Alexander Hošovský

Model-based Evolution of a Fast Hybrid Fuzzy Adaptive Controller for a Pneumatic Muscle Actuator

Pneumatic artificial muscle-based robotic systems usually necessitate the use of various nonlinear control techniques in order to improve their performance. Their robustness to parameter variation, which is generally difficult to predict, should also be tested. Here a fast hybrid adaptive control is proposed, where a conventional PD controller is placed into the feedforward branch and a fuzzy controller is placed into the adaptation branch. The fuzzy controller compensates for the actions of the PD controller under conditions of inertia moment variation. The fuzzy controller of Takagi-Sugeno type is evolved through a genetic algorithm using the dynamic model of a pneumatic muscle actuator. The results confirm the capability of the designed system to provide robust performance under the conditions of varying inertia.

Transfer Issues of Control Optimizing Combustion from Small-scale to Medium-scale Biomass-fired Boilers

Abstract The quick development of biomass fired boilers increased the attention being paid to the ecological and economical aspects of their operation. A very important role in the process of adapting to harder regulations has been the control technology replacing older simple solutions. Lately, we have been offered a very interesting opportunity to make use of our experience with small-scale boiler control which has been gradually equipped with instruments for more sophisticated experiments and to transfer verified control algorithms to a district heating boiler of medium-scale size. The following text describes what has been achieved in experiments with the small-scale boiler and how this can be transferred to bigger scale one. An algorithm to optimize the combustion process from the efficiency point of view, as well as harmful emission reduction is described in detail.

Experimental Validation of Nominal Model Characteristics for Pneumatic Muscle Actuator

Pneumatic artificial muscles belong to a category of nonconventional pneumatic actuators that are distinctive for their high power/weight ratio, simple construction and low price and maintenance costs. As such, pneumatic artificial muscles represent an alternative type of pneumatic actuator that could replace the traditional ones in certain applications. Due to their specific construction, PAM-based systems have nonlinear characteristics which make it more difficult to design a control system with good performance. In the paper, a gray-box model (basically analytical but with certain experimental parts) of the one degree-of-freedom PAM-based actuator is derived. This model interconnects the description of pneumatic and mechanical part of the system through a set of several nonlinear differential equations and its main purpose is the design of intelligent control system in simulation environment. The model is validated in both open-loop and closed-loop mode using the measurements on real plant and the results confirm that model performance is in good agreement with the performance of real actuator.

Biomass Combustion Control and Stabilization Using Low-Cost Sensors

The paper describes methods for biomass combustion process control and burning stabilization based on low-cost sensing of carbon monoxide emissions and oxygen concentration in the flue gas. The designed control system was tested on medium-scale biomass-fired boilers and some results are evaluated and presented in the paper.

Simulation of Hybrid Fuzzy Adaptive Control of Pneumatic Muscle Actuator

The pneumatic muscle actuator is highly nonlinear system and it is difficult to control it using only a linear controller with fixed gains. The hybrid fuzzy adaptive control scheme with reference model was designed to control such actuator. It uses a multiplicative signal adaptation with a linear controller in the feedforward and a fuzzy controller in the adaptive feedback loop. In the paper there are presented some simulation results of this control. The nonlinear dynamic model of one-DOF actuator based on the advanced geometric muscle model was used in simulation.

Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network

To make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use of analytical model makes it possible to retain the physical interpretation of the model and the model is validated using open-loop responses. Since it was considered important to design a robust controller based on this model, the effect of changed moment of inertia (as a representation of uncertain parameter) was taken into account and compared with nominal case. To improve the accuracy of the model, these effects are treated as a disturbance modeled using the recurrent (Elman) neural network. Recurrent neural network was preferred over feedforward type due to its better long-term prediction capabilities well suited for simulation use of the model. The results confirm that this method improves the model performance (tested for five of the measured variables: joint angle, muscle pressures, and muscle forces) while retaining its physical interpretation.

Wireless Device Based on MEMS Sensors and Bluetooth Low Energy (LE/Smart) Technology for Diagnostics of Mechatronic Systems

This paper deals with usability of MEMS sensors for diagnostics of mechatronics system state wirelessly. We can acquire basic kinematics and dynamics mechanism parameters (spatial position, speed, acceleration, vibration, angular rate, orientation, etc.) and some environment condition (local/remote temperature, humidity, pressure, electromagnetic noise) by MEMS sensors. Acquired data are sent to remote application in desktop computer. This system can replace expensive and separate diagnostic tools by small integrated solution with one wireless communication interface (with limitation of MEMS sensors precision). This solution can be battery powered with long operation time, because there is used new wireless technology based on Bluetooth 4 protocol (Low Energy/Smart Bluetooth). Some of integrated MEMS sensors measures same variable on different measuring principle. For example angle can be acquired from three different sensors: magnetometer, accelerometer or gyroscope. Combination of these sensor data can significantly improve value accuracy. The designed diagnostic tool can serve as an inertia measuring unit IMU or Wireless IMU (WIMU).

Diagnostics of Surface Errors by Embedded Vision System and its Classification by Machine Learning Algorithms

The Article deals with usability and advantages of embedded vision systems for surface error detection and usability of advanced algorithms, technics and methods from machine learning and artificial intelligence for error classification in machine vision systems. We provide experiments with following classification algorithms: Support Vector Machines (SVM), Random Threes, Gradient Boosted Threes, K-Nearest Neighbor and Normal Bayes Classifier. Next comparison experiment was conducted with multilayer perceptron (MLP), because currently it is very popular for classification in the field of artificial intelligence. These classification approaches are compared by precision, reliability, speed of teaching and algorithm implementation difficulty.

Comparison of Performance of Optimized Varela Immune Controller and PID Controller for Control of Time-Delay Process

Varela immune controller is a kind of nonlinear controller, which is said to have good anti-delay capabilities. We compare the performance of simulated annealing optimized improved Varela immune controller and optimized PID controller for controlling a process with very long time delay (approximation of biomass-fired boiler temperature control). The results confirm that Varela immune controller is indeed capable of stabilizing the process while being very robust even to extreme changes in process parameters (time constant and time delay). In addition to that, it is also found out that properly (optimally) tuned PID controller is capable of achieving similar performance. The problem of controller tuning is relevant for both controllers but there are no tuning rules for immune controllers, which might favor the use of conventional PID controller. On the other hand, Varela controller has greater flexibility due to its more complex structure, which might help to adapt it to some special kinds of processes.