Spyros G. Tzafestas

Incremental fuzzy expert PID control

An approach to intelligent PID (proportional integral derivative) control of industrial systems which is based on the application of fuzzy logic is presented. This approach assumes that one has available nominal controller parameter settings through some classical tuning technique (Ziegler-Nichols, Kalman, etc.). By using an appropriate fuzzy matrix (similar to Macvicar-Whelan matrix), it is possible to determine small changes on these values during the system operation, and these lead to improved performance of the transient and steady behavior of the closed-loop system. This is achieved at the expense of some small extra computational effort, which can be very easily undertaken by a microprocessor. Several experimental results illustrate the improvements achieved. >

The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs

The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging.

Feedback Control of an Omnidirectional Autonomous Platform for Mobile Service Robots

This paper proposes a feedback control scheme for an omnidirectional holonomic autonomous platform, which is equipped with three lateral orthogonal-wheel assemblies. Firstly, the dynamic properties of the platform are studied, and a dynamic model suitable for the application of control is derived. The control scheme constructed is of the resolved-acceleration type, with PI and PD feedback. The control scheme was experimentally applied to an actual mobile robotic platform. The results obtained show that full omnidirectionality can be achieved with decoupled rotational and translational motions. Omnidirectionality is one of the principal requirements for mobile robots designed for health-care and other general-hospital services.

Robust Sliding-mode Control Applied to a 5-Link Biped Robot

In this paper the application of robust control to a 5-link biped robotic model is investigated through the sliding mode approach, and compared to pure computed torque control. The biped consists of five links, namely the torso and two links in each leg. These links are connected via four (two hip and two knee) rotating joints which are considered to be friction-free and driven by independent d.c. motors. The locomotion of the biped is assumed to be constrained on the sagittal plane. The paper provides a full derivation of the biped dynamic model (single-leg support phase, biped-in-the-air phase) and an outline of the computed torque and sliding mode control algorithms. The simulation results were derived with two sets of parameters (one of which corresponds to a human-sized biped) and several degrees of parametric uncertainty (from 10% to 200%). In all cases the results obtained through the sliding mode control were much better than those obtained with the computed torque control. This superiority was shown to become stronger as the degree of uncertainty and the size of the biped increases.

Computational Intelligence Techniques for Short-Term Electric Load Forecasting

Electric load forecasting has received an increasing attention over the years by academic and industrial researchers and practitioners due to its major role for the effective and economic operation of power utilities. The aim of this paper is to provide a collective unified survey study on the application of computational intelligence (CI) model-free techniques to the short-term load forecasting of electric power plants. All four classes of CI methodologies, namely neural networks (NNs), fuzzy logic (FL), genetic algorithms (GAs) and chaos are addressed. The paper starts with some background material on model-based and knowledge-based forecasting methodologies revealing a number of key issues. Then, the pure NN-based and FL-based forecasting methodologies are presented in some detail. Next, the hybrid neurofuzzy forecasting methodology (ANFIS, GARIC and Fuzzy ART variations), and three other hybrid CI methodologies (KB-NN, Chaos-FL, Neurofuzzy-GA) are reviewed. The paper ends with eight representative case studies, which show the relative merits and performance that can be achieved by the various forecasting methodologies under a large repertory of geographic, weather and other peculiar conditions. An overall evaluation of the state-of-art of the field is provided in the conclusions.

NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model

NeuroFAST is an on-line fuzzy modeling learning algorithm, featuring high function approximation accuracy and fast convergence. It is based on a first-order Takagi-Sugeno-Kang (TSK) model, where the consequence part of each fuzzy rule is a linear equation. Structure identification is performed by a fuzzy adaptive resonance theory (ART)-like mechanism, assisted by fuzzy rule splitting and adding procedures. The well known delta rule continuously performs parameter identification on both premise and consequence parameters. Simulation results indicate the potential of the algorithm. It is worth noting that NeuroFAST achieves a remarkable performance in the Box and Jenkins gas furnace process, outperforming all previous approaches compared.

Robotics for Engineers

Robotics for engineers , Robotics for engineers , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Resolution of composite fuzzy relation equations based on Archimedean triangular norms

Lately, the sup-t-norm composition of fuzzy relations has been used instead of the well-known max–min. Thus, there is a need for methods of studying and solving sup-t-norm fuzzy relation equations (t is any t-norm). In this paper, the solution existence problem is first studied and solvability criteria for composite fuzzy relation equations of any t-norm are given. Then, a methodology for solving fuzzy relation equations based on sup-t composition, where t is an Archimedean t-norm, is proposed. This resolution method is simpler and faster than those proposed for covering all the continuous t-norms. The result is important, since, as is shown in the paper, the only continuous t-norm that is not Archimedean is the “minimum”.

Walsh series approach to lumped and distributed system identification

Abstract This paper considers the problem of identifying the parameters of dynamic systems from input-output records. Both lumped-parameter and distributed-parameter systems, deterministic and stochastic, are studied. The approach adopted is that of expanding the system variables in Walsh series. The key point is an operational matrix P which relates the coefficient matrix Г of the Walsh series of a given function with the coefficient matrix of its first derivative. Using this operational matrix P one overcomes the necessity to use differentiated data, a fact that usually is avoided either by integration of the data or by using discrete-time models. Actually, the original differential input-output model is converted to a linear algebraic (or regression) model convenient for a direct (or a least squares) solution. A feature of the method is that it permits the identification of unknown initial conditions simultaneously with the parameter identification. The results are first derived for single-input single-output systems and then are extended to multi-input multi-output systems. The case of non-constant parameters is treated by assuming polynomial forms. Some results are also included concerning the identification of state-space and integral equation models. The theory is supported by two examples, which give an idea of how effective the method is expected to be in the real practice.

Model-based predictive control for generalized production planning problems

Abstract This paper introduces and elucidates the concept of model-based predictive control (MBPC) used as a decision-making tool for handling complex integrated production planning problems within a stochastic environment. First, a functional overview of the MBPC methodology is provided, then the framework under which the MBPC approach is used as a decision-making tool is given. Simulation experiments have strongly indicated the flexibility and effectiveness of the proposed approach.