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The article is aimed at the mathematical and optimization modeling of technological processes of surface treatments, specifically the zincing process. In surface engineering, it is necessary to eliminate the risk that the resulting product quality will not be in line with the reliability requirements or needs of customers. To date, a number of research studies deal with the applications of mathematical modeling and optimization methods to control technological processes and eliminate uncertainties in the technological response variables. The situation is somewhat different with the acid zinc plating process, and we perceive their lack more. This article reacts to the specific requirements from practice for the prescribed thickness and quality of the zinc layer deposited in the acid electrolyte, which stimulated our interest in creating a statistical nonlinear model predicting the thickness of the resulting zinc coating (ZC). The determination of optimal process conditions for acid galvanizing is a complex problem; therefore, we propose an effective solving strategy based on the (i) experiment performed by using the design of experiments (DOE) approach; (ii) exploratory and confirmatory statistical analysis of experimentally obtained data; (iii) nonlinear regression model development; (iv) implementation of nonlinear programming (NLP) methods by the usage of MATLAB toolboxes. The main goal is achieved—regression model for eight input variables, including their interactions, is developed (the coefficient of determination reaches the value of R2 = 0.959403); the optimal values of the factors acting during the zincing process to achieve the maximum thickness of the resulting protective zinc layer (the achieved optimum value th* = 12.7036 μm), are determined. [ABSTRACT FROM AUTHOR]

Lane-changing and overtaking are conventional maneuvers on roads, and the reference trajectory is one of the prerequisites to execute these maneuvers. This study proposes a universal trajectory planning method for automated lane-changing and overtaking maneuvers, in which the trajectory is regarded as the combination of a path and its traffic state profiles. The two-dimensional path is represented by a suitable curve to connect the initial position with final position of the ego vehicle. Based on the planned path, its traffic state profiles are generated by solving a nonlinear mathematical optimization model. Moreover, the study discretizes the time horizon into several time intervals and determines the parameters to obtain the continuous and smooth profiles, which guarantees the safety and comfort of the ego vehicle. Finally, a series of simulation experiments are performed in the MATLAB platform and the results show the feasibility and effectiveness of the proposed universal trajectory planning method. [ABSTRACT FROM AUTHOR]

Abstract With the development of vision and optimization techniques, visual-inertial odometry (VIO) has shown the capability of motion estimating in the GNSS-denied condition. The VIO can provide absolute pitch and roll angles estimating value, but no the absolute azimuth. In the paper, we proposed a VIO aided by compass, which can obtain the azimuth with respect to the north direction in the geographic frame. Moreover, aided by compass, the yaw angle estimating error was reduced to a greater degree, due to the measurement of azimuth. Furthermore, the consistency of the VIO backend estimator is improved as well, while the accuracy of the estimated pose states was also wholly improved. The aiding approach is a tightly-couple information fusion system of camera, IMU and magnetoresistive sensors. The optimization method is based on the pre-integration and bundle adjustment. In the paper, we derived the compass residual model based on the pre-integration model, and then its Jacobian and covariance formation were deduced to solve the nonlinear equations. The compass aided VIO software was implemented based on the Nvidia Jetson Tx2. The system was fully tested based on hardware-in-the-loop simulation and vehicle test in the real physical environment. The pose errors of VIOs with and without compass aiding were compared in the above tests. The simulation results showed that the position was and yaw errors were improved obviously; the compass aided VIO was still consistent, but the pure VIO was consistent not. The consistency character is evaluated by average NEES by Monte-Carlo in simulation. The vehicle test showed that the position error was reduced by 23%; the yaw error was reduced by 21%. As a result, the compass aided VIO not only improved the pose estimated accuracy, especially position and yaw, but also improved the consistency of VIO system. [ABSTRACT FROM AUTHOR]

We investigate the nonlinear minimization problem on a convex closed set in a Hilbert space. It is shown that the uniform conditional well-posedness of a class of problems with weakly lower semicontinuous functionals is the necessary and sufficient condition for existence of regularization procedures with accuracy estimates uniform on this class. We also establish a necessary and sufficient condition for the existence of regularizing operators which do not use information on the error level in input data. Similar results were previously known for regularization procedures of solving ill-posed inverse problems. [ABSTRACT FROM AUTHOR]

A hybrid invasive weed optimization (HIWO) algorithm based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is proposed for the problems on parameter inversion of the nonlinear models of sun shadow with integer variables in the study. Our presented algorithm can take full advantage of the local search ability of BFGS algorithm and the global search ability of invasive weed optimization (IWO) algorithm. The HIWO algorithm can not only reverse the date of sun shadow model successfully, but also conquer the weaknesses that the classic mathematical methods are hard to address integer nonlinear optimization problems by utilizing integers in some random variables from algorithms. The results of numerical experiments demonstrate that the HIWO algorithm has not only high computing accuracy, but also fast convergence speed. It can effectively improve the accuracy and efficiency of the techniques of sun shadow location, and afford an effective and efficient technique to handle the issues of integer parameter inversion in engineering applications. [ABSTRACT FROM AUTHOR]

The application of nonlinear material models of concrete within numerical simulations focused on the design of safer and more economical protective concrete structures is currently the subject of investigation of many scientific researchers. However, one basic problem related to the nonlinear modelling of concrete is that very often there is a lack of knowledge about the material model parameters whose values must be defined. The solution to this problem can be in what is termed as inverse parameter identification, an approach which is presented in this paper. Specifically, the material parameters of the Continuous Surface Cap Model for concrete are identified within this paper using optimisation algorithms. The subsequent comparison of parameter identification results with experimental data shows the efficiency of the presented approach. [ABSTRACT FROM AUTHOR]

Distributed optimization, based on a decomposition of the entire optimization problem, has been applied to many complex decision making problems in process systems engineering, including nonlinear model predictive control. While decomposition techniques have been widely adopted, it remains an open problem how to optimally decompose an optimization problem into a distributed structure. In this work, we propose to use community detection in network representations of optimization problems as a systematic method of partitioning the optimization variables into groups, such that the variables in the same groups generally share more constraints than variables between different groups. The proposed method is applied to the decomposition of the optimal control problem involved in the nonlinear model predictive control of a reactor-separator process, and the quality of the resulting decomposition is examined by the resulting control performance and computational time. Our result suggests that community detection in network representations of the optimization problem generates decompositions with improvements in computational performance as well as a good optimality of the solution. [ABSTRACT FROM AUTHOR]

This paper aims to study the stability for primary frequency regulation of hydro-turbine governing system with surge tank. Firstly, a novel nonlinear mathematical model of hydro-turbine governing system considering the nonlinear characteristic of penstock head loss is introduced. The nonlinear state equations under opening control mode and power control mode are derived. Then, the nonlinear dynamic performance of nonlinear hydro-turbine governing system is investigated based on the stable domain for primary frequency regulation. New feature of the nonlinear hydro-turbine governing system caused by the nonlinear characteristic of penstock head loss is described by comparing with a linear model, and the effect mechanism of nonlinear characteristic of penstock head loss is revealed. Finally, the concept of critical stable sectional area of surge tank for primary frequency regulation is proposed and the analytical solution is derived. The combined tuning and optimization method of governor parameters and sectional area of surge tank is proposed. The results indicate that for the primary frequency regulation under opening control mode and power control mode, the nonlinear hydro-turbine governing system is absolutely stable and conditionally stable, respectively. The stability of the nonlinear hydro-turbine governing system and linear hydro-turbine governing system is the same under opening control model and different under power control model. The nonlinear characteristic of penstock head loss mainly affects the initial stage of dynamic response process of power output, and then changes the stability of the nonlinear system. The critical stable sectional area of surge tank makes the system reach critical stable state. The governor parameters and critical stable sectional area of surge tank jointly determine the distributions of stability states. [ABSTRACT FROM AUTHOR]

This paper presents nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (MHE) formulations for controlling the crystal size and shape distribution in a batch crystallization process. MHE is used to estimate unknown states and parameters prior to solving the NMPC problem. Combining these two formulations for a batch process, we obtain an expanding horizon estimation problem and a shrinking horizon model predictive control problem. The batch process has been modeled as a system of differential algebraic equations (DAEs) derived using the population balance model (PBM) and the method of moments. Therefore, the MHE and NMPC formulations lead to DAE-constrained optimization problems that are solved by discretizing the system using Radau collocation on finite elements and optimizing the resulting algebraic nonlinear problem using Ipopt . The performance of the NMPC–MHE approach is analyzed in terms of setpoint change, system noise, and model/plant mismatch, and it is shown to provide better setpoint tracking than an open-loop optimal control strategy. Furthermore, the combined solution time for the MHE and the NMPC formulations is well within the sampling interval, allowing for real world application of the control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to solve the transient stability constraint optimal power flow (TSC-OPF) problem. The transient stability constraints are expressed as the critical clearing time (CCT) of different contingencies, and are approximated using artificial neural networks (ANNs). The ANNs provide a nonlinear, differentiable mapping between the load ow variables and the CCT. As a result, the TSC-OPF with multiple transient stability constraints can be solved very efficiently with little additional computational burden. The effectiveness of the proposed method is demonstrated with the IEEE 39 bus and the IEEE 300 bus systems. [ABSTRACT FROM AUTHOR]

Global optimality of dynamic operations ensures maximum economic benefits over time. We introduce Dynamic Real Time Optimization (D-RTO) method which uses the same model for D-RTO and for Nonlinear Model Predictive Controller (NMPC), thereby eliminating the model mismatch between the D-RTO and the NMPC. An integration framework based on two different predictive horizons for time-scale decomposition is proposed. Fast updates and reoptimization of the dynamic trajectories ensure that the dynamic operation is optimal over time. Proposed global optimization algorithm is a variation of normalized multi-parametric disaggregation (NMDT) where NMDT is modified by the use of a bivariate partitioning to solve the D-RTO problem. Elimination of the mismatch between D-RTO and NMPC models significantly reduces the effort required to eliminate the model errors between D-RTO, NMPC and the plant. Three chemical process examples are included to illustrate the proposed framework. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the problem of a nonlinear model - specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between the data and the labels - exhibiting unstable performance depending on the hyperparameter under consideration. Design/methodology/approach: There are three main optimization search methods for hyperparameter tuning: manual search, grid search and random search. This study shows that HUCRFs' unstable performance depends on the hyperparameter values used and its performance is based on tuning that draws on grid and random searches. All experiments conducted used the n-gram features - specifically, unigram, bigram, and trigram. Findings: Naturally, selecting a list of hyperparameter values based on a researchers' experience to find a set in which the best performance is exhibited is better than finding it from a probability distribution. Realistically, however, it is impossible to calculate using the parameters in all combinations. The present research indicates that the random search method has a better performance compared with the grid search method while requiring shorter computation time and a reduced cost. Originality/value: In this paper, the issues affecting the performance of HUCRF, a nonlinear model with performance that varies depending on the hyperparameters, but performs better than CRF, has been examined. [ABSTRACT FROM AUTHOR]

The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequents are the predicted output represented by linear combinations of the input variables. The linear properties of the consequents lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequents are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy. [ABSTRACT FROM AUTHOR]

The analysis of human perceptions is often carried out by resorting to surveys and questionnaires, where respondents are asked to express ratings about the objects being evaluated. A class of mixture models, called CUB (Combination of Uniform and shifted Binomial), has been recently proposed in this context. This article focuses on a model of this class, the Nonlinear CUB, and investigates some computational issues concerning parameter estimation, which is performed by Maximum Likelihood. More specifically, we consider two main approaches to optimize the log-likelihood: the classical numerical methods of optimization and the EM algorithm. The classical numerical methods comprise the widely used algorithms Nelder–Mead, Newton–Raphson, Broyden–Fletcher–Goldfarb–Shanno (BFGS), Berndt–Hall–Hall–Hausman (BHHH), Simulated Annealing, Conjugate Gradients and usually have the advantage of a fast convergence. On the other hand, the EM algorithm deserves consideration for some optimality properties in the case of mixture models, but it is slower. This article has a twofold aim: first we show how to obtain explicit formulas for the implementation of the EM algorithm in nonlinear CUB models and we formally derive the asymptotic variance–covariance matrix of the Maximum Likelihood estimator; second, we discuss and compare the performance of the two above mentioned approaches to the log-likelihood maximization. [ABSTRACT FROM AUTHOR]

The ability to model nonlinear structures subject to random excitation is of key importance in designing hypersonic aircraft and other advanced aerospace vehicles. When a structure is linear, superposition can be used to construct its response to a known spectrum in terms of its linear modes. Superposition does not hold for a nonlinear system, but several works have shown that a system's dynamics can still be understood qualitatively in terms of its nonlinear normal modes (NNMs). This work investigates the connection between a structure's undamped nonlinear normal modes and the spectrum of its response to high amplitude random forcing. Two examples are investigated: a spring-mass system and a clamped-clamped beam modeled within a geometrically nonlinear finite element package. In both cases, an intimate connection is observed between the smeared peaks in the response spectrum and the frequency-energy dependence of the nonlinear normal modes. In order to understand the role of coupling between the underlying linear modes, reduced order models with and without modal coupling terms are used to separate the effect of each NNM's backbone from the nonlinear couplings that give rise to internal resonances. In the cases shown here, uncoupled, single-degree-of-freedom nonlinear models are found to predict major features in the response with reasonable accuracy; a highly inexpensive approximation such as this could be useful in design and optimization studies. More importantly, the results show that a reduced order model can be expected to give accurate results only if it is also capable of accurately predicting the frequency-energy dependence of the nonlinear modes that are excited. [ABSTRACT FROM AUTHOR]

Shimmy vibration is the oscillatory motion of the fork–wheel assembly about the steering axis. It represents one of the major problem of aircraft landing gear because it can lead to excessive wear, discomfort as well as safety concerns. Based on the nonlinear model of the mechanics of a single wheel nose landing gear (NLG), electromechanical actuator and tire elasticity, a robust active controller capable of damping shimmy vibration is designed and investigated in this study. A novel Decline Population Swarm Optimization ( P D SO ) procedure is introduced and used to select the optimal parameters for the controller. The P D SO procedure is based on a decline demographic model and shows high global search capability with reduced computational costs. The open and closed loop system behavior is analyzed under different case studies of aeronautical interest and the effects of torsional free play on the nose landing gear response are also studied. Plant parameters probabilistic uncertainties are then taken into account to assess the active controller robustness using a stochastic approach. [ABSTRACT FROM AUTHOR]

In this paper, topology optimization of geometrically nonlinear trusses with and without stability constraints is investigated. It is shown that if classical minimum compliance formulation is considered without any stability constraints, the optimized designs are unstable and convergence issues may be encountered in the nonlinear structural analyses. To address these issues, a minimum compliance formulation with critical load factor constraint is proposed together with a strategy based on spurious modal energy ratio to determine the true critical eigenmodes and the corresponding critical load factor. Several numerical examples are presented to demonstrate the effectiveness of the proposed approach, which show that the optimized truss topologies obtained using the proposed approach are stable. More importantly, the critical load constraint is able to guarantee that the first critical load of the optimized design is always above the applied load so that the proposed approach is free from convergence issues during the Newton Raphson solution process. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection. [ABSTRACT FROM AUTHOR]

In this paper, we describe an extraction procedure of nonlinear models for microwave field-effect transistor (FET). We use a nonlinear model available in commercial CAD tools, and we extract the parameters by combining direct extraction and numerical optimization. We determine a first estimate of the model parameters by few DC and S-parameter measurements. Next, we refine the parameters by optimization against low-frequency and high-frequency vector-calibrated large-signal measurements gathered with a Large-Signal-Network Analyzer (LSNA). As case study we consider a 0.25 × 200 µm2 GaN FET on SiC for power amplifier applications. Ultimately, we want to show that a good accuracy level can be achieved while minimizing the extraction effort and that an accurate model can be built and suitably tailored depending on the final application. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage estimation procedure is proposed for varying coefficient partially nonlinear models, in which the estimates of parametric vector and coefficient functions do not affect each other. Specifically, we first employ an orthogonality-projection-based method for the estimation of parametric coefficient by removing out the varying coefficient parts. In the second stage, we approximate each coefficient function via B-spline basis functions and develop a novel variable selection procedure based on smooth-threshold estimating equations. The proposed procedure can automatically eliminate the irrelevant covariates by setting the corresponding coefficient functions as zero, and simultaneously estimate the nonzero regression components. Besides, this approach not only avoids solving a convex optimization problem that is required in previous variable selection procedures, but also is flexible and easy to implement. Under some mild conditions, the asymptotic theories of the generated estimates are established, including model selection consistency, rate of convergence as well as asymptotic distribution. Finally, some numerical simulations are conducted to examine the finite sample performance of the proposed methodologies, and a real data analysis is followed to further illustrate the application of the methods. [ABSTRACT FROM AUTHOR]

This paper approaches the optimisation of looped water distribution networks supplied from one or more sources, according to demand variation. The pipe networks have concentrated outflows or uniform outflow along the length of each pipe. An optimisation model coupled with a computational iterative procedure of optimal discharges through pipes is developed on the basis of linear programming for the design of new or partially extended water distribution networks. The optimum solution obtained by this model consists of one or two pipe segments of different discrete sizes between each pair of nodes. The improved linear optimisation model guarantees a high reliability. Also, it is possible to take into account the network pipes with variable discharge on route. Additionally, the paper compares the linear optimisation model to some others, such as the classic model of economical velocities, the Moshnin model and a nonlinear model. This shows a good performance of the proposed model. [ABSTRACT FROM PUBLISHER]

This article describes a method for establishing an appliance scheduling scheme that can optimally coordinate a group of appliances in a consumer's premises. Finite-horizon scheduling optimizations are formulated to schedule the operation of appliances using a modeled predictive control method that incorporates both forecasts and newly updated information. A complex mixed discrete-continuous non-linear model is here in established, and a novel algorithm that hybridizes particle swarm optimization and constraint handing methods is proposed to derive optimum solutions within a limited computational time. Simulation results showed that the proposed algorithm can efficiently resolve real-life instances and can be embedded in resource limited devices. [ABSTRACT FROM AUTHOR]

The machine settings modification is a primary optimization stage in optimization design of the tooth form error flank. Spatial geometric errors (SGEs) as a main error source play an important role in affecting the precision of the actual CNC process, especially the grinding. Based on nonlinear analysis and application of computational algorithm, a new accurate grinding machine settings modification methodology considering the SGEs is proposed for manufacturing hypoid gears with a high level of accuracy. Firstly, the functional relationships between machine settings and SGEs of a six-axis CNC grinding machine tool are established. Then, an accurate modification objective function taking into account the residual tooth form error is investigated as a nonlinear least square problem, and a trust region algorithm with dogleg step is proposed to obtain accurate modification variations. After the SGEs are measured and compensated, the modification of grinding machine settings is accomplished. Finally, the practical application of a closed-loop modification process is presented to verify the good accuracy of the modification methodology by the proposed algorithm. [ABSTRACT FROM AUTHOR]

An innovative integrated optimization strategy for gasoline blend planning is proposed, and an improved method to achieve online optimization of real-time blend recipes is described. The proposed strategy can calculate a rough blend and delivery sequence of gasoline and then adapt to process changes by using a three-level discrete-time algorithm. Only one blender is considered in this study. A single-period nonlinear model (NLP) is solved at the top level of the algorithm to check the feasibility of a long-term production plan. A multi-period mixed-integer nonlinear model is formulated and solved at the middle level of the algorithm to compute a short-term blend plan. Finally, a single-period NLP is solved circularly at the lowest level of the algorithm to optimize blend recipes to consider the changes in the quality of blend components. The initial plan is modified if the top-level model is not feasible. The middle-level model is resolved if an unexpected event occurs during blending. The proposed approach is advantageous because the initial planning and blending recipes can be modified online, remarkably minimizing quality giveaway and increasing the blending success rate. The performance of the proposed strategy is illustrated through its industrial application in real-world gasoline blending. [ABSTRACT FROM AUTHOR]

The paper deals with a multi-objective optimization method inspired by island biogeography. Specifically, a nonlinear model of migration is proposed: a sinusoidal law rules both immigration and emigration curves in order to match the natural behaviour which is observed in a natural habitat. The optimal shape design of an electrostatic micromotor, which is a benchmark in MEMS design, is considered as the case study. [ABSTRACT FROM AUTHOR]

In this article, a new attributes double sampling plan for three-class products (ADSPTP) is presented, and the corresponding operating characteristic function is constructed based on the given procedure of performing ADSPTP. Average sample numbers (ASN) for complete inspection and curtailed inspection of the second sample are derived and the extreme point is discussed on the three-dimensional ASN surface. In addition, the differences arising from different parameters are studied. Sampling plans are designed by a nonlinear optimization model. Finally, numerical examples and discussions are given to illustrate the obtained results, and tables of the designed plans under various conditions are provided. [ABSTRACT FROM AUTHOR]

The standing wave design (SWD) andoptimization method are developedfor the first time for nonlinear thermal simulated moving bed (SMB)systems with significant mass-transfer effects. The design methodwas verified using computer simulations for two systems. The adsorptionisotherms of fructose/glucose and p-xylene/toluenesystems have weak and strong temperature dependence, respectively.The results show that nonlinear thermal SMB systems can simultaneouslyproduce pure solvent as well as concentrated, high-purity productsfrom dilute feeds. The condition that allows for production of puresolvent is determined from local equilibrium analysis. Optimizationof ten decision variables to achieve minimum solvent consumption andmaximum productivity takes less than 5 min using a laptop computer.The fructose/glucose thermal SMB system products are increased by>25% with solvent consumption decreased 2-fold. The p-xylene/toluene thermal SMB system product concentrations can beincreased 10-fold while simultaneously producing pure solvents. [ABSTRACT FROM AUTHOR]

Recent results in the development of efficient large-scale nonlinear programming (NLP) algorithms have led to fast, on-line realizations of optimization-based methods for nonlinear model predictive control (NMPC) and dynamic real-time optimization (D-RTO), with predictive nonlinear dynamic (e.g., first principle) models. For NMPC, optimization-based controllers are developed that lead to well-understood stability and robustness properties, even for large, complex plant models. The realization of NMPC requires the application of a fast NLP solver for time-critical, on-line optimization, as well as efficient NLP sensitivity tools that require 2–3 orders of magnitude less computation than the NLP solution. This leads to advanced step NMPC (asNMPC), which essentially eliminates computational delay. We also extend these capabilities to dynamic real-time optimization (D-RTO) with more general stage costs that are economically based. This overview also extends input to state stability (ISS) properties for asNMPC to handle active set changes, and also for D-RTO through convex regularizations. Two large scale distillation case studies, based on nonlinear first principle models, are presented that demonstrate the effectiveness of these approaches. [ABSTRACT FROM AUTHOR]

This paper presents an efficient model to represent nonlinear thermal generation costs in the mid-term hydrothermal generation planning problem. The proposed approach comprises two procedures: the first one consists in obtaining an exact piecewise quadratic equivalent thermal cost function for total thermal generation cost based on quadratic cost functions for each individual unit. The second procedure consists in using a dynamic piecewise linear model to represent such function in the optimization problem to be solved. The combination of both procedures yield a linearized model for the equivalent thermal generation cost curve for each system area and time step, which are represented in the problem as a set of constraints. Numerical results for some instances of the multi-period, stochastic nonlinear hydrothermal planning problem show a remarkable CPU time reduction and an improved accuracy in the final solution of the problem, as compared to an individual static piecewise linear model, which is the usual approach adopted in the literature. [ABSTRACT FROM AUTHOR]

A rapid reentry trajectory planning method based on nonlinear model prediction theory is studied for the new lifting reentry vehicle with many constraints. Since the lifting reentry characters and mission requires need to satisfy strong terminal constraints, the nonlinear predictive model and the receding optimization scheme are used to convert the nonlinear optimization into the linear optimization objection, and a quadratic programming is used to solve it. The simulation results show that the proposed method can generate a high precision reentry trajectory with strong terminal constraints. [ABSTRACT FROM AUTHOR]

The article discusses a study that applied a hybrid optimization method for the parameter estimation of two nonlinear Muskingum models. It describes the advantages of an optimization model for estimating the parameters of such models, including ease of use for engineers who do not have much information about optimization models. It also explains the significance of the models to the simulation of flood routing.

Abstract: Recently, joint feature selection and subspace learning, which can perform feature selection and subspace learning simultaneously, is proposed and has encouraging ability on face recognition. In the literature, a framework of utilizing L 2,1-norm penalty term has also been presented, but some important algorithms cannot be covered, such as Fisher Linear Discriminant Analysis and Sparse Discriminant Analysis. Therefore, in this paper, we add L 2,1-norm penalty term on FLDA and propose a feasible solution by transforming its nonlinear model into linear regression type. In addition, we modify the optimization model of SDA by replacing elastic net with L 2,1-norm penalty term and present its optimization method. Experiments on three standard face databases illustrate FLDA and SDA via L 2,1-norm penalty term can significantly improve their recognition performance, and obtain inspiring results with low computation cost and for low-dimension feature. [Copyright &y& Elsevier]

Gradient-based optimization may not be suited if the objective and constraint functions in a nonlinear model predictive control (NMPC) optimization problem are not differentiable. Some well-known derivative-free optimization (DFO)-algorithms are investigated, and a novel warm-start modification to the Wedge DFO-algorithm is proposed. Together with a gradient-based SQP-algorithm these are applied to the NMPC problem and compared in a single-shooting NMPC formulation to a subsea oil-gas separation process. The findings are that DFO is significantly more robust against the numerical issues, compared to a gradient-based SQP tested. Moreover, the warm-start modification reduces the computational complexity. [ABSTRACT FROM AUTHOR]

The purpose of the optimum designs is to determine the optimal experimental conditions in terms of minimum variance of the estimated vector of parameters, so that statistical inferences can be generated as accurate as possible. This theory presupposes knowledge of the function relating the explanatory variables with the response variable. In this situation, usually get designs with support points as parameters in the model. Since p-point designs assume that the model function is known, these could not be optimal in some practical situations because they do not allow to test the goodness of fit of the assumed model. In this paper we present a generalization of the methodology proposed by to increase the number of support points in D-optimality criterion. An expression for the variance of the predicted response in terms of a constant δ will be provided, which allow to determine the points to be added to the D-optimal design. We propose a strategy for choosing δ. Finally we provide a pseudo-optimal design with (p + s) support points. [ABSTRACT FROM AUTHOR]

Because of their complex behavior, wastewater treatment processes (WWTPs) are very difficult to control. In this paper, the design and implementation of a nonlinear model-predictive control (NMPC) system are discussed. The proposed NMPC comprises a self-organizing radial basis function neural network (SORBFNN) identifier and a multiobjective optimization method. The SORBFNN with concurrent structure and parameter learning is developed as a model identifier for approximating the online states of dynamic systems. Then, the solution of the multiobjective optimization is obtained by a gradient method which can shorten the solution time of optimal control problems. Moreover, the conditions for the stability analysis of NMPC are presented. Experiments reveal that the proposed control technique gives satisfactory tracking and disturbance rejection performance for WWTPs. Experimental results on a real WWTP show the efficacy of the proposed NMPC for industrial processes in many applications. [ABSTRACT FROM PUBLISHER]

Abstract: In the present work a methodology to tackle the problem of simultaneous utilization of hydroelectric and conventional power units with the goal of optimizing power production operations over the short term is presented. Most problem formulations found in the literature result in the development of nonlinear optimization programs, which are solved with stochastic methods. The methodology presented in this paper leads to the development of a convex mixed integer quadratic programming (MIQP) model, which is a special type of nonlinear model that enables reaching the global optimum solution in short computational time. The efficiency of the proposed approach is demonstrated by its application to a realistic power production system. [Copyright &y& Elsevier]

Abstract: Block-oriented nonlinear models are appealing due to their simplicity and parsimony. Existing methods to identify the Wiener–Hammerstein model suffer from one or several drawbacks. This paper shows that it is possible to generate initial estimates in an alternative way. A fractional model parameterization is the key to the success of this approach. Advantages are that no more than two iterative optimizations are needed and that large model orders can be handled. As illustrated through a simulation example and experimental benchmark data, it gives superior initial estimates and comparable optimized results. [Copyright &y& Elsevier]

This study answers two questions raised in the parameter estimation optimization for the nonlinear Muskingum flood routing model. The first question is whether a new global optimum was still found after the existing global optimum had already been found. In order to fairly verify this question, a standard routing procedure for the nonlinear Muskingum model, which has not been clearly described previously, is proposed. Because the routing procedure was coded in a spreadsheet, any researcher can easily test it after downloading it. The second question is the reason why various approaches, such as Lagrange multiplier, Broyden–Fletcher–Goldfarb–Shanno (BFGS), genetic algorithm, harmony search and particle swarm optimization, have tackled only Wilson's data set as the parameter estimation optimization for the nonlinear Muskingum model, because Wilson's data have a unique structure which is differentiated from other data sets. This study also provides various data sets to compare. [ABSTRACT FROM AUTHOR]

Highlights: [•] A systematic procedure for the tuning of NMPC controllers is proposed. [•] The procedure highlights the intrinsic multi-objective nature of the NMPC. [•] The proposed procedure is successfully tested on two challenging case studies. [•] The proposed procedure is also successfully tested in presence of disturbances model mismatch and changing in the NMPC settings. [•] The proposed procedure results to be particularly effective in disturbance rejection for application where one variable is of key importance. [ABSTRACT FROM AUTHOR]

Highlights: [•] The solar tower power plant system (STPP) is divided into four main subsystems. [•] The energy balance of each subsystem has been developed. [•] A general nonlinear mathematical model of the studied system (STPP) has been presented. [•] Using numerical optimization methods, the nonlinear mathematical model has been solved. [•] The obtained results are presented and analyzed. [Copyright &y& Elsevier]

Reliable power and temperature control in pressurized water reactor (PWR) nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC), by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC) scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops new results on the use of model predictive control to regulate the speed of a permanent magnet synchronous motor where the design is based on a linearized state-space representation of the nonlinear model that describes the dynamics. The model predictive controller employs integral action, resulting in zero steady-state error and load torque disturbance rejection. Design in the presence of constraints is considered, where a quadratic programming procedure is used to solve the resulting on-line optimization problem during experimental verification by application to an industry-sized motor. [Copyright &y& Elsevier]

The possibility toapply the nonlinear model predictive control(NMPC) to the fixed-bed tubular reactor for methanol synthesis isbroached. The distributed nature of the methanol synthesis reactorimplies a simultaneous management of temporal and spatial dynamicsto control the conventional physical time-dependent parameters (temperature,flows, etc.) but also the axial migration of the temperature hot-spottypical of methanol synthesis. The approach can be easily extendedto other systems characterized by exothermic reactions. Special attentionis given to the prompt response of NMPC to ensure the online effectiveness.Theoretical and practical information is provided. [ABSTRACT FROM AUTHOR]

Abstract: This work highlights the gains of a fast nonlinear model-based predictive control (NMPC) scheme applied to a 10 kW proton exchange membrane fuel cell (PEMFC). The freshness of the approach is based on a particular parameterization of the control action to decrease the optimization problem dimension. Due to its short computational time, its reliability and its low sensitivity to noise, an artificial neural network (ANN) model is designed and used as a predictive model. The performance of the proposed control strategy is confirmed thanks to simulations through varying control scenarios. Results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. Moreover, for similar setpoint tracking accuracy, the proposed NMPC scheme appears to be thirty times faster than a classical NMPC strategy. Therefore, the fast NMPC scheme proposed in this work appears to be a promising candidate to achieve real-time implementation. [Copyright &y& Elsevier]