Model predictive control of a mechatronic system with variable inputs

The mechatronic system with variable parameters operating under varying conditions discussed in this paper refers to an electric drive system for winding a strip with constant linear velocity on a drum. The continuously variation of the plant inputs, namely reference and load disturbance, is determined by the increasing radius and hence moment of inertia of the drum. This paper suggests a conventional control structure with a model predictive controller using a detailed mathematical model of the plant. This structure is advantageous due to its simplicity, straightforward formulation and simple design.

[1]  Claudia-Adina Dragos,et al.  Iterative performance improvement of fuzzy control systems for three tank systems , 2012, Expert Syst. Appl..

[2]  Stefan Preitl,et al.  Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications , 2006 .

[3]  Horia-Nicolai Teodorescu On the Characteristic Functions of Fuzzy Systems , 2013, Int. J. Comput. Commun. Control.

[4]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[5]  Robert Grover Brown,et al.  Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions , 1996 .

[6]  Eneko Osaba,et al.  AMCPA: A Population Metaheuristic With Adaptive Crossover Probability and Multi-Crossover Mechanism for Solving Combinatorial Optimization Problems , 2014 .

[7]  József K. Tar,et al.  On the design of an obstacle avoiding trajectory: Method and simulation , 2009, Math. Comput. Simul..

[8]  Saša S. Nikolić,et al.  Sensitivity Analysis of Imperfect Systems Using Almost Orthogonal Filters , 2012 .

[9]  A. TUSTIN,et al.  Automatic Control Systems , 1950, Nature.

[10]  Stefan Preitl,et al.  Fuzzy controllers for tire slip control in anti-lock braking systems , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[11]  Stefan Preitl,et al.  PI and PID controllers tuning for integral-type servo systems to ensure robust stability and controller robustness , 2006 .

[12]  Rodolfo E. Haber,et al.  Nonlinear internal model control using neural networks: an application for machining processes , 2004, Neural Computing & Applications.

[13]  Ahmad Jafarian,et al.  New Artificial Intelligence Approach for Solving Fuzzy Polynomial Equations , 2014 .

[14]  I. Dumitrache,et al.  SOME PROBLEMS OF ADVANCED MOBILE ROBOT CONTROL , 2006 .

[15]  Tong Heng Lee,et al.  Adaptive Non-Model-Based Vibration Control of Critical Flexible Modes in Mechatronic Systems , 2011 .

[16]  Oscar Castillo,et al.  Introduction to an optimization algorithm based on the chemical reactions , 2015, Inf. Sci..

[17]  Lee Tong Heng,et al.  Applied Predictive Control , 2001 .

[18]  Plamen P. Angelov,et al.  Online self-evolving fuzzy controller for autonomous mobile robots , 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

[19]  Rolf Isermann,et al.  Mechatronic Systems: Fundamentals , 2003 .

[20]  Krzysztof Kozlowski,et al.  Design of a Planar High Precision Motion Stage , 2009 .

[21]  Stefan Preitl,et al.  Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems , 2012, IEEE Transactions on Industrial Informatics.

[22]  Stefan Preitl,et al.  Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity , 2013, Inf. Sci..

[23]  Ján Vascák,et al.  Adaptation of fuzzy cognitive maps by migration algorithms , 2012, Kybernetes.

[24]  Levente Kovacs,et al.  Applicability Results of a Nonlinear Model-Based Robust Blood Glucose Control Algorithm , 2013, Journal of diabetes science and technology.

[25]  Drago Matko,et al.  Quadrocopter Hovering Using Position-estimation Information from Inertial Sensors and a High-delay Video System , 2012, J. Intell. Robotic Syst..

[26]  Motohiro Kawafuku,et al.  PRECISE MODELING OF ROLLING FRICTION IN BALL SCREW-DRIVEN TABLE POSITIONING SYSTEM , 2006 .

[27]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

[28]  Radu Calinescu,et al.  Large-scale complex IT systems , 2011, Commun. ACM.

[29]  Zsolt Csaba Johanyák,et al.  Fuzzy Modeling of Thermoplastic Composites' Melt Volume Rate , 2014, Comput. Informatics.

[30]  Claudia-Adina Dragos,et al.  Adaptable fuzzy control solutions for driving systems working under continuously variable conditions , 2013, 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI).

[31]  Emil M. Petriu,et al.  Experiment-Based Teaching in Advanced Control Engineering , 2011, IEEE Transactions on Education.

[32]  Stefan Preitl,et al.  Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications , 2006 .

[33]  Klaus Zeman,et al.  CONCEPTUAL DESIGN OF MECHATRONIC SYSTEMS AS A RECURRING ELEMENT OF INNOVATION PROCESSES , 2006 .