@@ -66,7 +66,7 @@ values in FBS2e.
6666
6767We begin by defining the dynamics of the system
6868
69- ..code-block ::
69+ ..code-block ::python
7070
7171import control
7272import numpyas np
@@ -96,7 +96,7 @@ We begin by defining the dynamics of the system
9696
9797
9898
99- ..code-block ::
99+ ..code-block ::python
100100
101101 io_predprey= control.NonlinearIOSystem(
102102 predprey_rhs,None ,inputs = (' u' outputs = (' H' ' L'
@@ -108,7 +108,7 @@ will be used as the output of the system.
108108The `io_predprey ` system can now be simulated to obtain the open loop dynamics
109109of the system:
110110
111- ..code-block ::
111+ ..code-block ::python
112112
113113 X0= [25 ,20 ]# Initial H, L
114114 T= np.linspace(0 ,70 ,500 )# Simulation 70 years of time
@@ -127,7 +127,7 @@ We can also create a feedback controller to stabilize a desired population of
127127the system. We begin by finding the (unstable) equilibrium point for the
128128system and computing the linearization about that point.
129129
130- ..code-block ::
130+ ..code-block ::python
131131
132132 eqpt= control.find_eqpt(io_predprey, X0,0 )
133133 xeq= eqpt[0 ]# choose the nonzero equilibrium point
@@ -137,7 +137,7 @@ We next compute a controller that stabilizes the equilibrium point using
137137eigenvalue placement and computing the feedforward gain using the number of
138138lynxes as the desired output (following FBS2e, Example 7.5):
139139
140- ..code-block ::
140+ ..code-block ::python
141141
142142 K= control.place(lin_predprey.A, lin_predprey.B, [- 0.1 ,- 0.2 ])
143143 A, B= lin_predprey.A, lin_predprey.B
@@ -149,7 +149,7 @@ applies a corrective input based on deviations from the equilibrium point.
149149This system has no dynamics, since it is a static (affine) map, and can
150150constructed using the `~control.ios.NonlinearIOSystem ` class:
151151
152- ..code-block ::
152+ ..code-block ::python
153153
154154 io_controller= control.NonlinearIOSystem(
155155None ,
@@ -162,7 +162,7 @@ populations followed by the desired lynx population.
162162To connect the controller to the predatory-prey model, we create an
163163`InterconnectedSystem `:
164164
165- ..code-block ::
165+ ..code-block ::python
166166
167167 io_closed= control.InterconnectedSystem(
168168 (io_predprey, io_controller),# systems
@@ -177,7 +177,7 @@ To connect the controller to the predatory-prey model, we create an
177177
178178
179179
180- ..code-block ::
180+ ..code-block ::python
181181
182182# Simulate the system
183183 t, y= control.input_output_response(io_closed, T,30 , [15 ,20 ])