Automatic Control

Introducing course, terminology, historical overview. Classification of control systems. Principle of feedback. Formal representation of control systems. Mathematical modelling. Static and dynamic working regime. Linearization. Responses of linear time invariant (LTI) systems. Use of Laplace transform. Basic dynamic components of control systems. Transfer function and frequency characteristics. Stability analysis: Lyapunov, algebraic and frequency methods. Internal model principle. Sensitivity. Digital control systems. Choice of the sampling period. Mathematical description of A/D and D/A converters, quantization. Discretization methods. Mathematical models of discrete-time systems. Controllability and observability. Performance indices of control systems. Introduction to design. PID regulator and parametrization of PID regulators. Feedforward and cascade control. Digital PID regulator. Windup and antiwindup. Design of digital control system by emulating continuous system.

Multilevel organization of distributed control systems for automation of plants and processes. Functions and databases of automation levels. Programmable logic controllers (PLCs) - architectures, programming and application examples. Individual work with PLCs - logical functions, PID controller. Communications in automation systems. Examples of industrial communication networks and protocols. Individual work with industrial communication networks. Introduction to real-time databases. Human - control system communication interface and SCADA programs. Individual work with a SCADA program.

Computer supported automatic control. Requirements, structures and implementations of computer controlled systems. Mathematical description of discrete-time systems - a short overview. Graphoanalytical identification methods of process mathematical models. Approaches to digital controllers design. Control systems design in time domain - relay method of PID controller design, general linear parametric controller and its design by optimization. Control systems design in frequency domain: Design by Bode diagrams. Lead-lag compensator. Analytical methods of control systems design: Truxal-Guillemin method. Control of systems with considerable delay. Smith predictor. Process periphery. Signal pre-processing in the digital automatic control system. Implementation aspects of the control algorithms. Distributed control systems. Computer networks for real-time applications. Event-triggered protocols. Time-triggered protocols. Sampling time selection of the control loops in distributed control systems. Basics of synthesis of controlled systems over the communication network. Examples of computer controlled systems.

The purpose of this course is to provide the students with a working introduction to the MATLAB technical computing environment and give the practical knowledge about programming techniques in MATLAB. Themes of vector and matrix data analysis, graphical visualization, data modeling, and MATLAB programming are explored in the context of realistic examples.

High complexity of tasks that the modern mobile robots are facing calls for using a programming infrastructure which enables efficient integration of independently developed subsystems into a single system enabling autonomous robot operation. The Robot Operating System (ROS) offers an environment for developing modular control software, a communication infrastructure to connect the software components and an open source library of implemented algorithms. In the last five years ROS has become the standard for robot control in the academic community and its influence is spreading also in the industry. In the scope of this course we shall cover the practical development of software modules in the ROS environment and their integration into a completely functional system for autonomous robot control.

Dynamic model of induction and permanent magnet synchronous motor (PMSM) machine. Induction motor scalar and vector control, brushless DC machine. Vector control structures with voltage and current inverter. Pulse with modulation and vector modulation. Vector model variable and parameters estimation of induction machine. Direct torque and flux control of AC induction machine. Variables estimation. Control of the brushless DC motor.
Electrical drives with complex mechanical load - significant impact of torsion, friction, backlash, inertia variation. Illustrative examples.
Elasticity, friction and backlash modeling. Motion controller synthesis based on the damping optimum and modulus optimum. Improvement of the tracking accuracy using feedforward controller. Algorithms for friction and backlash compensation. Positioning: point to point positioning.

Methods for system model estimation (system identification) and system states identification are elaborated. System identification based on deterministic excitation signals. Nonparametric system identification methods in time and frequency domain. Estimation of the system model parameters: least-squares method, instrumental variables method, maximal likelihood method. Selection of the model structure and verification of the obtained model. State estimation of the deterministic systems. State estimation of the stochastic systems: linear and nonlinear Kalman filters. Simultaneous estimation of system parameters and states. Examples of estimation techniques application to system control, system fault detection, signal processing etc.

General considerations regarding mobile robots: basic terms, definitions, classifications, historical development, applications and examples of mobile robots. Mobile robots hardware: drive mechanisms, actuators, sensors, control circuits. Non-visual and visual perception sensors. Robot sensor signals processing and interpretation. Multiple sensors information fusion in order to improve quality and robustness of robots navigation through space. Control and navigation system structures. Methods and algorithms of control and navigation system for obstacle avoidance, unknown space exploration, map building, localization and path planning for mobile robots. Introduction to self learning mobile robots and human-robot communication. Basics of coordinated work of multiple autonomous mobile robots.

Controlled jerk trajectory planning methods. Linear and nonlinear load torque estimation methods. Control of six-legged walking robots using cyclic GA. Remote robot control systems. Force feedback. Compensation of communication delay influence using wave variables and event triggered control. Creation of operator feeling about presence in the remote workspace. Influence of communication delay on the remote guidance quality. Mathematical models of aerial vehicles: balloon, helicopter, quadrotor. Actuators and sensors of unmanned aerial vehicles (UAV): inertial measurement system. Control of UAV. Motion in formations.

Estimation problems definition. The role of the estimation techniques in complex systems. State estimation of deterministic systems. State estimation of stochastic systems: linear and nonlinear Kalman filters, particle filters. Dual estimation, multi-model estimation. Measurement data set compression. Application examples: estimation of difficult-to-measure variables, multi-sensor information fusion and fault detection in various technical systems, neural network training, object tracking etc.

Introduction to autonomous systems: definitions, design, control architectures, examples, applications. Environment perception and modeling. Local and global self-localization of the autonomous systems based on the environment model. Simultaneous environment modeling and system self-localization. Path planning and following. Collision detection and obstacle avoidance. Unknown environments exploration. Intelligent self-learning autonomous systems. Coordination of several autonomous systems.