Damir Vrančić

Teaching particle swarm optimization through an open-loop system identification project

The particle swarm optimization (PSO), one of the most successful natural inspired algorithms, is revisited in the context of a proposal for a new teaching experiment. The problem considered is the open‐loop step identification procedure, which is studied as an optimization problem. The PSO canonical algorithm main issues addressed within the proposed open‐loop step identification experience are: the swarm random initialization methodology, the population size variation, and the inertia weight selection. The teaching experience learning outcomes are stated, simulation results presented, and feedback results from students analyzed.

Multi-Objective Particle Swarm Optimization Design of PID Controllers

A novel variant of a multi-objective particle swarm optimization algorithm is reported. The proposed multi-objective particle swarm optimization algorithm is based on the maximin technique previously proposed for a multi-objective genetic algorithm. The technique is applied to optimize two types of problems: firth to a set of benchmark functions and second to the design of PID controllers regarding the classical design objectives of set-point tracking and output disturbance rejection.

Underdamped Second-Order Systems Overshoot Control

Abstract The paper addresses the problem of decreasing the overshoot for underdamped second-order systems. A new technique to control the overshoot is proposed, which is based on Posicast control and proportional integral and derivative (PID) control, which performs switching between two controllers. The aim is to use open-loop feedforward control to increase tracking performance and PID control to deal with disturbance rejection. It has been shown that the proposed control scheme can have some advantages over the classical approaches without switching capabilities.

Grey Wolf, Gravitational Search and Particle Swarm Optimizers: A Comparison for PID Controller Design

Nature and biologically inspired metaheuristics can be powerful tools to design PID controllers. The grey wolf optimization is one of these promising and interesting metaheuristics, recently introduced. In this study the grey wolf optimization algorithm is proposed to design PID controllers, and the results obtained compared with the ones obtained with gravitational search and particle swarm optimization algorithms. Simulation results obtained with these three bio-inspired metaheuristics applied to a set of benchmark linear plants are presented, considering the design objective of set-point tracking. The results are also compared with two non-iterative PID tuning techniques.

Comparing filtered PID and smith predictor control of a thermal plant

The paper compares filtered PID control with two filtered Smith predictor modifications and experimentally points out different impact of the derivative terms in the considered controllers on the loop robustness and performance. All structures applied to a thermal plant control are based on its approximation by the first order time-delayed model. They include an nth order binomial filter for measurement noise attenuation. In order to stress the time delay impact, the intrinsic plant time delay is increased by an additional delay in Simulink. The evaluation shows a central role of an appropriate filtration in the controller design. It enables a significant control effort reduction by keeping nearly the same speed of controlled processes. To avoid different tuning scenarios for particular controllers, choice of an optimal solution is based on noise characteristics confronting the speed of transients with the additional control effort required.

Dual Mode Feedforward-Feedback Control System

A dual mode control configuration involving open-loop feedforward control to deal with set-point tracking and proportional integrative and derivative control to deal with disturbance rejection is proposed. The feedforward controller is a Posicast pre-filter shaping the reference input using either two or three steps. The switching between the open-loop and closed-loop control is performed automatically. The particle swarm optimization algorithm is deployed as design tool both for the feedforward and PID controller. Simulation results are presented showing the merits of the proposed technique.

Design of feedback control for underdamped systems

Abstract In practice, there are several processes which are exhibiting oscillatory behaviour. Some representatives are disk-drive heads, robot arms, cranes and power-electronics. One of techniques, aimed at reducing the oscillations, is Posicast Input Command Shaping (PICS) method. The paper combines the PICS method and Magnitude Optimum Multiple Integration (MOMI) tuning method for PID controllers. The combination of both methods significantly improves the speed and stability of the closed-loop tracking responses. Moreover, the proposed approach is relatively simple for implementation in practice and can be used either on process time-response data or on the process model in frequency-domain.

The Model-Based Disturbance Rejection with MOMI Tuning Method for PID Controllers

The paper presents a tuning method for PID controllers which substantially improves closed-loop disturbance rejection performance while keeping the tracking performance. The tuning method is based on the internal disturbance compensator which parameters are calculated according to the Magnitude Optimum criterion. The results of experiments show that the proposed model-based approach gives superior disturbance-rejection response and lower controller activity when compared to Disturbance Rejection Magnitude Optimum tuning method.

A PLC-Based System for Advanced Control

The chapter presents a PLC-based system for advanced control called ASPECT. The ASPECT controller was designed to be an efficient and user-friendly engineering tool for the implementation of parameter-scheduling nonlinear control in the process industry, which is achieved by partial automation of the commissioning procedure. The key to the concept is the self-tuning mechanism. The controller parameters are automatically tuned from a nonlinear process model. The model is determined on the basis of operating process signals by experimental modelling, where an online-learning procedure is used. This procedure is based on model identification using the local learning approach. The two main components of the ASPECT system are the Run-time Module (RTM) and the Configuration Tool (CT). The RTM runs on a PLC or an embedded controller, performing all the main functionality of real-time control, online learning, and control performance monitoring. The CT, used on a personal computer (PC) only during the initial configuration phase, simplifies the commissioning procedure by providing guidance and default parameter values. The performance of the system is demonstrated with simulation experiments on a pH control process and with experimental application to an industrial valve-testing apparatus. In the conclusion, the lessons learned during the development and implementation of the system are discussed.