Model+Predictive Control System Design and Implementation Using MATLAB

Model predictive control (MPC) has a long history in the field of control en- gineering. It is one of the few areas that has received on-going interest from researchers in both the industrial and academic communities. Four major as- pects of model predictive control make the design methodology attractive to both practitioners and academics. The first aspect is the design formulation, which uses a completely multivariable system framework where the perfor- mance parameters of the multivariable control system are related to the engi- neering aspects of the system; hence, they can be understood and ‘tuned’ by engineers. The second aspect is the ability of the method to handle both ‘soft’ constraints and hard constraints in a multivariable control framework. This is particularly attractive to industry where tight profit margins and limits on the process operation are inevitably present. The third aspect is the ability to perform on-line process optimization. The fourth aspect is the simplicity of the design framework in handling all these complex issues. This book gives an introduction to model predictive control, and recent developments in design and implementation. Beginning with an overview of the field, the book will systematically cover topics in receding horizon con- trol, MPC design formulations, constrained control, Laguerre-function-based predictive control, predictive control using exponential data weighting, refor- mulation of classical predictive control, tuning of predictive control, as well as simulation and implementation using MATLAB and SIMULINK as a platform. Both continuous-time and discrete-time model predictive control is presented in a similar framework.

Cambridge.How.to.Think.About.Algorithms.2008

There are many algorithm texts that provide lots of well-polished code and proofs of correctness. Instead, this one presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. It is a bit like a carpenter studying hammers instead of houses. Jeff Edmonds provides both the big picture and easy step-by-step methods for developing algorithms, while avoiding the comon pitfalls. Paradigms such as loop invariants and recursion help to unify a huge range of algorithms into a few meta-algorithms. Part of the goal is to teach students to think abstractly. Without getting bogged down in formal proofs, the book fosters deeper understanding so that how and why each algorithm works is transparent. These insights are presented in a slow and clear manner accessible to second- or third-year students of computer science, preparing them to find on their own innovative ways to solve problems.

addison wesley - effective software testing

Effective Software Testing provides experience-based practices and key concepts that can be used by an organization to implement a successful and efficient testing program. The goal is to provide a distilled collection of techniques and discussions that can be directly applied by software personnel to improve their products and avoid costly mistakes and oversights. This book details 50 specific software testing best practices, contained in ten parts that roughly follow the software life cycle. This structure itself illustrates a key concept in software testing: To be most effective, the testing effort must be integrated into the software-development process as a whole. Isolating the testing effort into one box in the "work flow" (at the end of the software life cycle) is a common mistake that must be avoided.