can provide fundamental descriptors and mechanical understanding for optimal efficiency control of a whole system. Advancing these methods can even realize a computational platform of the materials genome, which can substantially reduce the time period from discovery to commercialization into markets of new materials.

Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems

This paper focuses on the modelling of the performance of a Polymer Electrolyte Membrane Fuel Cell (PEMFC)-based cogeneration system to integrate it in hybrid and/or connected to grid systems and enable the optimization of the techno-economic efficiency of the system in which it is integrated. To this end, experimental tests on a PEMFC-based cogeneration system of 600 W of electrical power have been performed to train an Artificial Neural Network (ANN). Once the learning of the ANN, it has been able to emulate real operating conditions, such as the cooling water out temperature and the hydrogen consumption of the PEMFC depending on several variables, such as the electric power demanded, temperature of the inlet water flow to the cooling circuit, cooling water flow and the heat demanded to the CHP system. After analysing the results, it is concluded that the presented model reproduces with enough accuracy and precision the performance of the experimented PEMFC, thus enabling the use of the model and the ANN learning methodology to model other PEMFC-based cogeneration systems and integrate them in techno-economic efficiency optimization control systems.

Simulating quality assurance and efficiency analysis between construction management and engineering geodesy

Abstract High-rise building construction is highly complex and involves many different disciplines. Such projects can only be accomplished efficiently and with appropriate quality by means of an overall optimized construction process. However, the understanding of the term “quality” among involved participants is not always equivalent. E.g. providing required tolerances of elevator shaft walls constantly throughout the concrete works is often a challenging task during the execution of in-situ concrete works. This paper is based on the scientific research project “Efficiency optimization and quality control of engineering geodesy processes in civil engineering” (Effizienzoptimierung und Qualitatssicherung ingenieurgeodatischer Prozesse im Bauwesen - EQuiP), which aimed at developing a method for instant quality assurance in construction based on geodetic surveying techniques, and at the same time optimizing the efficiency of the process, e.g. with respect to time. This paper shows how hierarchical and modular modeling of construction and geodetic processes using high-level Petri nets delivers a base for a real time quality evaluation and re-planning on construction site. As an example, this modeling approach is simulated for the construction of concrete stairs and elevators core of a characteristic high-rise structure. The concrete works were considered to be carried out using climbing formwork. Three different scenarios are shown in this paper. The first scenario is simulated with deterministic durations and validates the developed Petri net. The second scenario uses stochastic process durations to show the robustness of the construction process. Finally, disturbances and delays are integrated and an automated rescheduling based on prioritized alternative paths is used.

Discrete optimal control for Birkhoffian systems and its application to rendezvous and docking of spacecrafts

In general, optimal control problems rely on numerically rather than analytically solving methods, due to their nonlinearities. The direct method, one of the numerically solving methods, is mainly to transform the optimal control problem into a nonlinear optimization problem with finite dimensions, via discretizing the objective functional and the forced dynamical equations directly. However, in the procedure of the direct method, the classical discretizations of the forced equations will reduce or affect the accuracy of the resulting optimization problem as well as the discrete optimal control. In view of this fact, more accurate and efficient numerical algorithms should be employed to approximate the forced dynamical equations. As verified, the discrete variational difference schemes for forced Birkhoffian systems exhibit excellent numerical behaviors in terms of high accuracy, long-time stability and precise energy prediction. Thus, the forced dynamical equations in optimal control problems, after being represented as forced Birkhoffian equations, can be discretized according to the discrete variational difference schemes for forced Birkhoffian systems. Compared with the method of employing traditional difference schemes to discretize the forced dynamical equations, this way yields faithful nonlinear optimization problems and consequently gives accurate and efficient discrete optimal control. Subsequently, in the paper we are to apply the proposed method of numerically solving optimal control problems to the rendezvous and docking problem of spacecrafts. First, we make a reasonable simplification, i.e., the rendezvous and docking process of two spacecrafts is reduced to the problem of optimally transferring the chaser spacecraft with a continuously acting force from one circular orbit around the Earth to another one. During this transfer, the goal is to minimize the control effort. Second, the dynamical equations of the chaser spacecraft are represented as the form of the forced Birkhoffian equation. Then in this case, the discrete variational difference scheme for forced Birkhoffian system can be employed to discretize the chaser spacecraft's equations of motion. With further discretizing the control effort and the boundary conditions, the resulting nonlinear optimization problem is obtained. Finally, the optimization problem is solved directly by the nonlinear programming method and then the discrete optimal control is achieved. The obtained optimal control is efficient enough to realize the rendezvous and docking process, even though it is only an approximation of the continuous one. Simulation results fully verify the efficiency of the proposed method for numerically solving optimal control problems, if the fact that the time step is chosen to be very large to limit the dimension of the optimization problem is noted.

Efficiency Optimization Control of IPMSM Driving System for Electric Vehicles Using Quasi-Z Source Inverter

Efficiency Optimization Control of IPMSM Driving System for Electric Vehicles Using Quasi-Z Source Inverter

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

Efficient optimal deadlock control of flexible manufacturing systems

The development of an efficient deadlock avoidance policy (DAP) is a critical step in the design and operation of a flexible manufacturing system (FMS). However, even for a simple FMS, the computation of an optimal DAP is intractable. This work addresses the optimal deadlock control problem of FMSs. Based on their Petri net models, it introduces the concept of κ-resources and proves that an FMS containing no κ-resources has only two types of reachable states: safe ones and deadlocks. It then can obtain an optimal DAP with polynomial computational complexity for a broader class of FMSs, which was never seen before. At the same time, for these FMSs, new structurally simpler optimal Petri net controllers are proposed.

Fully efficient time-parallelized quantum optimal control algorithm

We present a time-parallelization method that enables one to accelerate the computation of quantum optimal control algorithms. We show that this approach is approximately fully efficient when based on a gradient method as optimization solver: the computational time is approximately divided by the number of available processors. The control of spin systems, molecular orientation, and Bose-Einstein condensates are used as illustrative examples to highlight the wide range of applications of this numerical scheme.

Neural Network Reinforcement Learning for Walking Control of a 3-Link Biped Robot

In recent years, researches on adaptive control have focused on bio-inspired learning techniques to deal with real-life applications. Reinforcement Learning (RL) is one of these major techniques, which has been widely used in robot control approaches. The implementation of artificial neural networks in RL algorithms enables more efficient optimal control strategies. This article proposes a field application of neural network reinforcement learning (NNRL) for walking control of an active simulated 3-link biped robot. The adaptive control agent consists of two neural network units, known as actor and critic for learning prediction and learning control tasks. Results of the presented control method reveal its efficiency in stable walking control of the biped robot model as a nonlinear complex dynamic task.

Traffic flow optimisation in presence of vehicle automation and communication systems – Part I: A first-order multi-lane model for motorway traffic

Proposed or emerging vehicle automation and communication systems (VACS) may contribute to the mitigation of motorway traffic congestion on the basis of appropriate traffic control strategies. In this context, this paper presents a novel first-order multi-lane macroscopic traffic flow model for motorways which is mainly intended for use within a related optimal control problem formulation. The model’s starting point is close to the well-known CTM (cell-transmission model), which is modified and extended to consider additional aspects of the traffic dynamics, such as lane changing and the capacity drop, via appropriate procedures for computing lateral and longitudinal flows. The model has been derived with a view to combine realistic traffic flow description with a simple (linear or piecewise linear) mathematical form, which can be exploited for efficient optimal control problem formulations, as described in a companion (Part II) paper. Although the model has been primarily derived for use in future traffic conditions including VACS, it may also be used for conventional traffic flow representation. In fact, the accuracy of the proposed modelling approach is demonstrated through calibration and validation procedures using real data from an urban motorway located in Melbourne, Australia.

Magnetizing Curve Identification for Induction Motors at Standstill Without Assumption of Analytical Curve Functions

It is necessary to obtain accurate knowledge about the magnetizing curve of an induction machine so that the magnetizing inductance can be updated according to different flux references for either efficiency optimization or field weakening control. This paper proposes a computational saving method to obtain a one-to-one correspondence magnetizing curve without the need for conventional curve-fitting strategies, which require the assumption of explicit analytical curve functions. A detailed comparison between the proposed method and the conventional curve-fitting method has been done. It shows great accuracy improvements of this proposed method. The identification is easy to implement with high precision while maintaining the motor at standstill. No additional sensor devices are needed except the sensors carried by the drive itself. This proposed method is verified by simulation. Further experimental results of this method are utilized in a real control strategy with a 2.2-kW 50-Hz 4-pole induction motor to confirm its validity and feasibility.

Efficiency Optimization Control for Three-Phase Induction Motors with Hall Sensor

This thesis develops efficiency optimization control strategies for three phase induction motors with Hall sensor. This thesis compares the different control strategies include open-loop voltage-mode control of hard-commutation scheme, open-loop voltage-mode control of soft-commutation scheme, and closed-loop current-mode control scheme, and analyzes the current response and overall efficiency. This thesis also develops a low-cost and high efficiency sensorless control for three phase induction motors drives. The proposed scheme detects the back-EMF zero-crossing-point (ZCP) to realize current commutation without Hall sensor. An adjustable blanking time control method is used to ensure correct detection of the ZCP. When using sensorless commutation control, the fan motor can start-up smoothly and operate under wide speed range, moreover, the overall efficiency is almost the same to the results of control scheme with Hall sensor.

Fuzzy Logic-Based Efficiency Optimization and High Dynamic Performance of IPMSM Drive System in Both Transient and Steady-State Conditions

This paper presents both the efficiency optimization and high performance speed control of an interior permanent-magnet synchronous motor (IPMSM) drive, based on fuzzy logic controllers (FLCs). In order to maximize the efficiency during both transient-state and steady-state operations while meeting the speed and load torque demands, two fuzzy logic-based efficiency controllers are designed to generate the optimum magnetizing current, which is the d-axis component of the stator current id. The steady-state fuzzy efficiency controller is a search controller operating only in steady state to minimize the drive power losses to achieve higher efficiency by reducing the stator flux. The transient-state fuzzy efficiency controller operates only during transient state to increase the flux, depending on the speed error and its derivative. In order to achieve high dynamic performance, another FLC is used as speed controller, which controls the torque component of the stator current. Furthermore, a torque compensator is used to reduce the torque and speed ripples. The efficacy of the proposed IPMSM drive for efficiency optimization and robustness is tested in both simulation and experiment.

Control Impacts of Cold-Air Bypass on Pressurized Fuel Cell Turbine Hybrids

A pressure drop analysis for a direct-fired fuel cell turbine hybrid power system was evaluated using a hardware-based simulation of an integrated gasifier/fuel cell/turbine hybrid cycle, implemented through the hybrid performance (Hyper) project at the National Energy Technology Laboratory, U.S. Department of Energy (NETL). The Hyper facility is designed to explore dynamic operation of hybrid systems and quantitatively characterize such transient behavior. It is possible to model, test, and evaluate the effects of different parameters on the design and operation of a gasifier/fuel cell/gas turbine hybrid system and provide means of evaluating risk mitigation strategies. The cold-air bypass in the Hyper facility directs compressor discharge flow to the turbine inlet duct, bypassing the fuel cell, and exhaust gas recuperators in the system. This valve reduces turbine inlet temperature while reducing cathode airflow, but significantly improves compressor surge margin. Regardless of the reduced turbine inlet temperature as the valve opens, a peak in turbine efficiency is observed during characterization of the valve at the middle of the operating range. A detailed experimental analysis shows the unusual behavior during steady state and transient operation, which is considered a key point for future control strategies in terms of turbine efficiency optimization and cathode airflow control.

Efficiency Optimization in Speed-Sensorless Control of Induction Motors

An efficiency optimization model for induction motors with speed-sensorless control is presented in this paper. An mathematical loss model with stator iron loss in DTC(Direct Torque Control) system is used to calculate the motors loss, the loss efficiency and the optimal flux. Additionally, the efficiency optimization control strategy combined with the speed-sensorless model is used to rebuild the simulation modeling. The simulation results with the proposed control strategy show superior effects compared to the traditional control methods. The optimal control strategy can be achieved to improve the motor efficiency.

Optimal energy management for grid-connected storage systems

Summary This paper proposes three near-optimal (to a desired degree) deterministic charge and discharge policies for the maximization of profit in a grid-connected storage system. The changing price of electricity is assumed to be known in advance. Three near-optimal algorithms are developed for the following three versions of this optimization problem: (1) The system has supercapacitor type storage, controlled in continuous time. (2) The system has supercapacitor or battery type storage, and it is controlled in discrete time (i.e., it must give constant power during each sampling period). A battery type storage model takes into account the diffusion of charges. (3) The system has battery type storage, controlled in continuous time. We give algorithms for the approximate solution of these problems using dynamic programming, and we compare the resulting optimal charge/discharge policies. We have proved that in case 1 a bang off bang type policy is optimal. This new result allows the use of more efficient optimal control algorithms in case 1. We discuss the advantages of using a battery model and give simulation and experimental results. Copyright © 2014 John Wiley & Sons, Ltd.

Numerical Time-Scale Separation for Rotorcraft Nonlinear Optimal Control Analyses

This paper proposes a numerical technique for an efficient nonlinear optimal control analysis in which the system dynamics presents multiplicity in its time scale corresponding to a high-fidelity rotorcraft model. For this purpose, the integration of the fast dynamics is carried out separately from the integration of the slow dynamics using a pseudospectral method. The resultant nonlinear programming problem can be efficiently solved with a small number of computational nodes for the slow dynamics without degrading the fidelity of the original model. The integration process of each dynamics uses different nodes and shares the information for the states and controls by means of local Lagrange interpolation. In the algorithm implementation, the moving horizon approach and residualization method are investigated to guarantee solution convergence and to preserve the sensitivity of the cost and constraint functions to the slow states, respectively. To verify the proposed approach, two simulation studies are presented. First, the dynamic responses of the Bo-105 helicopter due to longitudinal doublet inputs are analyzed, which successfully provide the same level of integration accuracy as the Runge–Kutta method. Second, an optimal autorotational landing problem is considered as an example to prove the effectiveness of the proposed time-scale separation technique.

Wheel loader optimal transients in the short loading cycle

A nonlinear wheel loader model with nine states and four control inputs is utilized to study the fuel and time efficient optimal control of wheel loader operation in the short loading cycle. The wheel loader model consists of lifting, steering and powertrain subsystems where the nonlinearity originates from the torque converter in the drivetrain. The short loading cycle, from loading point to a load receiver and back to the loading point, for a fork lifting application is described in terms of boundary conditions of the optimization problem while the operation is divided into several phases with constant gearbox gear ratios in order to avoid discontinuities due to discrete gear ratios. The effect of load receiver standing orientation on the wheel loader trajectory, fuel consumption and cycle time is studied showing that a small deviation from the optimal orientation (≈ 20 [deg]) results in up to 18 % higher fuel consumption in the minimum time cycles. Also, an alternative lifting strategy where for operation safety load is lifted only when wheel loaders moves forward is studied showing that this increases the fuel consumption of a typical 25 [sec] cycle only less than 2 %. The wheel loader path between loading point and load receiver is also calculated by optimization and analyzed for different cases. It is shown that when the load receiver orientation is not optimized and is set manually, the time or fuel optimal paths will differ from the shortest distance path, however when the load receiver orientation is calculated by optimization the fuel, time and shortest distance paths become identical.

Evaluation of Relationship between Trunk Muscle Endurance and Static Balance in Male Students.

Fatigue of trunk muscle contributes to spinal instability over strenuous and prolonged physical tasks and therefore may lead to injury, however from a performance perspective, relation between endurance efficient core muscles and optimal balance control has not been well-known. The purpose of this study was to examine the relationship of trunk muscle endurance and static balance. Fifty male students inhabitant of Tehran university dormitory (age 23.9±2.4, height 173.0±4.5 weight 70.7±6.3) took part in the study. Trunk muscle endurance was assessed using Sørensen test of trunk extensor endurance, trunk flexor endurance test, side bridge endurance test and static balance was measured using single-limb stance test. A multiple linear regression analysis was applied to test if the trunk muscle endurance measures significantly predicted the static balance. There were positive correlations between static balance level and trunk flexor, extensor and lateral endurance measures (Pearson correlation test, r=0.80 and P<0.001; r=0.71 and P<0.001; r=0.84 and P<0.001, respectively). According to multiple regression analysis for variables predicting static balance, the linear combination of trunk muscle endurance measures was significantly related to the static balance (F (3,46) = 66.60, P<0.001). Endurance of trunk flexor, extensor and lateral muscles were significantly associated with the static balance level. The regression model which included these factors had the sample multiple correlation coefficient of 0.902, indicating that approximately 81% of the variance of the static balance is explained by the model. There is a significant relationship between trunk muscle endurance and static balance.