Refined model for studies of the nonlinear dynamics of self-propelled submersibles with allowance for the effect of the cable

Makarenko, A. I.; Poddubnyi, V. I.; Kholopova, V. V.

doi:10.1007/bf02700673

Cited by 1 publication

(8 citation statements)

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“…Since every subsystem is described by system (1), in what follows, the superscript [ ] is dropped for simplification reason except when needed for clarification.…”

Section: Distributed Wsns Modelmentioning

confidence: 99%

“…The dynamic equation is given by [32]: where is the mass of the bob, is the length of the rod, is the angle subtended by the rod and the vertical axis through the pivot point, is the acceleration due to gravity, is the coefficient of friction, and is the applied torque. Letting 1 = , 2 =, and = , model ( 73) is represented in the state-space form of [1] = [2]…”

Section: Simulation Examplesmentioning

confidence: 99%

“…where A [1,2] and A [2,1] represent the pendulums receiving information from each other over WSNs. The parameter values are adopted as follows:…”

Section: Simulation Examplesmentioning

confidence: 99%

“…Taking the sampling time = 0.1sec, system (74) is discretized into the discrete-time system in form as (1). The performance index matrices of (4) are chosen taking [1] = [2] = diag {1} , [1] = [2] = 1.…”

Section: Simulation Examplesmentioning

confidence: 99%

“…Command no. 1 pendulum to keep its angle tracking a step signal target as [1] = 1 and no. 2 pendulum tracking a sinusoidal signal target as [2] = sin .…”

Section: Simulation Examplesmentioning

confidence: 99%

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Optimal Distributed Tracking Control for Nonlinear Cooperative Wireless Sensor Networks

Lei

Wang

2013

International Journal of Distributed Sensor Networks

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The optimal distributed tracking control algorithms over nonlinear cooperative wireless sensor networks (WSNs) are presented in this paper. In order to solve transfer delay and packet loss problem, the architecture of wireless active sensor (WAS) is employed, where a state estimator is embedded, which can provide the needed state information. Furthermore, the optimal distributed tracking control algorithm is proposed. By solving the matrix equations and the adjoint difference equations, the optimal control law can be obtained easily, in which an increment integral regulator is designed to implement tracking target without steady-state error and a nonlinearity compensation term is designed to compensate for the effect produced by system nonlinearity. Moreover, the observer-based dynamical algorithm is given considering the physically unrealizable disturbance states and the unavailable sensor states. Finally, computer simulations are carried out with application to two nonlinear pendulums, which prove that the algorithm is effective and easy to implement, and the system achieves the desired performance based on tradeoff between tracking error and control energy consumption.

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“…Since every subsystem is described by system (1), in what follows, the superscript [ ] is dropped for simplification reason except when needed for clarification.…”

Section: Distributed Wsns Modelmentioning

confidence: 99%

Section: Simulation Examplesmentioning

confidence: 99%

“…where A [1,2] and A [2,1] represent the pendulums receiving information from each other over WSNs. The parameter values are adopted as follows:…”

Section: Simulation Examplesmentioning

confidence: 99%

Section: Simulation Examplesmentioning

confidence: 99%

“…Command no. 1 pendulum to keep its angle tracking a step signal target as [1] = 1 and no. 2 pendulum tracking a sinusoidal signal target as [2] = sin .…”

Section: Simulation Examplesmentioning

confidence: 99%

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