Teorema adalah pernyataan matematis yang telah dibuktikan secara logis sesuai dengan kaidah matematika dengan menggunakan asumsi - asumsi matematis yang telah diketahui
Lemma adalah suatu pernyataan matematis di dalam pembuktian suatu teorema. Dengan kata lain, lemma adalah "teorema kecil" yang digunakan untuk membuktikan suatu teorema. Lemma adalah alat bantu untuk membuktikan suatu teorema
Tampilkan postingan dengan label Control and System Engineering. Tampilkan semua postingan
Tampilkan postingan dengan label Control and System Engineering. Tampilkan semua postingan
Sabtu, 19 Januari 2013
Rabu, 31 Oktober 2012
Pseudo Inverse Matrix
Matriks yang tidak persegi membutuhkan suatu metode khusus untuk menginverskan, yang biasa disebut pseudo inverse matriks.
Properti 1
Suppose that A is m
n real matrix
If m<n, then the inverse of ATA does not exist
If m
n and if the inverse of ATA exists
A+=(ATA)-1AT satisfies the definition of pseudoinverse
Here, A+A=I holds. I is identity matrix.
A: m
n, AT:
n
m, A+:
n
m, ATA:
n
n, I: n
n
The rank of A and A+ is n
Properti 2
Suppose that A is m
n real matrix
If m>n, then the inverse of AAT does not exist
If m
n
and if the inverse of AAT exists
A+=AT(AAT)-1 satisfies the definition of pseudoinverse
Here, AA+=I holds. I is identity matrix.
A: m
n, AT:
n
m, A+:
n
m, AAT:
m
m, I: m
m
The rank of A and A+ is m
Pseudoinverse dengan Teknik Singular Decomposition Value (SVD)
Suppose A is m
n
matrix. If m<n, attach the row of 0 and make the size m=n, a
priori. Here, m
n.
A=UWVT is supposed to be the result of SVD
Assume that the left-upper part of W has larger number, and the right-lower part of W has smaller number
If the component of W is less than a threshold, set it to be 0, and define such matrix as W'
When W'=diag(w1,w2,...,wk,0,0,...,0), we define
W''=diag(1/w1,1/w2,...,1/wk,0,0,...,0)
Define A+=VW''UT
A+A is n
n matrix. Left-upper k
k is identity matrix. Otherwise 0.
AA+ is m
m matrix
If the rank of A is n, A+ satisfies the above property 1
Even if the rank of A is less than n, A+ satisfies the definition of pseudoinverse
Suppose that we want to solve Ax=b. Calculate x=VW''UTb
If the rank of A is n, then x is the value where the error is minimum
If the rank of A is less than n, then x is the solution where the norm ||x|| is minimum
Definition of Moore - Penrose generalized matrix inverse
Given m
n real
matrix A, n
m
matrix pseudoinverse A+ is defined as follows
AA+A=A
A+AA+=A+
(AA+)T=AA+
(A+A)T=A+A
Reference : Diktat Mata Kuliah Teknik Numerik Sistem Linier, ITS Surabaya
Properti 1
Suppose that A is m

If m<n, then the inverse of ATA does not exist
If m

A+=(ATA)-1AT satisfies the definition of pseudoinverse
Here, A+A=I holds. I is identity matrix.
A: m





The rank of A and A+ is n
Properti 2
Suppose that A is m

If m>n, then the inverse of AAT does not exist
If m

A+=AT(AAT)-1 satisfies the definition of pseudoinverse
Here, AA+=I holds. I is identity matrix.
A: m





The rank of A and A+ is m
Pseudoinverse dengan Teknik Singular Decomposition Value (SVD)
Suppose A is m


A=UWVT is supposed to be the result of SVD
Assume that the left-upper part of W has larger number, and the right-lower part of W has smaller number
If the component of W is less than a threshold, set it to be 0, and define such matrix as W'
When W'=diag(w1,w2,...,wk,0,0,...,0), we define
W''=diag(1/w1,1/w2,...,1/wk,0,0,...,0)
Define A+=VW''UT
A+A is n


AA+ is m

If the rank of A is n, A+ satisfies the above property 1
Even if the rank of A is less than n, A+ satisfies the definition of pseudoinverse
Suppose that we want to solve Ax=b. Calculate x=VW''UTb
If the rank of A is n, then x is the value where the error is minimum
If the rank of A is less than n, then x is the solution where the norm ||x|| is minimum
Definition of Moore - Penrose generalized matrix inverse
Given m


AA+A=A
A+AA+=A+
(AA+)T=AA+
(A+A)T=A+A
Reference : Diktat Mata Kuliah Teknik Numerik Sistem Linier, ITS Surabaya
Senin, 13 Agustus 2012
Model Predictive Control (MPC) - Preface
Model Predictive Control (MPC), variously known as rolling-horizon
control, receding-horizon control, etc, is a powerful technique employed
in diverse engineering applications, and valued for its inherent
ability to handle constraints while minimizing some cost (or maximizing
some reward). The underlying idea of MPC is to approximate an infinite
horizon constrained optimization problem by a finite horizon one, and
then an optimal control law is calculated at every time step and applied
in a rolling-horizon fashion. In most applications uncertainty in the
model is involved, either due to some external disturbances or due to
imprecise modeling of the controlled process. Uncertainty is commonly
addressed in literature by formulating a robust MPC problem, assuming
that the uncertainty is bounded and adopting a worst-case approach.
Although there has been some major advances in this field, this approach
is often too pessimistic.
But what happens if the uncertainty is not bounded? Or when it is not
uniformly distributed? A less conservative approach would clearly be to
take these possibilities into account, identifying appropriate
distributions of the uncertainties and formulating a stochastic MPC
problem instead. In this context, there are several points one should
take into consideration for the problem formulation:
- Probabilistic Constraints: In the context of unbounded stochastic disturbances, satisfying all constraints with probability one is impossible, as at any time the system might encounter a very big disturbance that forces it to violate its constraints. It is important thus, to substitute the hard constraints with soft, probabilistic ones, ensuring that they are respected with a desired probability
- Expected Value Constraints:
Another alternative for dealing with constraints is to make sure that they are respected on an average for the optimization problem considered. Depending on the nature of the problem such expected value constraints may be more meaningful than the probabilistic ones.
- Cost (Reward):
The simplest cost (reward) is perhaps an expected cost (reward), formulated as the expected value of the sum of discounted cost-per-stage functions. Depending on the application, alternative and more difficult formulations may be to consider long-run expected average cost (reward), or long-run pathwise cost (reward).
Sabtu, 14 April 2012
Inverter 3 Fasa
Inverter merupakan suatu peralatan yang dapat digunakan untuk mengkonversikan sumber daya 3 phasa menjadi tegangan DC yang kemudian dikonversikan lagi menjadi sumber daya 3 phasa dengan frekuensi yang sesuai. Cara ini bisa dipakai karena diketahui bahwa kecepatan sinkron motor induksi berbanding lurus dengan frekuensi sumber dayanya.
Salah satu cara yang efektif untuk menghasilkan tegangan dengan frekuensi yang bisa diatur yaitu dengan jalan membangkitkannya sendiri. Untuk itu diperlukan suatu sumber daya DC. Sumber daya ini diperoleh dari sumber daya PLN yang disearahkan dengan penyearah. Selanjutnya sumber daya ini ditapis dengan filter DC untuk mendapatkan sumber daya DC yang lebih rata. Kemudian dengan melalui suatu rangkaian switch (disebut sebagai jembatan inverter) yang bisa dikendalikan sedemikian rupa, sumber daya itu bisa diubah menjadi sumber daya 3 phasa pada ujung beban. Dengan cara mengontrol waktu pensaklaran dari switch-switch tersebut dengan menggunakan sinyal PWM (Pulse Width Modulation). Prinsip kerja inverter ditunjukkan oleh Gambar 1.
Gambar 1. Prinsip Kerja Inverter
Pada inverter 3 phasa, dikenal yang namanya enam langkah (six-steps) pembangkitan tegangan 3-phasa. Disebut demikian karena akan dijumpai enam langkah (tingkatan) pada tiap tegangan phasenya yang didapat dari enam macam kombinasi on-off dari keenam saklar inverter yang dilakukan secara berulang pada tiap periodenya.
Gambar 2. Enam Langkah Pembangkitan Tegangan Tiga Fasa pada Inverter
Gambar 3. Inverter Tiga Fasa
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