Abstract: In this paper we develop rational discrete-time approximations (IIR filters) to continuous fractional-order integrators and differentiators of type sα, α ∈ ℜ. For that, it is proposed the adoption of the techniques of Padé, Prony and Shanks usually applied in the signal modelling of deterministic signals. These methods yield suboptimal solutions to the problem which only requires finding the solution of a set of linear equations. The results reveal that this approach gives similar or superior approximations in comparison with other widely used methods. Their effectiveness is illustrated, both in the time and frequency domains, through several examples.

Keywords: IIR filters, rational approximations, digital differentiators, digital integrators, filter design, least-squares, discretization

Abstract: Motion control has been a fruitful ground for applying Variable Structure Systems (VSS) theory. This paper provides an assessment of the state of the art of the relevant theoretical results for sliding mode control. The design of a sliding-mode controller consists of three main steps. First step is the design of the sliding surface, the second step is the design the control law which holds the system trajectory on the sliding surface, and the third and key step is the chattering-free implementation. The main contribution of that paper is a new method for sliding surface sector design based on tensor product (TP) model transformation to reduce the chattering.

Keywords: Sliding mode control, sliding sector design, Tensor product, friction compensation

Abstract: The contribution sketches a functional-computational typological scale of agents starting form the reactive ones, and puts the family of (at least minimally) conscious agents into the proposed typology. Then it discusses the traditional computational properties of agents according their types, and sketches a way of a rather non-traditional computational characterization of conscious agents using the concept of hyper-computation. The contribution ends with relating the sketched formal approach to agents with agents embodiment, and relates embodiment of agents with their emergence of hypercomputational power.

Keywords: Agent, functional specification, computational specification, emergence, grammar systems, hyper-computation, consciousness

Abstract: H2 and H∞ controllers minimise the H2 or the H∞ norm of a suitable loop transfer function involving the plant to control and some weighing transfer functions chosen to fulfil performance specifications. In this paper this type of controllers is developed for the case when the plant and / or the weighing transfer functions are of fractional (commensurate) order. Since no analytical results similar to those existing for the integer case have been found, a genetic algorithm is used to minimise the desired norm. An application to temperature control is used to illustrate the method.

Keywords: H2 and H∞ controllers, fractional plants, genetic algorithms, temperature control

Abstract: An SME is able to cope with the global challenge if it realizes reliable, balanced and high-standard operation in its business. There are some management and organizational methods increasing the competitiveness of SME. Controlling as a management tool and management function as well as a factor affecting competitiveness came more into focus. Outsourcing of activities not belonging to the main profile of the enterprise seems natural for most SME-s. Family business as the driving motor of the business can bear bigger loads than SME of similar sizes but not organized around a family.

Keywords: competitiveness factors, management methods, family business speciality

Abstract: The paper is focused especially on presenting possibilities of applying off-line trained artificial neural networks at creating the system inverse models that are used at designing control algorithm for non-linear dynamic system. The ability of cascade feedforward neural networks to model arbitrary non-linear functions and their inverses is exploited. This paper presents a quasi-inverse neural model, which works as a speed controller of an induction motor. The neural speed controller consists of two cascade feedforward neural networks subsystems. The first subsystem provides desired stator current components for control algorithm and the second subsystem provides corresponding voltage components for PWM converter. The availability of the proposed controller is verified through the MATLAB simulation. The effectiveness of the controller is demonstrated for different operating conditions of the drive system.

Keywords: Artificial neural network, control methods, non-linear dynamic system, induction motor, speed control

Abstract: The Tensor Product (TP) model transformation is a recently proposed technique for transforming given Linear Parameter Varying (LPV) state-space models into polytopic model form, namely, to parameter varying convex combination of Linear Time Invariant (LTI) systems. The main advantage of the TP model transformation is that it is executable in a few minutes and the Linear Matrix Inequality (LMI)-based control design frameworks can immediately be applied to the resulting polytopc models to yield controllers with tractable and guaranteed performance. Various applications of the TP model transformation-based design were studied via academic complex and benchmark problems, but no real experimental environment-based study was published. Thus, the main objective of this paper is to study how the TP model transformation performs in a real world problem and control setup. The laboratory concept for TP model-based controller design, simulation and real time running on an electromechanical system is presented. Development system for TP model-based controller with one hardware/software platform and target system with real-time hardware/ software support are connected in the unique system. Proposed system is based on microprocessor of personal computer (PC) for simulation and software development as well as for real-time control. Control algorithm, designed and simulated in MATLAB/SIMULINK environment, use graphically oriented software interface for real-time code generation. Some specific conflicting industrial tasks in real industrial crane application, such as fast load positioning control and load swing angle minimization, are considered and compared with other controller types.

Keywords: Parallel Distributed Compensation, Linear matrix inequalities, TP model transformation, Single Pendulum Gantry (SPG), position control, swing angle control

Abstract: Following the wide spread usage of Fuzzy Systems, Rule Reduction has emerged as one of the most important areas of research in the field of Fuzzy Control. Many rule reduction methods have been proposed in the literature and can be broadly classified into Lossless or Lossy with respect to the inference, based on whether the outputs of the original and the reduced rule bases are identical or not. In a typical Multi-Input-Single-Output fuzzy system the number of rules far exceeds the number of fuzzy sets defined on the output domain. This suggests that the rule base can be partitioned into sets of rules, each set being mapped to a single consequent fuzzy set. In this paper, we investigate the conditions on the inference operators employed in a fuzzy system that enable “lossless” merging of rules with identical consequents. After briefly surveying the many techniques that have been proposed towards reducing the number of rules, we propose a general framework for Inference in Fuzzy Systems and then propose some sufficiency conditions on this general framework that give us a class of Fuzzy Systems that allow lossless rule reduction of the type mentioned above. We then explore these conditions in the setting of Fuzzy Logic. We find that R- and S-implications play a very critical role. We give examples from the above class of Fuzzy Systems. In this study we apply the above technique only on rules whose antecedents and consequents are fuzzy sets.

Keywords: Fuzzy Systems, Rule Reduction, Residuated Implications, Strong Implications, Fuzzy Inference.