1999
DOI: 10.1109/41.793340
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Nonlinear predictive control with application to manipulator with flexible forearm

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Cited by 327 publications
(9 citation statements)
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“…(2.50.π) = 3141 rad/s with ξ = 0.707. Therefore, based on (14), K P = 44.31 and K i = 98658. On the other hand, K P .s+K i in the nominator of the system characteristic equation will increase the bandwidth.…”
Section: Simulation Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…(2.50.π) = 3141 rad/s with ξ = 0.707. Therefore, based on (14), K P = 44.31 and K i = 98658. On the other hand, K P .s+K i in the nominator of the system characteristic equation will increase the bandwidth.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…In this circumstance, the control system will be stable. Considering P and A as follow, Q can be obtained as (14):…”
Section: B Application Of the Lyapunov Function In Power Systemsmentioning
confidence: 99%
“…By linearizing the dynamics equations, an adaptive augmented state feedback control method is developed in [7]. A nonlinear predictive controller built upon a neural network system model is presented in [8]. In order to obtain minimum-phase system model, a redefined output approach and an inverse dynamics strategy are adopted in [9] to design a feedback controller.…”
Section: Introductionmentioning
confidence: 99%
“…Although these methods greatly reduce the computational burden, the effect of the linearization could generate poor results when applied to the real nonlinear system. Other methods utilize iterative optimization procedures, as Gradient based algorithms or Sequential Quadratic Programming techniques ( Song & Koivo 1999). These methods, although can fully take into account the nonlinear dynamics of the system, suffer from the well known problem related to local minima.…”
Section: Improved Evolutionary Predictive Controllers For Real-time Amentioning
confidence: 99%
“…A possible strategy, as proposed in (Mutha et Al., 1997; consists of the linearization of the dynamics at each time step and of the use of standard linear predictive control tools to derive the control policies. Other methods utilize iterative optimization procedures, as gradient based algorithms (Song & Koivo, 1999) or discrete search techniques as Dynamic Programming (Luus, 1990) and Branch and Bound (Roubos et Al., 1999) methods. The main advantages of a search algorithm is that it can be applied to general nonlinear dynamics and that the structure of the objective function is not restricted to be quadratic as in most of the other approaches.…”
Section: Part Of the Following Article Has Been Previously Published mentioning
confidence: 99%