On-line multiobjective automatic control system generation by evolutionary algorithms

Evolutionary algorithms are applied to the on- line generation of servo-motor control systems. In this paper, the evolving population of controllers is evaluated at run-time via hardware in the loop, rather than on a simulated model. Disturbances are also introduced at run-time in order to pro- duce robust performance. Multiobjective optimisation of both PI and Fuzzy Logic controllers is considered. Finally an on-line implementation of Genetic Programming is presented based around the Simulink standard blockset. The on-line designed controllers are shown to be robust to both system noise and ex- ternal disturbances while still demonstrating excellent steady- state and dvnamic characteristics

Life buoy

A lifebuoy such as figure 1, or we can call as ring buoy, lifering, lifesaver, life donut, life preserver or lifebelt, also known as a "perry buoy", or "kisby ring". The "kisby ring", or sometimes will be call "Kisbie ring", is thought to be named after Thomas Kisbee (1792–1877) who was a British naval officer. Lifebuoy is a lifesaving buoy designed to save someone in the water. It also can provide buoyancy and prevent drowning. To improve aid rescue at night, mostly lifebuoys are fitted with one or more seawater-activated lights

Intelligent active force control of a three-link manipulator using fuzzy logic

The paper presents a novel approach to estimate the inertia matrix of a robot arm using a fuzzy logic (FL) mechanism in order to trigger the active force control (AFC) strategy. A comprehensive study is performed on a rigid three-link manipulator subjected to a number of external disturbances. The robustness and effectiveness of the proposed control scheme are investigated considering the trajectory track performance of the robotic arm taking into account the application of external disturbances and that the arm is commanded to describe a reference trajectory given a number of initial and operating conditions. The results show that the FL mechanism used in the study successfully computes appropriate estimated inertia matrix value to execute the control action. The proposed scheme exhibits a high degree of robustness and accuracy as the track error is bounded within an acceptable range of value even under the influence of the introduced disturbances

Fuzzy-PID Controller for Azimuth Position Control of Deep Space Antenna

The Deep Space Antennas are essential in achieving communication over very large distances. However, the pointing accuracy of this antenna needs to be as precise as possible to enable effective communication with the satellite. Therefore, this work addressed the pointing accuracy for a Deep Space Antenna using Fuzzy-PID control technique by improving the performance objectives (settling time, percentage overshoot rise time and mainly steady-state error) of the system. In this work, the PID controller for the system was first of all designed and simulated after which, a fuzzy controller was also designed and simulated using MATLAB and Simulink respectively for the sake of comparison with the fuzzy-PID controller. Then, the fuzzy-PID controller for the system was also designed and simulated using MATLAB and Simulink and it gives a better performance objective (rise time of 1.0057s, settling time of 1.6019s, percentage overshoot of 1.8013, and steady-state error of 2.195e-6) over the PID and fuzzy controllers respectively. Therefore, the steady state error shows improved pointing accuracy of 2.195e-6

Motion control and synchronisation of multi-axis drive systems

Motion control and synchronisation of multi-axis drive system

Controller Synthesis of Multi-Axial Robotic System Used for Wearable Devices

Wearable devices are commonly used in different fields to help improving performance of movements for different groups of users. The long-term goal of this study is to develop a low-cost assistive robotic device that allows patients to perform rehabilitation activities independently and reproduces natural movement to help stroke patients and elderly adults in their daily activities while moving their arms. In the past few decades, various types of wearable robotic devices have been developed to assist different physical movements. Among different types of actuators, the twisted-string actuation system is one of those that has advantages of light-weight, low cost, and great portability. In this study, a dual twisted-string actuator is used to drive the joints of the prototype assistive robotic device. To compensate the asynchronous movement caused by nonlinear factors, a hybrid controller that combines fuzzy logic rules and linear PID control algorithm was adopted to compensate for both tracking and synchronization of the two actuators.;In order to validate the performance of proposed controllers, the robotic device was driven by an xPC Target machine with additional embedded controllers for different data acquisition tasks. The controllers were fine tuned to eliminate the inaccuracy of tracking and synchronization caused by disturbance and asynchronous movements of both actuators. As a result, the synthesized controller can provide a high precision when tracking simple actual human movements

A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications

Neizrazito adaptivno upravljanje pogonom s asinkronim motorom

Industrial applications increasingly require electric drives with good position command tracking and load regulation responses. These conditions can only be achieved by adaptive-type control because of the loading conditions, inertias and system parameters all change during the motion. For this paper an Adaptive Speed Controller for AC drives with a very low computational algorithm was developed. The authors propose self-tuning control based on a supervisory fuzzy adaptation. The supervisor continuously monitors the status of the system and changes the Ki parameter of a standard PDF controller to adapt it to the plant’s evolution. The fuzzy logic adaptive strategy was readily implemented and showed very fast learning features and very good tracking and regulation characteristics. The stability of the controller developed was also analysed, and experimental results demonstrated the robustness of the suggested algorithm in contending with varying load and torque disturbance.Industrijske primjene sve više trebaju električne pogone s dobrim svojstvima pozicioniranja i regulacije tereta. To se može postići jedino adaptivnim načinom upravljanja, jer se uvjeti terećenja, momenti inercije kao i ostali parametri sustava mijenjaju tijekom gibanja. U članku je razvijen adaptivni regulator brzine vrtnje za izmjenični pogon s asinkronim motorom koji koristi jednostavan računski algoritam. Autori predlažu samopodešavajuće upravljanje zasnovano na neizrazitoj adaptaciji s nadzornog nivoa. Nadzorni algoritam neprestano prati stanje sustava i mijenja parametar Ki standardnog PDF regulatora da bi ga adaptirao na promjene stanja u postrojenju. Neizrazita adaptivna strategija realizirana je bez poteškoća, sa svojstvom vrlo brzog učenja te vrlo dobrim svojstvima pozicioniranja i regulacije tereta. Analiza stabilnosti razvijenog regulatora također je napravljena, a eksperimentalni rezultati pokazuju robustnost predloženog algoritma pri uvjetima djelovanja poremećaja u obliku promjenljivog momenta tereta