检索结果-内蒙古大学图书馆

A design technique for fast sampled-data nonlinear model predictive control with convergence and stability results

INTERNATIONAL JOURNAL OF control 2020年第1期93卷 81-97页

作者： Steinboeck, Andreas Guay, Martin Kugi, Andreas Vienna Univ Technol Fac Elect Engn & Informat Technol Automat & Control Inst Vienna Austria Queens Univ Dept Chem Engn Kingston ON Canada

In this study, a sampled-data nonlinear model predictive control scheme is developed. The control algorithm uses a prediction horizon with variable length, a terminal constraint set, and a feedback controller defined on this set. Following a suboptimal solution strategy, a defined number of steps of an iterative optimisation routine improve the current input trajectory at each sampling point. The value of the objective function monotonically decreases and the state converges to a target set. A discrete-time formulation of the algorithm and a discrete-time design model ensure high computational efficiency and avoid an ad hoc quasi-continuous implementation. This design technique for a fast sampled-data nonlinear model predictive control algorithm is the main contribution of the paper. Based on a benchmark control problem, the performance of the developed control algorithm is assessed against state-of-the-art nonlinear model predictive control methods available in the literature. This assessment demonstrates that the developed control algorithm stabilises the system with very low computational effort. Hence, the algorithm is suitable for real-time control of fast dynamical systems.

关键词： nonlinear model predictive control receding horizon control real-time optimisation sampled-data control dynamic optimisation

来源：评论

学校读者我要写书评

暂无评论

Neighboring extremal nonlinear model predictive control of a rigid body on SO(3)

引用

ROBOTICA 2023年第4期41卷 1313-1334页

作者： Bagherzadeh, Shiva Karimpour, Hossein Keshmiri, Mehdi Isfahan Univ Technol Dept Mech Engn Esfahan Iran Univ Isfahan Fac Engn Dept Mech Engn Esfahan Iran

The issue of implementing nonlinear model predictive control (NMPC) on mechanical systems evolving on special orthogonal group (SO(3)) is taken into consideration in the first place. Necessary conditions of optimality are extracted based on Lie group variational integrators, leading to a two-point boundary value problem (TPBVP) which is solved using sensitivity derivatives and indirect shooting methods. Fast Newton-like methods referred to as fast solvers which are commonly used to solve the TPBVP are established based on the repetition of a nonlinear process. The numerical schemes employed to alleviate the computation burden consist of eliminating some constraint-related but non-essential terms in the trend of sensitivity derivatives calculation and for solving the TPBVP equations. As another claim, assuming that a first attempt to resolve the NMPC problem is accessible, the problem subjected to some changes in its initial conditions (due to some re-planning schemes) can be resolved cost-effectively based on it. Instead of solving the whole optimization process from the scratch, the optimal control inputs and states of the system are updated based on the neighboring extremal (NE) method. For this purpose, two approaches are considered: applying NE method on the first solution that leads to a neighboring optimal solution, or assisting this latter by updating the NMPC-related optimization using exact TPBVP equations at some predefined intermediate steps. It is shown through an example that the first method is not accurate enough due to error accumulations. In contrast, the second method preserves the accuracy while reducing the computation time significantly.

关键词： nonlinear model predictive control neighboring extremal Lie group variational integrators sensitivity derivatives computation time

来源：评论

学校读者我要写书评

暂无评论

Safety-Aware nonlinear model predictive control for Physical Human-Robot Interaction

引用

IEEE ROBOTICS AND AUTOMATION LETTERS 2021年第3期6卷 5665-5672页

作者： Oleinikov, Artemiy Kusdavletov, Sanzhar Shintemirov, Almas Rubagotti, Matteo Nazarbayev Univ Sch Engn & Digital Sci Dept Robot & Mechatron Nur Sultan 010000 Kazakhstan

This letter proposes a nonlinear model predictive control (NMPC) approach for real-time planning of point-to-point motions of serial robot manipulators that share their workspace with a human. The NMPC law solves a nonlinear program online, based on a kinematic model, and guarantees safety by constraining the robot speed within the time-varying bounds determined by the speed-and-separation-monitoring (SSM) principle. Closed-loop stability is proven in detail, and the performance (in terms of productivity) of the proposed method is tested against standard SSM schemes via experiments on a Kinova Cen3 robot.

关键词： Optimization and optimal control human-aware motion planning physical human-robot interaction nonlinear model predictive control speed and separation monitoring

来源：评论

学校读者我要写书评

暂无评论

Robust nonlinear model predictive control for reference tracking of dynamic positioning ships based on nonlinear disturbance observer

引用

OCEAN ENGINEERING 2020年 215卷 107885-107885页

作者： Yang, Hualin Deng, Fang He, Yan Jiao, Dongmei Han, Zhaolin Qingdao Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266061 Peoples R China

This paper presents a robust nonlinear model predictive control (NMPC) scheme for the dynamic positioning (DP) ship subject to time-varying environmental disturbances and input saturation. The control objective is to steer the perturbed DP ship to track prespecified reference trajectories. A nonlinear disturbance observer (NDO) is designed to estimate the unknown disturbances and uncertainties. The NMPC optimization problem is formulated to reject the disturbances by combining the disturbance estimates into the prediction model. The partial asymptotical stability of NMPC is guaranteed without considering any terminal costs and terminal constraints. Furthermore, detail time-iteration NMPC algorithm for DP ships is presented. Simulations of the proposed NDO-based NMPC scheme are implemented for the modes of setpoint regulation and trajectory tracking, respectively. Simulation results well demonstrate the effectiveness and robustness of the proposed control law.

关键词： Dynamic positioning nonlinear model predictive control nonlinear disturbance observer Partial stability Setpoint regulation Trajectory tracking

来源：评论

学校读者我要写书评

暂无评论

Learning-based nonlinear model predictive control with accurate uncertainty compensation

引用

nonlinear DYNAMICS 2021年第4期104卷 3827-3843页

作者： Xie, Jingjie Zhao, Xiaowei Dong, Hongyang Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England

A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system's safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme.

关键词： nonlinear model predictive control Learning-based control Adaptive control Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

System Identification and nonlinear model predictive control of a Solid Oxide Fuel Cell

引用

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2010年第10期49卷 4800-4808页

作者： Bhattacharyya, Debangsu Rengaswamy, Raghunathan Texas Tech Univ Dept Chem Engn Lubbock TX 79409 USA W Virginia Univ Dept Chem Engn Morgantown WV 26506 USA

Solid oxide fuel cells (SOFCs) are high temperature fuel cells with a strong potential for stationary power house applications. However, considerable challenges need to be overcome to connect these cells to the power grid. The fluctuating grid demand has to be met without sacrificing the cell efficiency and causing structural/material damage to the system. This requirement coupled with fast and highly nonlinear transients of the transport variables results in a challenging control problem. This paper is on synthesizing a controller that can address some of these challenges. For using in the model predictive controller (MPC), input output models are identified from the data generated by a detailed dynamic model. A traditional SISO control and a novel MIMO control are considered here. In the SISO control problem, power is the controlled variable (CV) and H-2 flow is the manipulated variable (MV). In the MIMO control problem, power and the utilization factor (UF) of the fuel are the CVs while voltage and the flow of H-2 are the MVs. The identification study shows that the nonlinear NAARX models with properly chosen cross terms can improve the model performance significantly in a MIMO problem. The results from the control study indicate that a well-tuned proportional integral derivative (PM) controller is sufficient for the single input single output (SISO) power control of a tubular SOFC. It also shows that the mutiple input multiple output (MIMO) control of power and the UF is highly interactive and necessitates a nonlinear model predictive controller (NMPC). Without using any additional hardware such as an ultracapacitor or battery pack, the designed NMPC could satisfy a step change in load with acceptable overshoot in power and the UF. A well-tuned PID controller is found to perform poorly for the MIMO problem. On the basis of these findings, future work will focus on the development of nonlinear predictive control approaches for stack-level control of tubular solid

关键词： System identification multiple input multiple output systems single input single output systems Supercapacitors predictive controllers Hydrogen Urofacial syndrome Power control nonlinear model predictive control fuel batteries HIGH-TEMPERATURE FUEL CELLS Energy sources Solid oxide fuel cells step change nonlinear predictive control

来源：评论

学校读者我要写书评

暂无评论

Multistage nonlinear model predictive control for pumping treatment in hydraulic fracturing

引用

AICHE JOURNAL 2022年第3期68卷 e17537-e17537页

作者： Lin, Kuan-Han Eason, John P. Biegler, Lorenz T. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Exenity LLC Pittsburgh PA USA

Hydraulic fracturing has gained increasing attention as it allows the constrained natural gas and crude oil to flow out of low-permeability shale formations and significantly increase production. Perilous operating states of extremely high pressure also raise some safety concerns, requiring us to formulate an appropriate dynamic model, and provide a careful engineering control to ensure safe operating conditions. Moreover, uncertainties due to spatially varying rock properties increase the difficulties in control of the fracturing process. In this work, we formulate a first-principles model by considering the fracture evolution, mass transport of substances in the slurry, changing fluid properties, and the monitored operating pressure on the ground level. Next, we implement nonlinear model predictive control (NMPC) to control the process under a set of final requirements and process constraints. Our results show that the performance of standard NMPC degrades when the rock uncertainty causes the parameter mismatch between the process and the predictive model in the controller. With standard NMPC, designed with a nominal model, the process fails to meet the terminal requirements of fracture geometry, and pressure is violated in one of the parameter mismatch cases. Therefore, we resort to multistage NMPC, which considers uncertainty evolution in a scenario tree with separate control sequences to address constraint violations. We demonstrate that multistage NMPC presents good performance by showing constraint satisfaction whether the uncertain rock parameter realization is time-invariant or time-variant. We also simulate the process with multistage NMPC including different numbers of scenarios and compare their control performance. Our investigation demonstrates that multistage NMPC effectively manages parametric uncertainties attributed to non-homogeneous rock formation, and provides a promising control strategy for the hydraulic fracturing process.

关键词： hydraulic fracturing nonlinear model predictive control nonlinear programming process modeling robust nonlinear model predictive control

来源：评论

学校读者我要写书评

暂无评论

A novel optimal control design for unknown nonlinear systems based on adaptive dynamic programming and nonlinear model predictive control

引用

ASIAN JOURNAL OF control 2022年第4期24卷 1638-1649页

作者： Hu, Wei Zhang, Guoshan Zheng, Yuqing Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

This paper presents a novel adaptive optimal control algorithm by combining adaptive dynamic programming with nonlinear model predictive control for unknown continuous-time affine nonlinear systems. The adaptive optimal control design is realized by the model-critic-actor architecture. model neural network, critic neural network and actor neural network are constructed to approximate the system dynamics, the cost function and the optimal control law respectively. The random initialization of neural networks usually influences the control performance, so three neural networks are initialized properly to obtain the suitable initial values so that the control performance is improved. Especially, actor neural network is initialized to approximate the near-optimal control law which is obtained from nonlinear model predictive control. The convergence of the proposed algorithm is proved by the Lyapunov theory. Finally, simulation results are provided to illustrate the effectiveness of the proposed algorithm.

关键词： adaptive control adaptive dynamic programming neural networks nonlinear model predictive control optimal control

来源：评论

学校读者我要写书评

暂无评论

Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation

引用

AICHE JOURNAL 2022年第11期68卷 e17830-e17830页

作者： Wei, Lai McCloy, Ryan Bao, Jie Cranney, Jesse Univ New South Wales UNSW Sch Chem Engn Sydney NSW Australia Australian Natl Univ Res Sch Astron & Astrophys Canberra ACT Australia

Modern chemical processes need to operate around time-varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time-varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.

关键词： contraction theory discrete-time nonlinear systems graph theory nonlinear model predictive control stability design

来源：评论

学校读者我要写书评

暂无评论

Decentralized nonlinear model predictive control of a multimachine power system

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 106卷 358-372页

作者： Patil, Bhagyesh V. Sampath, L. P. M. I. Krishnan, Ashok Eddy, Foo Y. S. Cambridge Ctr Adv Res & Educ Singapore CARES Singapore Singapore Nanyang Technol Univ Interdisciplinary Grad Sch Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

This paper proposes a novel decentralized nonlinear model predictive control approach for excitation control in multimachine power systems. A key feature of the proposed approach is the reduction of the multimachine control problem to multiple single-machine control problems. These reduced size control problems only require direct measurements from individual generators, thereby making implementation simple in practice. The effectiveness of the proposed approach is demonstrated through computer simulations on a one small-size Western System Coordinating Council (WSCC) and one large-size New England power system under a wide range of operating conditions. The performance of the proposed approach is evaluated in terms of damping power system oscillations, critical time enhancement, and control signal energy consumption. The results are compared with those obtained using power system stabilizer and feedback linearization based control schemes and found to be impressive.

关键词： Bernstein polynomials Global optimization Decentralized control nonlinear model predictive control Multimachine power systems Excitation control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：