This course introduces the students to the fundamental concepts of control systems engineering. Students will be exposed to techniques of modeling physical systems involving linear and non-linear systems including mechanical, electrical, and mechatronic systems. Both the frequency domain and time domain (state-space) are covered. Several criteria for performance and stability analyses of control systems will be taught. Modeling and analysis of control systems in discrete-time for digital control will also be introduced. The student will also be exposed to MATLAB for the design, development, and analysis of simulation models. Finally, a feedback control system with the controller to achieve control system objectives are described. Several case studies of the applications of controllers will be used to enhance student understanding.

MEEM 1713 Artificial Intelligence and Applications

Artificial intelligence (AI) involves the development of algorithms derived from human & animal intelligence that have capabilities such as learning, reasoning, generalization, adaptation, reproduction, etc. Nowadays, these techniques are getting popular due to a large number of successful reports of implementations. AI techniques have also made their way into many domestic & industrial products & provided solutions to many difficult engineering problems. In this course, students are exposed to several AI techniques i.e., Artificial Neural Network (ANN), Fuzzy Logic, Genetic Algorithm (GA) & Particle Swarm Optimization (PSO), & how they are used in solving engineering & non-engineering problems.

MEEM1753 Advanced Instrumentation and Measurement

This course is an introduction to advanced instrumentation and measurement. Key components studied in detail are a review of powerful measurement techniques and basic principles and typical problems of sensor elements, detailed up-to-date reviews of the features of temperature sensors, displacement sensors, flow sensors, level sensors, position sensors, motion sensors, and biometrics. This course also provides detailed knowledge on error and determination of uncertainties in measurement. Besides that, this course introduces the multi-sensor, Fusion application, wireless sensor network, and Internet of Things. Finally, the basic concepts of safety instrumented systems, standards, and risk analysis techniques will be discussed.

MEEM 1803 Embedded Control Systems

This course introduces the principles and applications of embedded systems. The topics emphasized are the microcontroller system architecture, software programming using C, and system design. The content covers internal peripherals such as general input and output, analog to digital converter, serial communication interface, timer/counter, and interrupt. Emphasis on the software will include interrupt servicing, multi-tasking, and task communication and scheduling. The relation of system sampling time related to closed-loop control will also be covered. At the end of the course, the students will learn the technique to interface the microcontroller system with other devices in the embedded system for real-world application.

MEEM 1783 Nonlinear and Robust Control Systems

This course covers the analysis and design of nonlinear control systems using Lyapunov Theory. The contents of the course include the introduction to nonlinear dynamical systems, behaviors, and properties of solutions of nonlinear dynamical systems, Lyapunov stability analysis techniques and nonlinear control design tools for stabilization using state feedback and output feedback linearization, integral control, and gain scheduling.

MEEM 1763 System Identification and Adaptive Control

This course is an introduction to system identification and adaptive control. In the first part, the course covers an introduction to system identification, acquiring and pre-processing data, nonparametric model estimation methods, parametric model estimation methods, partially known estimation methods, model estimation methods in closed-loop systems, recursive model estimation methods, analyzing, validating, and converting models and system identification case study. This requires an in-depth understanding of control system engineering, modern control system, and digital control system. The emphasis will be on the theoretical basis as well as practical implementations. Key components studied in detail are time response analysis, frequency response analysis, correlation analysis, power spectrum density analysis, model structure, parametric model, parameter estimation method, test signals, and model validation methods.  The second part of this course introduces the students to adaptive and self-tuning control.  The students will first learn the real-time parameter estimation technique, which will provide them with the key concepts required to understand many aspects of adaptive and self-tuning control.  The students will then be exposed to the main techniques in Self-Tuning Control (STC), the Pole Assignment, and Minimum Variance Control.  For the adaptive control, the students will be exploring the Model Reference Adaptive Control (MRAC) design using Gradient Approach/MIT Rule and the Lyapunov method.  Finally, some practical issues on implementation, applications, and perspectives of adaptive and self-tuning control will be discussed.

MEEM 1723 Advanced Process Control

This course introduces the implementation of various control system designs and strategies in industries.  The first part provides an introduction to the classical and modern control systems, the mathematical formulation of the dynamic behavior of systems using theoretical and empirical principles. Then, discussion on how to identify the control structure to handle different control problem formulation such as feedback control system, cascade control system, feed-forward control system, and internal model control. This course covers SISO and MIMO control systems, which analyzed the robust stability and performance of these systems. In enhancing the performance of the system, advanced control techniques are utilized such as adaptive control and model predictive control. At the end of the course, several case studies related to real plant-wide control in specific applications are introduced to reflect the various control ideas. The use of intelligent control and soft computing are also embedded in the specific case studies.

MEEM 1943 Model Predictive Control

This course introduces the theory and practice of Model Predictive Control (MPC).  The course syllabus begins with the philosophical thinking behind predictive control and continues with modeling assumptions as a fundamental part of MPC. Then, the procedure of prediction using mathematical models is introduced with state-space and transfer function models. The unbiased prediction is introduced to address the accuracy of the predictions. For measuring the control performance, a performance index is constructed. At the end of the course, several types of linear predictive control algorithms are introduced to demonstrate the predictive application. The course will make use of the MPC Toolbox for MATLAB developed by The MathWorks, Inc.

MEEM 1953 Linear Control System Design

This course is an introduction to linear control system design. It is intended to give a good background for designing the performance of a linear control system. Students will learn how to use the mathematical equations of the system to analyze its dynamic performance in terms of stability, controllability, and observability. This course will cover the design of controllers using pole placement and Linear Quadratic Regulator (LQR).

MEEM 1893 Embedded Systems for Innovative Product Design

This course emphasizes the engineering knowledge and skills in the design of an embedded system for the development of an innovative product. As the continuation of the Embedded Control System, the materials covered in the course will further address the problems of designing electronics/embedded systems for mechatronic systems that meet customized user demand specifications. Real-time and safety criticalness, design constraints, hardware-software partitioning, and time-to-market elements will be the main elements to be addressed. Efficient coding techniques for embedded systems, the basic operation of real-time operating systems (RTOS), schedulability of periodic task sets, and design refinement are among the main contents of the courses. The Design Thinking (DT) approach will be adapted for the proposal of product development. At the end of the course, students will be able to integrate the components of the software and hardware to form an efficient embedded system for specific product design. Embedded system design tools will be used to facilitate the design process. At the end of the course, students in a group will develop an embedded system-based product as a testimony of the knowledge and skill acquired during the course.

MEEM 1883 Autonomous Mobile Robotics

This course gives the students an in-depth treatment of the main aspects of autonomous mobile robotics namely mechanism & locomotion, intelligence in mobile robotics, and sensor fusion for autonomous decision-making capability. The course delivery is not limited to lectures, tutorials only but also personal reading, research-based assignments on frontier knowledge materials and actual experimental research carried out in UTM’s mobile robotics laboratory. The course blends knowledge derived in-house with actual physical world autonomous mobile robotics, hence providing the unique experimental learning geared towards carrying out research.

MEEM 1823 Advanced Robotics

As technology advances, it has been envisioned that in the very near future, robotic systems will become part and parcel of our everyday lives. Even at the current stage of development, semi-autonomous or fully automated robots are already indispensable in a staggering number of applications. To bring forth a generation of truly autonomous and intelligent robotic systems that will meld effortlessly into human society involves research and development on several levels, from robot perception, to control, to abstract reasoning. This course tries for the first time to provide a comprehensive treatment of autonomous mobile systems and examines the fundamental constraints, technologies, and algorithms of autonomous robotics. The focus of this course will be on computational aspects of autonomous wheeled mobile robots. The following topics will be covered: major paradigms in robotics, methods of locomotion, kinematics, simple control systems, sensor technologies, stereo vision, feature extraction, modeling uncertainty of sensors and positional information, localization, SLAM, obstacle avoidance, and 2-D path planning.

MEEM 1913 Mechatronic Design

This course introduces mechatronics as an integrated design approach with the synergistic combination of mechanical, electronics, control, and computer engineering.  It provides insight into the advantages and challenges of the mechatronics design approach. The course introduces the various aspects of mechatronics design including physical system modeling, simulation, sensors, and actuators selection, computer interfacing, and real-time control implementation.  This course tries to balance between theoretical and practical aspects, and hardware implementation is emphasized.  Several case-study from industrial projects and the problem-solving approaches of real systems are used throughout the course.

MEEM 1923 Sensors and Actuators

This course introduces the working principle of sensors and actuators and their application in mechatronics systems. This course covers the fundamentals of sensors and actuators, the details of their functionality, the characteristic, the fabrication, and materials used, numerical study, and the system integration of sensors and actuators in mechatronics systems. Various case studies are introduced and discussed during classes to help further understanding the diversity of the mechatronics systems in multidisciplinary fields.

MEEM 1813 Smart Manufacturing

Industry 4.0 is the new wave in manufacturing that involves a combination of cyber-physical systems, automation, and the Internet of Things (IoT) which is often called Smart Manufacturing. This course is to introduce what is Smart Manufacturing and to actually learn some of the important components in smart manufacturing such as industrial automation, robotics, machine vision, AI, and Big Data. This course also studies several real industries case studies.

MEEM 1903 Rapid Prototyping and Simulation

This course introduces rapid prototyping technologies that can help to speed up the process of designing and verifying a mechatronic system. The participants gain hands-on experience of the required skills in computer-aided design and knowledge of machine elements to model and verify a mechatronic system. In the hands-on project, the participants learn how to interface input-output (IO) devices to control a model of a mechatronic system. Lastly, the participants learn how to generate the code of the model and deploy it on an embedded controller board.

MEEM 1933 Cyber-Physical System

Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. Designing algorithms to control CPS is challenging due to their tight coupling with physical behavior. The future CPS workforce is likely to include CPS engineers, who focus on the knowledge and skills spanning cyber technology and physical systems that operate in the physical world. This course provides a foundation that highlights the interaction of cyber (computation and/or communication) and physical aspects (physical plants) of systems.