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A Sequential Linear Programming algorithm for economic optimization of Hybrid Renewable Energy Systems

JOURNAL OF PROCESS CONTROL 2019年 74卷 189-201页

作者： Vaccari, M. Mancuso, G. M. Riccardi, J. Cantu, M. Pannocchia, G. Univ Pisa Dept Civil & Ind Engn Largo L Lazzarino 2 I-56126 Pisa Italy Enel Green Power Innovat & Sustainabil Via Andrea Pisano 120 I-56120 Pisa Italy Enel Engn & Res Via Andrea Pisano 120 I-56120 Pisa Italy

Combining renewable energy sources, as photovoltaic arrays (PV), wind turbine (WT), biomass fuel generators (BM), with back-up units to form a Hybrid Renewable Energy System (HRES) can provide a more economic and reliable energy supply architecture compared to the separate usage of such units. In this work an optimization tool for a general HRES is developed: it generates an operating plan over a specified time horizon of the setpoints of each device to meet all electrical and thermal load requirements with possibly minimum operating costs. A large number of devices, such as conventional and renewable source generators, mandatory and deferrable adjustable electrical loads, batteries, combined heat and power configurations are modeled with high fidelity. The optimization tool is based on a Sequential Linear Programming (SLP) algorithm, equipped with trust region, which is able to efficiently solve a general nonlinear program. A case study of a real HRES in Tuscany is presented to test the major functionalities of the developed optimization tool. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Energy systems numerical optimization algorithms Sequential Linear Programming Hybrid Renewable Energy Systems (HRES)

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Real-time tuning of PID controller based on optimization algorithms for a quadrotor

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2022年第3期94卷 418-430页

作者： Can, Muharrem Selim Ercan, Hamdi Iskenderun Tech Univ Dept Av Iskenderun Turkey Erciyes Univ Dept Av Kayseri Turkey

Purpose This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor. Design/methodology/approach The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations. Findings In this study, the most efficient control parameters of the quadrotor are selected using evolutionary optimization algorithms in real-time simulations. The control parameters of PID directly affect the controller's performance that position error and stability improved by tuning the parameters. Therefore, the optimization algorithms can be used to improve the trajectory tracking performance of the quadrotor. Practical implications The online optimization result showed that evolutionary algorithms improve the performance of the trajectory tracking of the quadrotor. Originality/value This study states the design of an optimized controller compared with manually tuned controller methods. Fitness functions are defined as a custom fitness function (overshoot, rise-time, settling-time and steady-state error), mean-square-error, root-mean-square-error and sum-square-error. In addition, all the simulations are performed based on a realistic simulation environment. Furthermore, the optimization process of the parameters is implemented in real-time that the proposed controller searches better parameters with real-time simulations and finds the optimal parameter online.

关键词： Nonlinear control numerical optimization algorithms Proportional-integral-derivative (PID) control Quadrotor dynamics Real-time simulations

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Tailored presolve techniques in branch-and-bound method for fast mixed-integer optimal control applications

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第6期44卷 3139-3167页

作者： Quirynen, Rien Di Cairano, Stefano Mitsubishi Elect Res Labs Control Auton Cambridge MA USA

Mixed-integer model predictive control (MI-MPC) can be a powerful tool for controlling hybrid systems. In case of a linear-quadratic objective in combination with linear or piecewise-linear system dynamics and inequality constraints, MI-MPC needs to solve a mixed-integer quadratic program (MIQP) at each sampling time step. This paper presents a collection of exact block-sparse presolve techniques to efficiently remove decision variables, and to remove or tighten inequality constraints, tailored to mixed-integer optimal control problems. In addition, we describe a novel approach based on a heuristic presolve algorithm to compute a feasible but possibly suboptimal MIQP solution. We present benchmarking results for a C code implementation of the proposed BB-ASIPM solver, including a branch-and-bound (B & B) method with the proposed tailored presolve techniques and an active-set based interior point method (ASIPM), compared against multiple state-of-the-art MIQP solvers on a case study of motion planning with obstacle avoidance constraints. Finally, we demonstrate the feasibility and computational performance of the BB-ASIPM solver in embedded system on a dSPACE Scalexio real-time rapid prototyping unit for a second case study of stabilization for an underactuated cart-pole with soft contacts.

关键词： hybrid model predictive control mixed-integer programming numerical optimization algorithms

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Direct zigzag search for discrete multi-objective optimization

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COMPUTERS & OPERATIONS RESEARCH 2015年第Sep.期61卷 100-109页

作者： Wang, Honggang Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08855 USA

Multiple objective optimization (MOO) models and solution methods are commonly used for multicriteria decision making in real-life engineering and management applications. Much research has been conducted for continuous MOO problems, but MOO problems with discrete or mixed integer variables and black-box objective functions arise frequently in practice. For example, in energy industry, optimal development problems of oil gas fields, shale gas hydraulic fracturing, and carbon dioxide geologic storage and enhanced oil recovery, may consider integer variables (number of wells, well drilling blocks), continuous variables (e.g. bottom hole pressures, production rates), and the field performance is typically evaluated by black-box reservoir simulation. These discrete or mixed integer MOO (DMOO) problems with black-box objective functions are more challenging and require new MOO solution techniques. We develop a direct zigzag (DZZ) search method by effectively integrating gradient-free direct search and zigzag search for such DMOO problems. Based on three numerical example problems including a mixed integer MOO problem associated with the optimal development of a carbon dioxide capture and storage (CCS) project, DZZ is demonstrated to be computationally efficient. The numerical results also suggest that DZZ significantly outperforms NSGA-II, a widely used genetic algorithms (GA) method. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Multiple criteria decision making Pareto optimum Gradient free direct search Blackbox simulation numerical optimization algorithms Integer and mixed integer programming

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A Perturbed Chord (Newton-Kantorovich) Method for Constrained Nonlinear Model Predictive Control

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IFAC-PapersOnLine 2016年第18期49卷 253-258页

作者： Butts, Ken Dontchev, Asen Huang, Mike Kolmanovsky, Ilya Toyota Technical Center MI48105 United States the University of Michigan MI48109 United States The University of Michigan MI48109 United States

An approach to the computational implementation of Constrained Nonlinear Model Predictive Control (CNMPC) algorithms is described based on exploiting the modified Newton's method originally proposed by Kantorovich for solving nonlinear equations. This method is distinguished by avoiding the need to compute the Jacobian at each iteration while still ensuring convergence. In this paper, new results characterizing the convergence of this method while accounting for perturbations that may result from various approximations are obtained. Examples illustrating the potential of the proposed approach for spacecraft attitude control and diesel engine air path control are reported. They indicate that good tracking performance can be achieved and constraints can be enforced while the computational effort is reduced. © 2016

关键词： Model predictive control Attitude control Constrained optimization Diesel engines Newton Raphson method Nonlinear equations Nonlinear systems numerical methods Predictive control systems Constrained controls Engine control Kantorovich method numerical optimization algorithms Spacecraft attitude control

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Optimal Trajectories and State Estimation Based on Sparsity-in-Time

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IFAC-PapersOnLine 2017年第1期50卷 15349-15354页

作者： Ikeda, Takuya Kashima, Kenji Kyoto University Kyoto Japan Japan Science and Technology Agency CREST Saitama Japan

In this paper, we consider optimal trajectories based on the sparsity-in-time of control input. This optimization constrains the state partially on specific time instants while minimizing the L0norm of control. The problem formulation is closely related with state estimation when the system is corrupted by sparse noise. For the analysis, we describe the relationship between the optimal trajectories and Lpoptimal control with 0 © 2017

关键词： Trajectories Convex optimization Motion planning numerical methods State estimation numerical optimization algorithms Optimal controls Optimal trajectories Problem formulation sparsity Specific time Time of control

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A Perturbed Chord (Newton-Kantorovich) Method for Constrained Nonlinear Model Predictive Control

A Perturbed Chord (Newton-Kantorovich) Method for Constraine...

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10th IFAC Symposium on Nonlinear Control Systems (NOLCOS)

作者： Butts, Ken Dontchev, Asen Huang, Mike Kolmanovsky, Ilya Toyota Tech Ctr Ann Arbor MI 48105 USA Math Reviews AMS Ann Arbor MI 48109 USA Univ Michigan Ann Arbor MI 48109 USA

Model predictive control requires the solution of a sequence of continuous optimiza- tion problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm (developed in a com- panion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the effectiveness of the proposed approach. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Esevier Ltd. All rights reserved.

关键词： Model Predictive Control Nonlinear Systems Constrained Control numerical optimization algorithms Newton-Kantorovich Method Spacecraft Attitude Control Engine Control Powertrain Control

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Maximization of Data Gathering in Clustered Wireless Sensor Networks

Maximization of Data Gathering in Clustered Wireless Sensor ...

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Wang, Tianqi Heinzelman, Wendi Seyedi, Alireza

ISBN: (纸本)9781424456383

In this paper, we investigate the maximization of the amount of gathered data in a clustered wireless sensor network (WSN). The amount of gathered data is maximized by (1) choosing the optimal transmit power, and (2) selecting the optimal cluster head. For problem (1), we find closed-form solutions for the optimal or near optimal transmit power of cluster members (CM). For problem (2), we propose a near optimal cluster head selection (CHS) algorithm. The communication burden and computational complexity of CHS only grow linearly with the size of the cluster. In the proposed algorithms, iterations have been avoided in order to significantly lower the complexity of the algorithms compared with traditional iteration-based numerical optimization algorithms, making these approaches suitable for use in energy-constrained wireless sensor networks. The optimization gain is shown to be significant.

关键词： clustered wireless sensor networks computational complexity computational complexity data gathering maximization numerical optimization algorithms optimal transmit power optimisation wireless sensor networks

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On certain optimization methods with finite-step inner algorithms for convex finite-dimensional problems with inequality constraints

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Computational Mathematics and Mathematical Physics 2007年第12期47卷 1928-1937页

作者： Antipin, I.P. Ishmukhametov, A.Z. Karyukina, Yu.G. Dorodnicyn Computing Center Russian Academy of Sciences Moscow 119991 ul. Vavilova 40 Russian Federation

numerical methods are proposed for solving finite-dimensional convex problems with inequality constraints satisfying the Slater condition. A method based on solving the dual to the original regularized problem is proposed and justified for problems having a strictly uniformly convex sum of the objective function and the constraint functions. Conditions for the convergence of this method are derived, and convergence rate estimates are obtained for convergence with respect to the functional, convergence with respect to the argument to the set of optimizers, and convergence to the g-normal solution. For more general convex finite-dimensional minimization problems with inequality constraints, two methods with finite-step inner algorithms are proposed. The methods are based on the projected gradient and conditional gradient algorithms. The paper is focused on finite-dimensional problems obtained by approximating infinite-dimensional problems, in particular, optimal control problems for systems with lumped or distributed parameters. © 2007 Pleiades Publishing, Ltd.

关键词： Convex finite-dimensional optimization problems Inequality constraints numerical optimization algorithms Regularization methods

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numerical optimizations in Inverse Kinematics of Robotics Hand 6th

Numerical Optimizations in Inverse Kinematics of Robotics Ha...

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6th Symposium on Mechanism Design for Robotics (MEDER)

作者： Fonte, Cosimo Cocconcelli, Marco Ceccarelli, Marco Univ Modena & Reggio Emilia Reggio Emilia Italy Univ Roma Tor Vergata Rome Italy

ISBN: (纸本)9783031673856;9783031673832;9783031673825

The analysis of hand movement focuses on two primary optimization issues: the calibration of the hand's kinematic model parameters and the calculation of joint angles for specific postures. Typically, these problems are addressed independently. Calibration or scaling is resolved using static postures, whereas angle calculation can be performed through geometric or numerical methods. This work proposes a comparative analysis of three numerical optimization algorithms to solve these two problems simultaneously, without the aid of specific static tests but using dynamic trials only. Four tasks were selected based on specific literature to cover relevant grasping activities. The analysis was conducted through error analysis, using the discrepancies between the measured and estimated positions of markers. Results demonstrated error reduction in marker position when employing appropriate algorithm.

关键词： Hand Kinematics numerical optimization algorithms Joint Angle Determination Mutibody motion capture

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