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In a certain country the weather forecast takes place as follows. The percentage of sunshine per day is measured. At day \(k\) there has been \(a_{k} \%\) sunshine. The forecast for the day thereafter is made according to $$ \hat{a}_{k+1}=\left(6 a_{k}+3 a_{k-1}+a_{k-2}\right) / 10 $$ where \(\hat{a}_{k+1}\) is the forecast. Write the system in state space form for this forecast where the \% of sunshine of today is the input and where the forecast for tomorrow is the output. What is the dimension of the state?

Short Answer

Expert verified
The state space system has a dimension of 2.

Step by step solution

01

Define the State Variables

Introduce state variables based on the past sunshine percentages. Let's define: \[ x_1 = a_{k-1}, \quad x_2 = a_k \]This transforms the recursive relationship given into a first-order system state equation.
02

Formulate the State Equations

Express the state equations using the state variables. Use the recursive formula to express \(\hat{a}_{k+1}\) in terms of the state variables:\[ x(k+1)_1 = x_2 \]\[ x(k+1)_2 = a_{k+1} \]Then insert these into the formula:\[ a_{k+1} = \hat{a}_{k+1} = \frac{6x_2 + 3x_1 + a_{k-2}}{10} \]
03

Express the System in State Space Form

The input to the system is \(a_k\) and the output is \(\hat{a}_{k+1}\). Hence, we can express this in state space form:\[\begin{bmatrix} x_1(k+1) \ x_2(k+1) \end{bmatrix} = \begin{bmatrix} 0 & 1 \ 0.3 & 0.6 \end{bmatrix} \begin{bmatrix} x_1(k) \ x_2(k) \end{bmatrix} + \begin{bmatrix} 0 \ 0.1 \end{bmatrix} a_k\]The output equation is:\[\hat{a}_{k+1} = \begin{bmatrix} 0.3 & 0.6 \end{bmatrix} \begin{bmatrix} x_1(k) \ x_2(k) \end{bmatrix} + 0.1 a_k \]
04

Determine the Dimension of the State

The state vector is \([x_1, x_2]^T\), which means there are 2 state variables. Thus, the state dimension is 2.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Sunshine Forecasting
Forecasting sunshine is an interesting aspect of weather predictions. It involves predicting the amount of sunlight expected on a given day based on past data. The given problem uses a simple mathematical model to forecast sunshine by averaging past sunshine percentages. The formula provided in the exercise allows a refined prediction by incorporating sunshine data from three consecutive days: the current day, the previous day, and the day before that. This approach enables forecasters to take advantage of patterns in sunshine levels over time, which can help in predicting future weather conditions more accurately.
State Variables
State variables are key components in transforming complex systems into simpler models. In this exercise, the state variables are derived from the past sunshine percentages, specifically defined as:
  • The value of sunshine from two days ago termed as the first state variable, i.e., \(x_1 = a_{k-1}\).
  • Today's sunshine percentage becomes the second state variable, denoted as \(x_2 = a_k\).
These variables help to reconstruct the sequence of sunshine data in a way that makes forecasting more manageable. By using these state variables, we can form state equations that simplify computation and manipulation of data.
System Dynamics
System dynamics refers to the behavior of interconnected components over time. In the context of sunshine forecasting, it examines how daily sunshine percentages interact to produce forecasts. The transformation into state space form allows us to abstract the dynamics of sunshine changes more effectively. Starting with established state variables, we derive state equations that reflect the system's dynamic behavior: - The forecast calculations incorporate recursive elements, relying on past and present sunshine values to make predictions. Recognizing the dynamics helps identify how sensitive the system is to changes in one or more variables, thus providing better insights into daily sunshine forecasting mechanisms.
First-Order System
A first-order system is a type of mathematical model that relies on varying inputs from past states to predict future outputs. In this exercise, expressing sunshine forecasting as a first-order system involves utilizing the information from previous days to forecast the next day's sunshine percentage. The process starts by formulating the governing equations using state variables, focusing on current conditions influenced by prior states. The equations result in a straightforward structure where the next state is a linear combination of the current state and some external inputs. The simplicity of a first-order system lies in its linearity, making it efficient for real-time applications like weather forecasting. By relying on first-order dynamics, we minimize variables and focus on key past values that significantly impact the forecasted values. This approach helps streamline our predictive model and hone in on the most relevant data for accurate sunshine predictions.

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Most popular questions from this chapter

We are given the time discrete system $$ \begin{aligned} x(k+1) &=\left(\begin{array}{cc} 0 & 1 \\ -2 & -3 \end{array}\right) x(k)+\left(\begin{array}{l} 0 \\ 1 \end{array}\right) u(k) \\ y(k) &=\left(\begin{array}{cc} 2 & 1 \end{array}\right) x(k) \end{aligned} $$ Determine the transition matrix, impulse response function and the transfer function of this system. Suppose the following periodic input signal is applied to the system: $$ u(k)= \begin{cases}0 & k<0 \\ (-1)^{k}, & k \geq 0\end{cases} $$ What is the output response (take \(x(0)\) as the zero state)? Why is the output signal not periodic? The time-discrete, time-invariant, linear system characterized by matrices \((A, B, C, D)\) is called controllable if for each \(x_{0}, x_{1} \in \mathcal{R}^{n}\) a time \(k>0\) and a sequence \(u(0), u(1), \ldots\) exist such that \(x\left(k, x_{0}, u\right)=x_{1} .\) The meaning of \(x\left(k, x_{0}, u\right)\) will be clear; the state at time instant \(k\), starting with initial condition \(x(0)=x_{0}\) and having applied an input sequence \(u\). The system is observable if a \(k>0\) exists such that for any sequence of controls \(u\) we have: $$ y\left(j, x_{0}, u\right)=y\left(j, x_{1}, u\right), \quad j=0,1, \ldots, k, \text { implies } x_{0}=x_{1} $$ The conditions in terms of matrices \(A, B, C\) and \(D\) for controllability and observability are the same as in the time-continuous case. This will be shown in the next theorem for controllability. Sometimes one distinguishes null controllability \(\left(x_{1}=0\right)\) and reachability \(\left(x_{0}=0\right) .\) It can be shown that "standard" controllability, i.e. with arbitrary \(x_{0}\) and \(x_{1}\), is as strong as reachability (see also the proof of next theorem); the condition is: $$ \operatorname{rank}\left[B A B \ldots A^{n-1} B\right]=n $$ wheras null controllability is not as strong as "standard" controllability.

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