H∞ control for uncertain one‐sided Lipschitz nonlinear systems in finite frequency domain

The current article discusses the H_∞ disturbance attenuation control design problem for one‐sided Lipschitz systems in finite frequency domain. Models containing norm‐bounded parameter uncertainties, disturbances, and input nonlinearities are considered. By contrast to existing full frequency methods, the H_∞ controller is computed depending on the frequency ranges of disturbances. The finite frequency disturbance attenuation index is initially defined. Thanks to Finsler's lemma, sufficient and less conservative analysis conditions are also derived for the closed‐loop system. Then, synthesis conditions in the low, middle, and high frequency ranges as well as the whole frequency range, are formulated in terms of linear matrix inequalities. At last, to prove the effectiveness and the superiority of the proposed approach, a physical example is used and a comparative study is done.     

Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Robust predictive extended state observer for a class of nonlinear systems with time-varying input delay

ABSTRACT

This paper deals with asymptotic stabilisation of a class of nonlinear input-delayed systems via dynamic output feedback in the presence of disturbances. The proposed strategy has the structure of an observer-based control law, in which the observer estimates and predicts both the plant state and the external disturbance. A nominal delay value is assumed to be known and stability conditions in terms of linear matrix inequalities are derived for fast-varying delay uncertainties. Asymptotic stability is achieved if the disturbance or the time delay is constant. The controller design problem is also addressed and a numerical example with an unstable system is provided to illustrate the usefulness of the proposed strategy.     

Robust orientation control of multi‐DOF cell based on uncertainty and disturbance estimation

Multiple degrees‐of‐freedom (multi‐DOF) cell orientation control is a vital important technique involved in single cell surgery applications. Currently, few studies have been performed toward automation of multi‐DOF cell orientation control using robotically controlled optical tweezers. In this paper, a robust control framework is developed to perform multi‐DOF cell rotational control with consideration of model uncertainties and external disturbances. Both simulation and experimental studies are presented to illustrate the performance of the proposed control strategy. The main contributions of this work lie in that this is the first time to develop a unified framework to achieve multi‐DOF cell orientation control without the need for accurate dynamic model parameters and/or any knowledge about uncertainty characteristic, which greatly enhances the robustness of the overall system.     

Pinning exponential synchronisation and passivity of coupled delayed reaction–diffusion neural networks with and without parametric uncertainties

In this paper, we focus on the pinning exponential synchronisation and passivity of coupled reaction–diffusion neural networks (CRDNNs) with and without parametric uncertainties, respectively. On the one hand, with the help of designed nonlinear pinning controllers and Lyapunov functional method, sufficient conditions are established to let the CRDNNs with hybrid coupling and mixed time-varying delays realise exponential synchronisation and passivity. On the other hand, considering that the external perturbations may lead the reaction–diffusion neural networks (RDNNs) parameters to containing uncertainties, the robust pinning exponential synchronisation and robust pinning passivity for coupled delayed RDNNs with parametric uncertainties are investigated by designing appropriate pinning control strategies. Finally, the effectiveness of the theoretical results are substantiated by the two given numerical examples.     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.      

A dual Newton strategy for tree‐sparse quadratic programs and its implementation in the open‐source software treeQP

This paper presents a dual Newton scheme for tree‐sparse quadratic programs as they may arise in the field of stochastic programming. Previous work suggests to introduce auxiliary variables to decompose the tree into scenarios and use Newton's method to solve a dual problem formulation. Following a different approach, we apply the same principle directly on the tree‐sparse problem, avoiding the increase in dimensionality. In combination with a tailored algorithm for the calculation of the step direction, which is typically the most expensive operation per iteration, the proposed algorithm achieves a linear complexity in the number of nodes and supports parallel processing of the tree branches in a stage‐wise fashion. An open‐source implementation of the presented dual Newton strategy is publicly available in treeQP , a toolbox of open‐source solvers for tree‐sparse quadratic programs.     

[制造前沿]创新·强基·智能——建设制造强国

世界工业发达国家美、德、英等国“重振制造业”的发展战略均将智能制造作为发展趋势。我国制造业总体而言，大而不强。我国已是世界制造第一大国，但创新能力弱、缺乏关键核心及共性技术。我国《制造强国发展战略》聚焦“五大工程”，其中将智能制造作为主攻方向，而“五大工程”是相互联系和相互支持的。数字化设计与制造是智能制造关键共性技术，而建模与仿真是数字化设计与制造的科学基础。要全面实施智能制造，建成制造强国，任重而道远。     

精密磨床转台静压轴承的强健化设计

针对精密磨床转台静压轴承性能仿真数据与其回转精度实测数据不一致的问题，为提高转台静压轴承的设计水平，提出了静压轴承的强健化设计方法。该方法考虑了轴承结构参数在轴承全生命周期内的变化，分析了半径间隙、油腔尺寸、节流比及润滑油黏度在设计、加工、装配和服役4个阶段的变化规律，重点研究了半径间隙在各阶段对轴承性能造成的影响，给出了转台静压轴承的强健化设计流程。通过一个径向轴承的设计实例对该强健化设计方法进行了说明，并将设计结果与传统设计进行了对比。对比的指标是轴承在4个阶段的刚度、承载力和温升，这3个指标对轴承回转精度有直接影响。研究表明：进行强健化设计后，在生命周期内静压轴承的刚度和承载力的变化量为10%~27%，温度的变化量为0.5%~1.6%，较传统设计更能保证转台回转精度的稳定。