a1 xl ðtÞþa2 xg ðtÞ
Remark 5。 p_ i ðtÞ describes the velocity of the variable piðtÞ ¼ a1 þa2 。 Moreover, the decision process is a discrete-time
decision process, which results in that piðtÞ is piece-wise continuous。 Consequently, the direction of p_ iðtÞ can be
approximately calculated from piðk — DkÞ to piðkÞ (k — Dk < t 6 k) and the magnitude of that is decided by the maximum linear velocity of the robot。 Similarly, p€iðtÞ can also be approximately calculated based on p_ iðtÞ and the previous velocity information。
3。4。Convergence analysis
In this subsection, we will prove the finite-time convergence of the continuous-time model of the FPSO algorithm。 The following Lyapunov analysis provides a global convergence result。
Theorem 1。 Consider the continuous-time model of the FPSO algorithm (16) with ðx; a; a; c; bÞ 2 Xc in (18) and a is a constant in
。 1—a 。
the deterministic case。 The continuous-time FPSO algorithm converges over a finite-time interval 0; ð1þaÞVð0Þ1þa
kð1—aÞ
where Vð0Þ
1—a
and k
1þaÞVð0Þ1þa
can be calculated according to (20) and (25), respectively, i。e。 xiðtÞ ! piðtÞ and viðtÞ ! p_ iðtÞ when t ! ð
kð1—aÞ 。
Proof。 Introduce ni ðtÞ ¼ xi ðtÞ— pi ðtÞ into (16), and set y1ðtÞ ¼ ni ðtÞ and y2ðtÞ ¼ n_ i ðtÞ。 As a result, the system (14) can be obtained。 Considering the deterministic case, we write the system (14) as
Fig。 4。 The curve of the average oscillation magnitude for the parameter c for the system state y2 ðtÞ in (14) (x ¼ 0:8; a ¼ 6; a ¼ 0:5; b ¼ 0:1; y1 ð0Þ ¼ 5, and y2 ð0Þ ¼ —9)。
118 Q。 Lu et al。 / Information Sciences 277 (2014) 111–140
Fig。 5。 The curve of the convergence time for the parameter b for the system state y2 ðtÞ in (14) (x ¼ 0:8; a ¼ 6; a ¼ 0:5; c ¼ 1; y1 ð0Þ ¼ 5, and y2 ð0Þ ¼ —9)。
。 y_ 1 ðtÞ¼ y2 ðtÞ
y_ 2 ðtÞ¼ —c/m — bsigð/mÞ
ð19Þ
for every a 2 ð0; 1Þ, where /m ¼ ð1 — xÞy2ðtÞþ ay1 ðtÞ。
If the origin of the system (19) is a finite-time-stable equilibrium, xiðtÞ and vi ðtÞ will reach piðtÞ and p_ iðtÞ in finite time, respectively。 Choose a Lyapunov function candidate as