Cooperative Control for Multiple Autonomous Vehicles Using Descriptor Functions

The paper presents a novel methodology for the control management of a swarm of autonomous vehicles. The vehicles, or agents, may have different skills, and be employed for different missions. The methodology is based on the definition of descriptor functions that model the capabilities of the single agent and each task or mission. The swarm motion is controlled by minimizing a suitable norm of the error between agents’ descriptor functions and other descriptor functions which models the entire mission. The validity of the proposed technique is tested via numerical simulation, using different task assignment scenarios

Design of Fuzzy Optimized Controller for Satellite Attitude Control by Two State actuator to reduce Limit Cycle based on Takagi-Sugeno Method

In this paper, an algorithm was presented to control the satellite attitude in orbit in order to reduce the fuel consumption and increase longevity of satellite. Because of proper operation and simplicity, fuzzy controller was used to save fuel and analyze the uncertainty and nonlinearities of satellite control system. The presented control algorithm has a high level of reliability facing unwanted disturbances considering the satellite limitations. The controller was designed based on Takagi-Sugeno satellite dynamic model, a powerful tool for modeling nonlinear systems. Inherent chattering related to on-off controller produces limit cycles with low frequency amplitude. This increases the system error and maximizes the satellite fuel consumption. Particle Swarm Optimization (PSO) algorithm was used to minimize the system error. The satellite simulation results show the high performance of fuzzy on-off controller with the presented algorithm.DOI:http://dx.doi.org/10.11591/ijece.v4i3.583

Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory

In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance