A multi-model framework to implement self-managing control systems for QoS management
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Proceedings of the Third Symposium on Information and Communication Technology
Ant colony optimization for model predictive control for blood glucose regulation
Proceedings of the Third Symposium on Information and Communication Technology
Power flow management of microgrid networks using model predictive control
Computers & Mathematics with Applications
Journal of Systems and Software
Weight optimisation for iterative distributed model predictive control applied to power networks
Engineering Applications of Artificial Intelligence
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
Designing cost-efficient wireless sensor/actuator networks for building control systems
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
An Efficient Model Predictive Control Scheme for an Unmanned Quadrotor Helicopter
Journal of Intelligent and Robotic Systems
Adaptive predictive control of time-delay systems
Computers & Mathematics with Applications
Observer based biped walking control, a sensor fusion approach
Autonomous Robots
A cost effective high-speed auto-coded embedded model predictive controller
International Journal of Intelligent Systems Technologies and Applications
Adaptive PID controllers: State of the art and development prospects
Automation and Remote Control
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Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. Issues such as plant optimization and constrained control which are critical to industrial engineers are naturally embedded in its designs. Model Predictive Control System Design and Implementation Using MATLAB proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: continuous- and discrete-time MPC problems solved in similar design frameworks; a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, detailed coverage is given to three industrial applications: a food extruder, a motor and a magnetic bearing system. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book, mainly based on advances in MPC using state-space models and basis functions to which the author is a major contributor, will be of interest to control researchers and practitioners, especially of process control. From a pedagogical standpoint, this volume includes numerous simple analytical examples and every chapter contains problems and MATLAB programs and exercises to assist the student.