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Matrix, Control and Vision (1) | Rome wasn't built in a day

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Matrix, Control and Vision (1) | Rome wasn't built in a day | shiyuzhao1.wordpress.com Reviews
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Matrix, Control and Vision (1) | Rome wasn't built in a day | shiyuzhao1.wordpress.com Reviews

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Rome wasn't built in a day

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Matrix norm for block partitioned matrices | Matrix, Control and Vision (1)

https://shiyuzhao1.wordpress.com/2015/06/21/matrix-norm-for-block-partitioned-matrices

Matrix, Control and Vision (1). Rome wasn't built in a day. Matrix norm for block partitioned matrices. June 21, 2015. Here is a memo:. Block partitioned matrix norm. From → Uncategorized. Larr; Some Latex Errors. Matlab Code for Computing Bearing Rigidity Matrix and Distance Rigidity Matrix →. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). You are commenting using your WordPress.com account. ( Log Out.

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The simplest example to demonstrate PI control | Matrix, Control and Vision (1)

https://shiyuzhao1.wordpress.com/2015/03/29/the-simplest-example-to-demonstrate-pi-control

Matrix, Control and Vision (1). Rome wasn't built in a day. The simplest example to demonstrate PI control. March 29, 2015. If A is Hurwitz, then e converges to zero. Where b is a nonzero unknown disturbance. Then, e converges to. Which is not zero. In order to make e go to zero in the presence of the disturbance b, introduce the integral term. And the system becomes. Then, it is easy to see that the augmented system is. Converges to zero, and. As a result,. Finally cancels the disturbance of b. The syst...

3

Matlab Code for Computing Bearing Rigidity Matrix and Distance Rigidity Matrix | Matrix, Control and Vision (1)

https://shiyuzhao1.wordpress.com/2015/06/28/matlab-code-for-computing-bearing-rigidity-matrix-and-distance-rigidity-matrix

Matrix, Control and Vision (1). Rome wasn't built in a day. Matlab Code for Computing Bearing Rigidity Matrix and Distance Rigidity Matrix. June 28, 2015. The following matlab code is used to calculate the bearing rigidity matrix and the distance rigidity matrix of a network. It can be run alone and it contains an example. I have verified it. From → Uncategorized. Larr; Matrix norm for block partitioned matrices. Null space of characteristic matrix equation →. Leave a Reply Cancel reply.

4

Some Latex Errors | Matrix, Control and Vision (1)

https://shiyuzhao1.wordpress.com/2015/06/10/some-latex-errors

Matrix, Control and Vision (1). Rome wasn't built in a day. June 10, 2015. When I compile the source tex code, the PDF viewer does not pop up automatically. I checked carefully and found that it is caused by the command. Include{figures/ fig tikz Example AugmentedNetwork}. If I comment this line, problem solved and the PDF viewer can pop up. This problem is very strange because all the code (tex and tikz) have been used elsewhere. Oct 2016) The above problem happened again when I wrote the CSM! Second, t...

5

A simple but useful fact about eigenvalues of matrix products | Matrix, Control and Vision (1)

https://shiyuzhao1.wordpress.com/2015/03/21/a-simple-but-useful-fact-about-eigenvalues-of-matrix-products

Matrix, Control and Vision (1). Rome wasn't built in a day. A simple but useful fact about eigenvalues of matrix products. March 21, 2015. Edit: Sept 8, 2015. I have checked the results below and they seem correct. However, just be careful when the matrix is positive semi-definite but not definite. Example: A=[1 0; 0 0] is PSD, and B=[0 0; 0 1] is also PSD. But AB=BA=0! Basic result: Suppose A and B are two square matrices. Then AB and BA has the same positive/negative/zero eigenvalues. It is obvious that.

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Matrix, Control and Vision | Rome wasn't built in a day | Page 2

https://shiyuzhao.wordpress.com/page/2

Matrix, Control and Vision. Rome wasn't built in a day. Discretize a continuous LTI system: zero-order holding. The continuous LTI system is. The corresponding discrete system is. What are F and G? Usually we have two methods: use zero-order hold method to discretize it; or use approximation to a rigorous discrete system. Today I will show they are the same to the first order! 1) Zero-order holding method:. 2) Rigorous discrete system. It is easy to see. From → Control. Error model of 6-DOF. Minimum vari...

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Observability: staircase | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2012/01/12/observability-staircase

Matrix, Control and Vision. Rome wasn't built in a day. From → Control. Larr; Linearizing first or discretizing first? Memo: the definition of the Euler angles →. Http:/ shiyuzhao1.wordpress.com. Blog at WordPress.com.

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Controllability, Linear system Ax=b, and Least-squares problem | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2015/08/06/controllability-linear-system-axb-and-least-squares-problem

Matrix, Control and Vision. Rome wasn't built in a day. Controllability, Linear system Ax=b, and Least-squares problem. Key words: controllability, reachability, linear system Ax=b, and least-squares problem. First, we analyze the existance and uniqueness of the solutions to Ax=b. Memo – solutions to Ax=b. Second, we show the controllability and reachability can be interpreted as Ax=b. Memo -localizability as Ax=b. From → Matrix. Larr; Memo: the definition of the Euler angles. Blog at WordPress.com.

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Memo on ellipse and ellipsoid | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2011/11/13/memo-on-ellipse-and-ellipsoid

Matrix, Control and Vision. Rome wasn't built in a day. Memo on ellipse and ellipsoid. For the eigenvector x1 that associated with the smallest eigenvalue of A. For the eigenvector yn that associated with the largest eigenvalue of B. It is not correct that. Counterexamples can be easily found if you consider the geometric interpretation of the ellipsoids x T*A*x=1 and x T*B*x=1. Then it defines an ellipsoid. Then the vector x is inside the ellipsoid. Then x is outside the ellipsoid. Is contained inside of.

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Relationship between the covariance of continuous and discrete noise processes. | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2011/11/08/relationship-between-the-covariance-of-continuous-and-discrete-noise-processes

Matrix, Control and Vision. Rome wasn't built in a day. Relationship between the covariance of continuous and discrete noise processes. Relationship between the covariance of continuous and discrete noise processes. Covariance of continuous and discrete systems. But you should know what you have now is for the continuous or the discrete system. From → Kalman Filter. Larr; Discretize a continuous LTI system: zero-order holding. Memo on ellipse and ellipsoid →. Leave a Reply Cancel reply.

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Non-holonomic | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2011/11/20/non-holonomic

Matrix, Control and Vision. Rome wasn't built in a day. In robotics, holonomicity refers to the relationship between the controllable. And total degrees of freedom. Of a given robot (or part thereof). If the controllable degrees of freedom is equal. To the total degrees of freedom then the robot is said to be holonomic. If the controllable degrees of freedom are less. Than the total degrees of freedom it is non-holonomic. A robot is considered to be redundant if it has more. The resulting phenomenon is.

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Memo: the definition of the Euler angles | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2012/01/16/memo-the-definition-of-the-euler-angles

Matrix, Control and Vision. Rome wasn't built in a day. Memo: the definition of the Euler angles. We often encounter rotating one vector. What if we rotate a reference frame to coincide with another reference frame? What will we get? Memo rotate one frame to the other. From → Control. Larr; Observability: staircase. Controllability, Linear system Ax=b, and Least-squares problem →. Http:/ shiyuzhao1.wordpress.com. Create a free website or blog at WordPress.com.

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Linearizing first or discretizing first? | Matrix, Control and Vision

https://shiyuzhao.wordpress.com/2012/01/09/linearizing-first-or-discretizing-first

Matrix, Control and Vision. Rome wasn't built in a day. Linearizing first or discretizing first? Given a nonlinear model:. In practice, we need to both discretize and linearize the nonlinear model. The question is which procedure should be first? 1) Method 1: Linearizing first, then discretizing. 2) Method 2: Discretizing first, then linearizing. Conclusions: the two methods are equivalent. From → Control. Observability: staircase →. Http:/ shiyuzhao1.wordpress.com. Blog at WordPress.com.

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Matrix, Control and Vision | Rome wasn't built in a day

Matrix, Control and Vision. Rome wasn't built in a day. Controllability, Linear system Ax=b, and Least-squares problem. Key words: controllability, reachability, linear system Ax=b, and least-squares problem. First, we analyze the existance and uniqueness of the solutions to Ax=b. Memo – solutions to Ax=b. Second, we show the controllability and reachability can be interpreted as Ax=b. Memo -localizability as Ax=b. Comments Off on Controllability, Linear system Ax=b, and Least-squares problem. To the tot...

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Matrix, Control and Vision (1) | Rome wasn't built in a day

Matrix, Control and Vision (1). Rome wasn't built in a day. Matlab Code for Computing Bearing Rigidity Matrix and Distance Rigidity Matrix. June 28, 2015. The following matlab code is used to calculate the bearing rigidity matrix and the distance rigidity matrix of a network. It can be run alone and it contains an example. I have verified it. From → Uncategorized. Matrix norm for block partitioned matrices. June 21, 2015. Here is a memo:. Block partitioned matrix norm. From → Uncategorized. June 10, 2015.

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