Model Predictive Control:Theory, Computation, and Design,2nd Edition. James B. Rawlings, David Q. Mayne, Moritz M. Diehl.
Chapter 1 is introductory. It is intended for graduate students in engineering who have not yet had a systems course. But it serves a second purpose for those who have already taken the first graduate systems course. It derives all the results of the linear quadratic regulator and optimal Kalman filter using only those arguments that extend to the nonlinear and constrained cases to be covered in the later chapters. Instructors may find that this tailored treatment of the introductory systems material serves both as a review and a preview of arguments to come in the later chapters.
Chapters 2-4 are foundational and should probably be covered in any graduate level MPC course. Chapter 2 covers regulation to the origin for nonlinear and constrained systems. This material presents in a unified fashion many of the major research advances in MPC that took place during the last 20 years. It also includes more recent topics such as regulation to an unreachable setpoint that are only now appearing in the research literature. Chapter 3 addresses MPC <em>design</em> for robustness, with a focus on MPC using tubes or bundles of trajectories in place of the single nominal trajectory. This chapter again unifies a large body of research literature concerned with robust MPC. Chapter 4 covers state estimation with an emphasis on moving horizon estimation, but also covers extended and unscented Kalman filtering, and particle filtering.
Chapters 5-7 present more specialized topics. Chapter 5 addressesthe special requirements of MPC based on output measurement instead of state measurement. Chapter 6 discusses how to <em>design</em> distributed MPC <em>control</em>lers for large-scale systems that are decomposed into many smaller, interacting subsystems. Chapter 7 covers the explicit optimal <em>control</em> of constrained linear systems. The choice of coverage of these three chapters may vary depending on the instructor's or student's own research interests.
Three appendices are included, again, so that the reader is not sent off to search a large research literature for the fundamental arguments used in the text. Appendix A covers the required mathematical background. Appendix B summarizes the results used for stability analysis including the various types of stability and Lyapunov function <em>theory</em>. Since MPC is an optimization-based <em>control</em>ler, Appendix C covers the relevant results from optimization <em>theory</em>.
In this thesis we consider the problem of <em>design</em>ing and implementing Model Predictive Controllers (MPC) for stabilizing the dynamics of an autonomous ground vehicle. For such a class of systems, the non-linear dynamics and the fast sampling time limit the real-time implementation of MPC algorithms to local and linear operating regions. This phenomenon becomes more relevant when using the limited computational resources of a standard rapid prototyping system for automotive applications. In this thesis we first study the <em>design</em> and the implementation of a nonlinear MPC <em>control</em>ler for an Active Font Steering (AFS) problem. At each time step a trajectory is assumed to be known over a finite horizon, and the nonlinear MPC <em>control</em>ler computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We demonstrate that experimental tests can be performed only at low vehicle speed on a dSPACE rapid prototyping system with a frequency of 20 Hz. Then, we propose a low complexity MPC algorithm which is real-time capable for wider operating range of the state and input space (i.e., high vehicle speed and large slip angles). The MPC <em>control</em> algorithm is based on successive on-line linearizations of the nonlinear vehicle <em>model</em> (LTV MPC). We study performance and stability of the proposed MPC scheme. Performance is improved through an ad hoc stabilizing state and input constraints arising from a careful study of the vehicle nonlinearities. The stability of the LTV MPC is enforced by means of an additional convex constraint to the finite time optimization problem. We used the proposed LTV MPC algorithm in order to <em>design</em> AFS <em>control</em>lers and combined steering and braking <em>control</em>lers. We validated the proposed AFS and combined steering and braking MPC algorithms in real-time, on a passenger vehicle equipped with a dSPACE rapid prototyping system. Experiments have been performed in a testing center equipped with snowy and icy tracks. For both <em>control</em>lers we showed that vehicle stabilization can be achieved at high speed (up to 75 Kph) on icy covered roads. This research activity has been supported by Ford Research Laboratories, in Dearborn, MI, USA.
基于matlab SIMULINK<em>模型</em>实现自适应<em>模型</em><em>预测控制</em>，Adaptive MPC <em>control</em>lers adjust their prediction <em>model</em> at run time to compensate for nonlinear or time-varying plant characteristics. To implement adaptive MPC, first <em>design</em> a traditional <em>model</em> <em>predictive</em> <em>control</em>ler for the nominal operating conditions of your <em>control</em> system, and then update the plant <em>model</em> and nominal conditions used by the MPC <em>control</em>ler at run time. For more information, see Adaptive MPC. After updating, the plant <em>model</em> and nominal conditions remain constant over the prediction horizon.
fast_mpc: code for fast <em>model</em> <em>predictive</em> <em>control</em>
Version Alpha (Sep 2008)Yang Wang and Stephen Boyd
fast_mpc contains two C functions, with MATLAB mex interface, that implement the fast mod...