This paper aims at exploring the theoretical research and distributed filtering design of state estimation for sensor networked systems with quantized measurement and switching topologies. In a sensor network, each sensor node has an independent static logarithmic quantizer function, and the quantized measurement is transmitted to the filtering network via the wireless network. In the corresponding filtering network, each local estimator achieves distributed consistent state estimation of the plant based on the local measurement and the neighboring shared information. In particular, the design of the distributed filter fully takes into account the fact that the communication links between the nodes are not fixed. That is, the communication topology has random switching, and such random switching behavior is described using Markov chains with partially unknown transition probabilities. A set of linear matrix inequalities gives the sufficient conditions for the existence of the distributed filter, while ensuring that the filter error system has the desired H∞ performance. Finally, two numerical simulations show the effectiveness of the design method.
This paper deals with the trajectory tracking control problem of a planar underactuated tendon‐driven truss‐like manipulator (UTTM) by using fuzzy logic control methods, including type‐1 fuzzy and interval type‐2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon‐driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic‐based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type‐1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type‐1 fuzzy controllers, an interval type‐2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type‐1 and interval type‐2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type‐2 fuzzy‐based trajectory tracking controller provides better performance than the type‐1 counterpart in terms of tracking accuracy and capacity against uncertainties.
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