In this paper, leader-follower consensus problems of a kind of discrete-time heterogeneous multi-agent systems(MASs) with independent topologies are studied by using iterative learning control(ILC) in a repeatable con...
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ISBN:
(纸本)9781728159225
In this paper, leader-follower consensus problems of a kind of discrete-time heterogeneous multi-agent systems(MASs) with independent topologies are studied by using iterative learning control(ILC) in a repeatable control environment. The heterogeneous multi-agent systems are composed of second-order and first-order dynamic systems, and independent topology refers to the topological structure of velocity and position is different. An iterative learning controlalgorithm is proposed to solve the exact consensus of discrete-time heterogeneous multi-agent systems with independent topology. A necessary and sufficient condition of the consensus is also given for the MASs. Finally, the simulation example proves the effectiveness of the iterative learning controlalgorithm.
The mechanical pulping process is non-linear and multivariable. To solve the related control problem, the dynamic model of the pulping process undergoes first approximate linearization around a temporary operating poi...
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The mechanical pulping process is non-linear and multivariable. To solve the related control problem, the dynamic model of the pulping process undergoes first approximate linearization around a temporary operating point which is updated at each iteration of the controlalgorithm. The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the pulping process. For the approximately linearized description of the pulping process, a stabilizing H-infinity feedback controller is designed. To compute the controller's feedback gains, an algebraic Riccati equation is solved at each time-step of the control method. The stability properties of the control scheme are proven through Lyapunov analysis.
Electrostrictive actuators are a class of smart transducers with a great potential for many submicron motion applications. A major challenge for the electrostrictive actuators exists in the control of such ultra-preci...
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Electrostrictive actuators are a class of smart transducers with a great potential for many submicron motion applications. A major challenge for the electrostrictive actuators exists in the control of such ultra-precision motions, which are often seriously influenced by the intrinsic behaviors of electrostrictive material like non-linearity, hysteresis and creep. Based on Newton's method, this paper presents a new iterative control algorithm to improve the positioning and tracking performances of a linear multilayer electrostrictive actuator. In this algorithm, the iterative gain is not fixed but variable according to the previous output feedback and the nominal input/output relationship of the electrostrictive actuator. The convergence of this algorithm is theoretically proved quadratic and experimentally verified correct. A comparison of effectiveness of the new algorithm with that of the conventional proportional integral (PI) control and gain-fixed iterative control algorithms is made. The results show that using this new iterative control algorithm both the stability and the speed of submicron motion control have been obviously improved for the tested electrostrictive actuator. (c) 2005 Elsevier Inc. All rights reserved.
To clarify the pathophysiological role of dynamic arterial properties in cardiovascular diseases, we attempted to develop a new control system that imposes desired aortic impedance on in situ rat left ventricle. In 38...
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To clarify the pathophysiological role of dynamic arterial properties in cardiovascular diseases, we attempted to develop a new control system that imposes desired aortic impedance on in situ rat left ventricle. In 38 anesthetized open-chest rats, ascending aortic pressure and flow waveforms were continuously sampled (1,000 Hz). Desired flow waveforms were calculated from measured aortic pressure waveforms and target impedance. To minimize the difference between measured and desired aortic flow waveforms, the computer generated commands to the servo-pump, connected to a side branch of the aorta. By iterating the process, we could successfully control aortic impedance in such a way as to manipulate compliance and characteristic impedance between 60 and 160% of their respective native values. The error between desired and measured aortic flow waveforms was 70 +/- 34 mu l/s (root mean square;4.4 +/- 1.4% of peak flow), indicating reasonable accuracy in controlling aortic impedance. This system enables us to examine the importance of dynamic arterial properties independently of other hemodynamic and neurohumoral factors in physiological and clinical settings.
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