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	1. Trajectory tracking of manipulator with full state constraints based on observer
	ZHANG Su-Su,CUI Ming-Yue
	Mathematics 17 May 2022
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	Abstract:This paper studies the trajectory tracking of manipulator under disturbances and full state constraints. Firstly, for time-varying disturbances of manipulator, under reasonable assumptions, a disturbance observer is designed to estimate time-varying disturbances. Then, by making full use of the properties of the system, skillfully constructs a barrier Lyapunov function, and designs a vector state feedback tracking controller based on disturbance observer, so that the tracking error can be sufficiently small by adjusting the parameters while keeping the state limited. Finally, the effectiveness of the control strategy is verified by a two degree of freedom manipulator system.
	TO cite this article:ZHANG Su-Su,CUI Ming-Yue. Trajectory tracking of manipulator with full state constraints based on observer[OL].[17 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757772


	2. Generalized Set Order Relations and Scalarization in Set Optimization with Applications to Continuity and Robustness
	ZHAO Yi,CHEN Chun-Rong
	Mathematics 17 January 2020
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	Abstract:In this paper, mainly using linear and nonlinear scalarization characterizations of generalized set order relations, solution continuity results for parametric set optimization problems and robustness equivalent characterizations for uncertain multiobjective optimization problems are discussed, respectively. The results obtained generalize corresponding ones in the literature.
	TO cite this article:ZHAO Yi,CHEN Chun-Rong. Generalized Set Order Relations and Scalarization in Set Optimization with Applications to Continuity and Robustness[OL].[17 January 2020] http://en.paper.edu.cn/en_releasepaper/content/4750559


	3. Distributed fuzzy adaptive iterative learning control with initial-state learning for consensus of multi-agent systems with uncertain communication topology structure
	WU Hui,LI Junmin
	Mathematics 26 April 2017
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	Abstract:Distributed consensus problem is addressed in this paper for linearly parameterized multi-agent systems with uncertain communication topology structure under initial-state learning condition. T-S fuzzy models are presented to describe the uncertain communication topology structure, and a distributed iterative learning control protocol is proposed without using any global information for the consensus problem. The AILC protocols are designed with distributed initial-learning and it is not essential to fix the initial value at the start of each iteration. It is proved that the proposed protocol ensures all the internal signals in the multi-agnt system are bounded and the follower agents track the leader exactly on [0,T]. Sufficient conditions of perfectly consensus for multi-agent systems are obtained by appropriately constructing Lyapunov function. The formation control problem is also studied by converting to the consensus problem. Finally, the simulation examples are given to verify the efficacy of the theoretical analysis.
	TO cite this article:WU Hui,LI Junmin. Distributed fuzzy adaptive iterative learning control with initial-state learning for consensus of multi-agent systems with uncertain communication topology structure[OL].[26 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726586


	4. A Sequential Bundle Method for Solving a MPEC Problem
	Xia Zunquan,Shen Jie ,Pang Liping
	Mathematics 19 October 2005
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	Abstract:In this paper we consider a convex MPEC problem with a nondifferentiable convex objective function and constraints separable in two variable vectors whose second variable vector belongs to the set of optimal solutions of the constraint problem. A sequential bundle method for dealing with this kind of problem is presented. It is constructed by combining a proximal bundle method due to Hintermuller (2001) and a descent proximal level bundle method due to Brannlund, Kiwiel and Lindberg (1995). The first bundle method is used to provide a starting point at the beginning of each iteration of the sequential iterate process and the second one is used to find an (approximate) optimal solution of the constraint problem at each iteration of the sequential iterate process. The convergence analysis given in the last section shows that under some conditions the algorithm presented can terminate at an approximate solution in finite steps according to a given tolerance error.
	TO cite this article:Xia Zunquan,Shen Jie ,Pang Liping. A Sequential Bundle Method for Solving a MPEC Problem[OL].[19 October 2005] http://en.paper.edu.cn/en_releasepaper/content/3308


	5. First Order Necessary Optimality Conditions for a Class of Nonsmooth Generalized Semi-Infinite Optimization Problems
	Pang Liping,Wang Mingzheng,Xia Zunquan
	Mathematics 19 October 2005
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	Abstract: In this paper, we study two classes of generalized semi-infinite nonsmooth optimization problems, One is the nonsmooth convex generalized semi-infinite programming problem. Another is the nonsmooth Lipschitz semi-infinite programming problem. First order necessary optimality conditions for these two kinds of problems are obtained using the differentiability properties of the optimal value functions or bounds for the directional derivatives of the optimal value function.
	TO cite this article:Pang Liping,Wang Mingzheng,Xia Zunquan. First Order Necessary Optimality Conditions for a Class of Nonsmooth Generalized Semi-Infinite Optimization Problems[OL].[19 October 2005] http://en.paper.edu.cn/en_releasepaper/content/3307

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