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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.
2009-11-04
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.
2009-05-10
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.
2009-05-10
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