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We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mechanism to imitate the human upper limb. The response of the virtual human upper limb to various inputs and external disturbances is determined by solving the Equations of Motion (EOM). The control input is determined by the MPC Controller using only the current and the desired states of the system. MPC replaces the closed loop optimization problem with an open loop optimization allowing the ease of implementation of control law. Results presented in this thesis show that the proposed controller can produce physically realistic adaptive simulations of a planar upper limb of digital human in presence of impulsive and sustained disturbances.
This text discusses Adaptive Predictive Control Systems from their concepts to their application to the optimization in the operation of industrial plants. The book will represent the scientific and engineering background to SCAP Optimization Systems, which represent the first and only systematic implementation of Adaptive Predictive Control offered in the industrial market.
The Special Issue aims to bring together scientists working in various branches of control theory to discuss manufacturing control problems that include the following: enterprise control and digital ecosystem creation; the development of identification theory and methodology, and related mathematical problems; parameter, nonparametric, and structure identification and expert analysis; problems regarding selection and data analysis; control systems with an identifier; modeling in intelligent systems; simulation procedures and software; digital identification; reinforcement learning; quantum modeling; intelligent model predictive control; predictive cognitive issues; problems with software quality for complex systems; and global network resources for support processes of modeling and control.
This focused treatment includes the fundamentals and some state-of-the-art developments in the field of predictive control. A substantial part of the book addresses application issues in predictive control, providing several interesting case studies for more application-oriented readers.
Using a common unifying framework, this volume explores the main topics of Linear Quadratic control, predictive control, and adaptive predictive control -- in terms of theoretical foundations, analysis and design methodologies, and application-orient ed tools.Presents LQ and LQG control via two alternative approaches: the Dynamic Programming (DP) and the Polynomial Equation (PE) approach. Discusses predicable control, an important tool in industrial applications, within the framework of LQ control, and presents innovative predictive control schemes having guaranteed stability properties. Offers a unique, thorough presentation of indirect adaptive multi-step predictive controllers, with detailed proofs of globally convergent schemes for both the ideal and the bounded disturbance case. Extends the self-tuning property of one-step-ahead control to multi-step control.For engineers and mathematicians interested in the theory, analysis and design methodologies, and application-oriented tools of optimal, predictive and adaptive control.
This book offers a novel approach to adaptive control and provides a sound theoretical background to designing robust adaptive control systems with guaranteed transient performance. It focuses on the more typical role of adaptation as a means of coping with uncertainties in the system model.
Model Predictive Control Toolbox provides functions, an app, and Simulink blocks for designing and simulating model predictive controllers (MPCs). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance. You can adjust the behavior of the controller by varying its weights and constraints at run time. To control a nonlinear plant, you can implement adaptive and gain-scheduled MPCs. For applications with fast sample rates, you can generate an explicit model predictive controller from a regular controller or implement an approximate solution. For rapid prototyping and embedded system implementation, the toolbox supports automatic C-code and IEC 61131-3 Structured Text generation. The most important features that this Toolbox provides are the following: - Inroduction: Learn the basics of Model Predictive Control Toolbox - Plant Specification: Specify plant model, input and output signal types, scale factors - MPC Design: Basic workflow for designing traditional (implicit) model predictive controllers - Adaptive MPC Design: Adaptive control of nonlinear plant by updating internal plant model at run time - Explicit MPC Design: Fast model predictive control using precomputed solutions instead of run-time optimization - Gain-Scheduled MPC Design: Gain-scheduled control of nonlinear plants by switching controllers at run time - Case-Study Examples