Additional Exercises
The following exercises cover some of the topics introduced in this chapter. Exercises marked with a * appear in the printed text.
- Exercise: Exploring the dynamics of a rolling mill
- Exercise: Popular articles about control
Frequently Asked Questions
- Question: Can a control system include a human operator as a component?
- Question: How are stability, performance and robustness different?
- Question: How can I go from a continuous time linear ODE to a discrete time representation?
- Question: How can we tell from the phase plots if the system is oscillating?
- Question: How do you know when your model is sufficiently complex?
- Question: In the predator prey example, where is the fox birth rate term?
- Question: What is "closed form"?
- Question: What is a state? How does one determine what is a state and what is not?
- Question: What is a stochastic system?
- Question: What is the advantage of having a model?
- Question: What is the definition of a system?
- Question: Why does the effective service rate f(x) go to zero when x = 0 in the example on queuing systems?
- Question: Why isn't there a term for the rabbit death rate besides being killed by the foxes?
Errata
|
Python Code
The following Python scripts are available for producing figures that appear in this chapter.
Figure 1.11: A feedback system for controlling the velocity of a vehicle, Figure 1.18: Air–fuel controller based on selectors, Figure 2.11: Response to a step change in the reference signal for a system with a PI controller having two degrees of freedom, Figure 2.12: Responses of a static nonlinear system, Figure 2.14: Responses of the systems with integral feedback, Figure 2.19: System with positive feedback and saturation, Figure 2.8: Step responses for a first-order, closed loop system with proportional and PI control, Figure 2.9: Responses to a unit step change in the reference signal for different values of the design parameters, Figure 3.11: Simulation of the forced spring–mass system with different simulation time constants, Figure 3.12: Frequency response computed by measuring the response of individual sinusoids, Figure 3.22: Queuing dynamics, Figure 3.24: Consensus protocols for sensor networks, Figure 3.26: The repressilator genetic regulatory network, Figure 3.28: Response of a neuron to a current input, Figure 3.2: Illustration of a state model, Figure 3.4: Input/output response of a linear system, Figure 3.8: Discrete-time simulation of the predator–prey model, Figure 4.12: Internet congestion control, Figure 4.13: Internet congestion control for N identical sources across a single link, Figure 4.20: Simulation of the predator-prey system, Figure 4.2: Torque curves for typical car engine, Figure 4.3: Car with cruise control encountering a sloping road, Figure 5.10: Phase portraits for a congestion control protocol running with N = 60 identical source computers, Figure 5.11: Comparison between phase portraits for a nonlinear system and its linearization, Figure 5.12: Stability analysis for a tanker, Figure 5.13: Solution curves for a stable limit cycle, Figure 5.15: Stability of a genetic switch, Figure 5.16: Dynamics of a genetic switch, Figure 5.17: Stabilized inverted pendulum, Figure 5.1: Response of a damped oscillator, Figure 5.3: Phase portraits, Figure 5.4: Equilibrium points for an inverted pendulum, Figure 5.5: Phase portrait and time domain simulation for a system with a limit cycle, Figure 5.6: Illustration of Lyapunov’s concept of a stable solution, Figure 5.7: Phase portrait and time domain simulation for a system with a single stable equilibrium point, Figure 5.8: Phase portrait and time domain simulation for a system with a single asymptotically stable equilibrium point, Figure 5.9: Phase portrait and time domain simulation for a system with a single unstable equilibrium point, Figure 6.14: Linear versus nonlinear response for a vehicle with PI cruise control, Figure 6.1: Superposition of homogeneous and particular solutions, Figure 6.5: Modes for a second-order system with real eigenvalues, Figure 8.13: Vehicle steering using gain scheduling
See the software page for more information on how to run these scripts.
Additional Information
|