Complete the code to define the state update equation in discrete state-space form.
x_next = A * x + [1] * uC instead of the input matrix.A again instead of the input matrix.The input matrix is represented by B in the discrete state-space equation.
Complete the code to define the output equation in discrete state-space form.
y = [1] * x + D * uB instead of the output matrix.A instead of the output matrix.The output matrix is represented by C in the discrete state-space output equation.
Fix the error in the discrete state-space update equation code.
x_next = A * x [1] B * uThe correct discrete state update equation adds the input effect: x_next = A * x + B * u.
Fill both blanks to complete the dictionary comprehension that creates a state dictionary with state names and their initial values.
state_dict = {name: [1] for name, [2] in zip(state_names, initial_values)}The dictionary comprehension maps each state name to its initial value using the variable value.
Fill all three blanks to complete the code that filters states with values greater than zero and creates a new dictionary with their names and values squared.
filtered_states = { [1]: [2]**2 for [1], [2] in states.items() if [2] > 0 }The dictionary comprehension uses name and value to filter and square positive state values.
