2008 34th Annual Conference of IEEE Industrial Electronics 2008
DOI: 10.1109/iecon.2008.4757970
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Optimal trajectory generation for camless internal combustion engine valve control

Abstract: Camless internal combustion engines offer improvements over traditional engines in terms of torque performance, reduction of emissions, reduction of pumping losses and fuel economy. Theoretically, electromagnetic valve actuators offer the highest potentials for improving efficiency due to their control flexibility. For real applications, however, the valve actuators developed so far suffer from high power consumption and other control problems. One key point for the control is the design of the reference traje… Show more

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Cited by 5 publications
(2 citation statements)
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“…to to (25) It can be expressed as l tt oL oL ltJ (26) l tt oL ltt oL ltt (27) Because of: (28) For variations bq(to) = bq(t f ) = 0, thus…”
Section: C(q(t) G(t) F(t))dtmentioning
confidence: 99%
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“…to to (25) It can be expressed as l tt oL oL ltJ (26) l tt oL ltt oL ltt (27) Because of: (28) For variations bq(to) = bq(t f ) = 0, thus…”
Section: C(q(t) G(t) F(t))dtmentioning
confidence: 99%
“…To solve optimal control problems numerically, the paper [24] proposed that control states can be approximated by values at a finite number of time points, the control history can be parametrized by piecewise polynomials, and further this problem can be solved by a standard Non- Optimal trajectory generation problem is one kind of optimal control problems. Its applications vary from the control of various devices such as control of linear system [25], and engine valves [26], to motion planning of robots [27] [28], manipulator robots [29] [3~], humanoid robot [31], and trajectory tracking for boom cranes [32]. Optimal trajectory generation for hypersonic vehicles as a research topic was raised in [33], Philip D. Hattis and Richard K. Smolskis proposed a calculus of variations direct method of steepest descent to determine the trajectory for hypersonic vehicles.…”
Section: Tf Minimize J = £(Q(t) Q(t) T)dt Subject To Q(t O ) = a mentioning
confidence: 99%