PTCIC336 Model Based Control Syllabus:

PTCIC336 Model Based Control Syllabus – Anna University Part time Regulation 2023

COURSE OBJECTIVES:

 To introduce the Knowledge about Multivariable and Multiloop systems.
 To understand the Model predictive control schemes and its elements.
 Get exposed to state space MPC along with case studies.
 To acquire knowledge on various constrained MPC.
 To make the student understand the principles of STR, MRAC and Gain scheduling.
 To make the student design simple adaptive controllers for linear systems

UNIT I INTRODUCTION TO MIMO CONTROL

Introduction to MIMO Systems-Multivariable control-Multiloop Control-Multivariable IMC-IMCPIDCase studies

UNIT II MODEL PREDICTIVE CONTROL SCHEMES

Introduction to Model Predictive Control – Model Predictive Control Elements – Generalized Predictive Control Scheme – Multivariable Generalized Predictive Control Scheme – Multiple Model based Model Predictive Control Scheme Case Studies

UNIT III STATE SPACE BASED MODEL PREDICTIVE CONTROL SCHEME

State Space Model Based Predictive Control Scheme – Review of Kalman Update based filters – State Observer Based Model Predictive Control Schemes – Case Studies

UNIT IV CONSTRAINED MODEL PREDICTIVE CONTROL SCHEME

Constraints Handling: Amplitude Constraints and Rate Constraints –Constraints and Optimization – Constrained Model Predictive Control Scheme – Case Studies.

UNIT V ADAPTIVE CONTROL SCHEME

Introduction to Adaptive Control-Gain Scheduling-Self tuning regulators–MARS-Adaptive Model Predictive Control Scheme –Case Studie

TOTAL:45 PERIODS
SKILL DEVELOPMENT ACTIVITIES (Group Seminar/Mini Project/Assignment/Content Preparation / Quiz/ Surprise Test / Solving GATE questions/ etc)

1 Explore various MIMO controllers presently used in industries.
2 Develop MPC, Adaptive and MIMO controllers for industrial processes.
3 Implement the controllers for MIMO systems.
4 Using software tools for practical exposures to the controllers used in industries by undergoing training.
5 Realisation of various optimization techniques for economical operation of process.

COURSE OUTCOMES:

Students able to
CO1 Ability to apply engineering knowledge to understand the control schemes on MIMO systems
CO2 Ability to design controller for MIMO system
CO3 Ability to analyze the control schemes available in industries
CO4 Ability to design MPC, Adaptive controllers for practical engineering problems
CO5 Ability to choose suitable controllers for the given problems

REFERENCES:

1. Paul Serban Agachi, Zoltan K. Nagy, Mircea Vasile Cristea, and Arpad Imre-Lucaci Model Based Control Case Studies in Process Engineering,WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 2007.1st Edition.
2. Ridong Zhang, Anke Xue Furong Gao,“Model Predictive Control Approaches Based on the Extended State Space Model and Extended Non-minimal State Space Model”,Springer Nature Singapore Pte Ltd. 2019, 1st Edition.
3. J.A. ROSSITER “Model-Based Predictive Control A Practical Approach”Taylor & Francis eLibrary, 2005, 1st edition.