The interactions between subsystems are important for large-scale systems. We introduce a local strongly coupled system which coupled by random communication between subsystems. Due to the intermittent communication, ...
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ISBN:
(纸本)9781467313971
The interactions between subsystems are important for large-scale systems. We introduce a local strongly coupled system which coupled by random communication between subsystems. Due to the intermittent communication, it is difficult to apply the standard Kalman or robust filter to design procedures to such systems. In this paper, we addressed the distributed robust filter design method for this kind of system based on the consensus idea. The main result is a sufficient condition which guarantees a suboptimal level of disagreement of estimates in a coupled network of estimators. The condition is formulated in terms of feasibility of biaffine matrix inequalities (BMIs). The generic algorithm is used to treat the bilinear relation between filter parameters and the interconnection gains. The proposed approach is applied to the problem of formation-based robust synchronization. The numerical simulations show the effectiveness of the proposed filtering method.
The development of an innovative H∞ controller for looper and tension control in hot strip finishing mills is traced based on approximately linearized model. This solution has been considered thanks to its well- know...
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The development of an innovative H∞ controller for looper and tension control in hot strip finishing mills is traced based on approximately linearized model. This solution has been considered thanks to its well- known robustness and simplicity characteristics concerning disturbances' attenuation. The controller is designed based on an optimal problem with linear matrix inequality (LMI) constraints, and the problem is solved by the mincx function of Matlab LMI Toolbox. Simulation results show the effectiveness of the proposed controller compared with conventional ones.
This paper studies the leader-following consensus problem for a group of agents with identical linear systems subject to control input saturation. We focus on two classes of linear systems, neutrally stable systems an...
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ISBN:
(纸本)9781457710957
This paper studies the leader-following consensus problem for a group of agents with identical linear systems subject to control input saturation. We focus on two classes of linear systems, neutrally stable systems and double integrator systems. For neurally stable systems, we establish that global consensus can be achieved by linear local feedback laws over an undirected fixed or a switching communication topology. For double integrator systems, we establish that global consensus can be achieved by linear local feedback laws over a fixed communication topology and, with the help of a simple saturation function in the local feedback laws, global consensus can also be achieved over a switching undirected topology. Simulation results illustrate the theoretical results.
This paper studies the finite-time cooperative tracking problem for networked Lagrange systems with a time-varying leader's generalized coordinate derivative. First, a finite-time state feedback control protocol i...
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ISBN:
(纸本)9781457710957
This paper studies the finite-time cooperative tracking problem for networked Lagrange systems with a time-varying leader's generalized coordinate derivative. First, a finite-time state feedback control protocol is proposed for each follower by using only local information, under which the states of the followers are shown to converge to those of the leader in finite time. The results of the static feedback design are then extended to those of the dynamic feedback design. The finite-time cooperative tracking problem of networked Lagrange systems over a directed switching communication topology is proposed. With the help of a so-called finite-time consensus-based observer, we show that cooperative tracking of networked Lagrange systems can be achieved in finite time if the leader has directed information paths to each follower at each time instant and the control parameters satisfy certain conditions.
In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws...
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In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.
Brain Computer Interface (BCI) provides a direct communication channel from brain to peripheral equipment. Common Spatial Patterns (CSP) is wildly used to extract features for electroencephalogram (EEG). However, basi...
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Vector quantization is one of high performance and popular methods for data compression. But it is extremely time consuming during the encoding process. In this paper, a fast encoding algorithm for vector quantization...
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Approximate Entropy (ApEn) is a regularity statistic that quantifies the unpredictability of fluctuations in a time series and can classify complex systems. This study, ApEn is used to extract features from motor imag...
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For a class of uncertain multi-input-multi-output (MIMO) discrete-time nonlinear systems with strong coupling and unstable zero-dynamics, an adaptive generalized predictive decoupling switching control method based on...
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For a class of uncertain multi-input-multi-output (MIMO) discrete-time nonlinear systems with strong coupling and unstable zero-dynamics, an adaptive generalized predictive decoupling switching control method based on unmodeled dynamic compensation is proposed. It is only required that the higher order nonlinear terms of the system to satisfy a linear growth condition, rather than the global boundedness condition widely used. The analysis of stability and convergence of the adaptive control method are performed. Moreover, in designing the nonlinear generalized predictive decoupling controller, we combine the adaptive-network-based fuzzy inference system (ANFIS) training with the "one-toone mapping" technique to adaptively estimate the unmodeled dynamics, so that the universal approximation property of ANFIS can be guaranteed. Finally, simulation results demonstrate the superiority of the proposed method and validate the theoretical analysis.
In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems ...
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In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
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