Boris Tovornik

Swinging up and stabilization of a real inverted pendulum

The basic aim of the present work was to swing up a real pendulum from the pending position and to balance stably the pendulum at the upright position and further move the pendulum cart to a specified position on the pendulum rail in the shortest time. Different control strategies are compared and tested in simulations and in real-time experiments, where maximum acceleration of the pendulum pivot and length of the pendulum rail are limited. A comparison of fuzzy swinging algorithm with energy-based swinging strategies shows advantages of using fuzzy control theory in nonlinear real-time applications. An adaptive state controller was developed for a stabile, and in the same time optimal balancing of an inverted pendulum and a switching mechanism between swinging and balancing algorithm is proposed.

Modeling and control of the magnetic suspension system.

A fuzzy logic based controller applied to a simple magnetic suspension is presented in this paper. The simple electromagnet-ball system and the contactless optical position measurement system are developed as a physical model of the magnetic suspension. A nonlinear mathematical model is presented and linearized. This model has been used to design a discrete linear PID controller with optimal parameters. The physical real-time model was constructed in order to compare the performance of the linear discrete PID controller and the proposed fuzzy logic based PID controller. The decomposed fuzzy PID controller has proportional, integral, and derivative separate parts which are tuned independently. When testing it becomes clear that the decomposed fuzzy PID controller gives better performance over a typical operational range than a traditional linear PID controller.

How to design a discrete supervisory controller for real-time fuzzy control systems

The task of the supervisory controller is to stabilize the systems states within a bounded region defined by designer. In this paper, a discrete approach to the solution of the stable fuzzy control system is presented. It is proved that the fuzzy control system equipped with the discrete supervisory controller is globally stable in the Lyapunov sense. Finally, a fuzzy controller with a discrete supervisory controller is applied to the balance control system, both in simulations and in the real-time implementation.

Real-time implementation of fault diagnosis to a heat exchanger

Abstract The detection and isolation of faults in engineering systems has lately become of great significance. This paper is concerned with application of analytical fault detection techniques to a heat exchanger. The system is nonlinear and a velocity-based linearization is proposed before the residual generation, which is then realized using an observer, and parity relations. The problem of reasoning is treated by an approximate reasoning approach, called the transferable belief model. Its important feature is the ability to treat inconsistency in data by using more general belief functions. The system under consideration is controlled by programmable logic controller and supervised by supervisory control and data acquisition system. The procedure for implementation is designed with particular emphasis on industrial practice and the Ole for process control data access client/server technology is used for communication between sub-systems.

Autotuning Autopilots for Micro-ROVs

Underwater vehicles are highly nonlinear and complex systems, that makes designing autopilots extremely difficult. This paper presents autotuning as a method for tuning parameters of a micro-ROV autopilot. The main benefit of this procedure is that the model of the process does not have to be known. Autotuning is often used for industrial processes but not on marine vessels. This procedure, which is performed in closed-loop, is completely automated and enables the operator to retune an autopilot whenever ROV performance is degraded (due to different operating points, tether influence, currents, etc.). In this article we use already known different autotuning recommendations (primarily designed for type 0 processes) with some modifications which we recommend for micro-ROVs. We also give results of using different types of PID controllers, whose parameters are being tuned. A real life demonstration on a VideoRay Pro II micro-ROV is provided

Input-output modelling with decomposed neuro-fuzzy ARX model

This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and an optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set.

Design of an auto-associative neural network by using design of experiments approach

Advanced monitoring systems enable integration of data-driven algorithms for various tasks, for e.g., control, decision support, fault detection and isolation (FDI), etc. Due to improvement of monitoring systems, statistical or other computational methods can be implemented to real industrial systems. Algorithms which rely on process history data sets are promising for real-time operation especially for online process monitoring tasks, e.g., FDI. However, a reliable FDI system should be robust to uncertainties and small process deviations, thus, false alarms can be avoided. To achieve this, a good model for comparison between process and model is needed and for easier FDI implementation, the model has to be derived directly from process history data. In such cases, model-based FDI approaches are not very practical. In this paper a nonlinear statistical multivariate method (nonlinear principal component analysis) was used for modeling, and realized with auto-associative artificial neural network (AANN). A Taguchi design of experiments (DoE) technique was used and compared with a classic approach, where according to the analysis best AANN model structure was chosen for nonlinear model. Parameters that are important for neural network’s performance have been included into a joint orthogonal array to consider interactions between noise and control process variables. Results are compared to AANN design recommendations by other authors, where obtained nonlinear model was designed for reliable fault detection of very small faults under closed-loop conditions. By using Taguchi DoE robust design on AANN, an improved and reliable FDI scheme was achieved even in case of small faults introduced to the system. The accuracy and performance of AANN and FDI scheme were tested by experiments carried out on a real laboratory hydraulic system, to validate the proposed design for industrial cases.

Identification of non-linear dynamic systems with decomposed fuzzy models

This paper presents an approach which is useful for the identification of discrete non-linear dynamic systems based on fuzzy relational models. Fuzzy systems are characterized by a rule-base specification. If the complexity of a rule-base increases, knowledge acquisition may become tedious because the number of rules increases with an increasing number of fuzzy variables. Decomposed fuzzy models are proposed and applied to dynamic systems modeling. The evolution of the identification algorithms for the decomposed fuzzy model is suggested. A comparative study of the dynamic system identification with the conventional relational model and the decomposed relational model is presented for a well-known identification problem, namely the Box-Jenkins gas furnace data.

MODELLING, SIMULATION AND FUZZY CONTROL OF THE GMAW WELDING PROCESS

Abstract Welding is an important manufacturing process that can be automated and optimised. The dynamic characteristic of a self-regulated, consumable electrode welding arc has been studied when the torch-to-work-piece distance varies with time. The self-regulation process have been modelled analytically and various dynamic models have been developed. Computer simulations have been used to obtain a better understanding of the mechanisms which change arc voltage and current in response to changes in arc length. A welding current fuzzy controller has been proposed.

AUV FORMATIONS ACHIEVED BY VIRTUAL POTENTIALS TRAJECTORY PLANNING IN A SIMULATED ENVIRONMENT

This paper explores the forming-up, robustness of the ensuing formation and coordinated movement of autonomous, non-communicating submerged vehicles (AUV) planning their trajectories using a virtual potential fields method. The behavior and characteristic merits and problems of the proposed scheme, which plans the trajectory on the basis of AUV kinematics is tested in 2D simulations. A brief commentary on further avenues of research and improvement in order to make the method applicable to hardware-in-the-loop usage is given.