This paper addresses the design of predictive networked controller with adaptation of a communication delay. The networked control system contains random delays from sensor to controller and from controller to actuator. The proposed predictive controller includes an adaptation loop which decreases the influence of communication delay on the control performance. Also, the predictive controller contains a filter which improves the robustness of the control system. The performance of the proposed adaptive predictive controller is demonstrated by simulation results in comparison with PI controller and predictive controller with constant delay.

In this paper the neural network-based controller is designed for motion control of a mobile robot. This paper treats the problems of trajectory following and posture stabilization of the mobile robot with nonholonomic constraints. For this purpose the recurrent neural network with one hidden layer is used. It learns relationship between linear velocities and error positions of the mobile robot. This neural network is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control inputs. The performance of the proposed system is investigated using a kinematic model of the mobile robot. Keywords—Mobile robot, kinematic model, neural network, motion control, adaptive learning rate.

The problem of motion planning and control of mobile robots has attracted the interest of researchers in view of its theoretical challenges because of their obvious relevance in applications. From a control viewpoint, the peculiar nature of nonholonomic kinematics and dynamic complexity of the mobile robot makes that feedback stabilization at a given posture cannot be achieved via smooth time-invariant control (Oriolo et al., 2002). This indicates that the problem is truly nonlinear; linear control is ineffective, and innovative design techniques are needed. In recent years, a lot of interest has been devoted to the stabilization and tracking of mobile robots. In the field of mobile robotics, it is an accepted practice to work with dynamical models to obtain stable motion control laws for trajectory following or goal reaching (Fierro & Lewis, 1997). In the case of control of a dynamic model of mobile robots authors usually used linear and angular velocities of the robot (Fierro & Lewis, 1997; Fukao et al., 2000) or torques (Rajagopalan & Barakat , 1997; Topalov et al., 1998) as an input control vector. The central problem in this paper is reduction of control torques during the reference position tracking. In the case of dynamic mobile robot model, the position control law ought to be nonlinear in order to ensure the stability of the error that is its convergence to zero (Oriollo et al., 2002). The most authors solved the problem of mobile robot stability using nonlinear backstepping algorithm (Tanner & Kyriakopoulos, 2003) with constant parameters (Fierro & Lewis, 1997), or with the known functions (Oriollo et al., 2002). In (Tanner & Kyriakopoulos, 2003) a combined kinematic/torque controller law is developed using backstepping algorithm and stability is guaranteed by Lyapunov theory. In (Oriollo et al., 2002) method for solving trajectory tracking as well as posture stabilization problems, based on the unifying framework of dynamic feedback linearization was presented. The objective of this chapter is to present advanced nonlinear control methods for solving trajectory tracking as well as convergence of stability conditions. For these purposes we developed a backstepping (Velagic et al., 2006) and fuzzy logic position controllers (Lacevic, et al., 2007). It is important to note that optimal parameters of both controllers are adjusted using genetic algorithms. The novelty of this evolutionary approach lies in automatic obtaining of suboptimal set of control parameters which differs from standard manual adjustment presented in (Hu & Yang, 2001; Oriolo et al., 2002). The considered motion control system of the mobile robot has two levels. The lower level subsystem deals with the

This paper develops a fuzzy logic position controller which membership functions are tuned by genetic algorithm. The main goals are to ensure both successfully velocity and position trajectories tracking between the mobile robot and the reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance between the mobile robot and the reference cart. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. Outputs represent linear and angular velocity inputs, respectively. The performance of the fuzzy controller is investigated through comparison with previously developed a mobile robot position controller based on backstepping control algorithm. Simulation results obtained the good quality of both position tracking and torque capabilities with the proposed fuzzy controller. Also, sufficient improvement of torques reduction is achieved in the case of fuzzy controller.

The teleoperation (telerobotic) systems often face two key challenges: the existence of communication delays between the master and slave site as well as the addition of force feedback to improve the user's sense of presence. The first goal of this paper is that the slave manipulator should track the position of the master manipulator and the second goal is that the environmental force acting on the slave, when it contacts a remote environment, be accurately transmitted to the master. For solving both problems we proposed the symmetric impedance matched teleoperation systems with a wave filter in feedback loop. Simulations results using a single-degree of freedom master/slave system are presented showing the performance of the resulting system.

This paper proposes a new reactive planning algorithm for mobile robot navigation in unknown environments. The overall navigation system consists of three navigation subsystems. The lower level subsystem deals with the control of the linear and angular velocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is in the medium level, and it is a nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. The high level subsystem uses the Fuzzy logic and Dempster-Shafer evidence theory to design the fusion of sensor data, map building and path planning tasks. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. A particular attention is paid to detection of the robot's trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.

In this paper we proposed a new stable control algorithm for mobile robot trajectory tracking. The stability conditions are guaranteed by Lyapunov theory. The control parameters of backstepping algorithm are adjusted using genetic algorithm. Some of them are represented by unknown functions which are generated by neural network. The performance of the proposed controller is investigated using a kinematic model of a nonholonomic mobile robot. The efficient position tracking performance was obtained but the velocities were very high at the start of the motion. In order to avoid this, we proposed the extension of backstepping position controller by adding a new control law, which provided lower velocity servo inputs. Simulation results show the good quality of both velocity and position tracking capabilities of a mobile robot.

The goal of this paper is to develop an approach to increase the mobile robot navigation speed in an a priori unknown environment. The overall navigation system consists of three navigation subsystems. The lower level subsystem deals with the control of the linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot represents the medium level control, which is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. The high level subsystem uses Dempster-Shafer evidence theory to design the fusion of sensor data and map building processes. Path planning algorithm is based on the modificated potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. The performance of the proposed system is investigated using a dynamic model of a mobile robot with various frictions.

We have previously developed a mobile robot position controller based on backstepping control algorithm. In this paper, we propose the extension of mentioned controller with an aim to rapidly decrease the control torques needed to achieve the desired position and orientation of mobile robot. The parameters of this controller are adjusted by genetic algorithm. The same genetic algorithm was used for evolution of the control parameters of a multivariable PI velocity controller described with a full matrix. The performance of the proposed system is investigated using a dynamic model of a nonholonomic mobile robot with friction. Simulation results show the good quality of both velocity and position tracking capabilities of a mobile robot

We havepreviously developed amobile robot position controller basedonbackstepping control algorithm. In thispaper,we proposetheextention ofmentioned controller withanaimtorapidly decrease thecontrol torques neededtoachive thedesired position andorientation of mobile robot. Theparameters ofthis controller areadjusted bygenetic algorithm. Thesamegenetic algorithm wasused forevolution ofthecontrol parameters ofamultivariable PI velocity controller described witha fullmatrix. The performance oftheproposed systemisinvestigated using a dynamic modelofanonholonomic mobile robot withfriction. Simulation results showthegoodquality ofbothvelocity and position tracking capabilities ofamobile robot. 1~~~~~~. EUROCON2005

Thispaperproposes theusageofthefuzzy logic forbothposition control andstabilization ofcurrent pulsations. Theaimistoensureprecise control ofthejoint motionposition withveryquickly joints movements. The blockdiagramofa control systeminvolves controller, actuator, robotandappropriate sensors. Eachofthese componentsis described. The dynamicmodelof a manipulator isbasedontheNewton-Euler formulation that provides adescription oftherelationship between thejoint actuator torques andthemotionofthestructure. Thefuzzy logic controller Mamdanitypecontains twocontrol inputs: position errorand armaturecurrent.Inputvariable fuzzification, inference basedonfuzzy rules andoutputset defuzzification are described. The same fuzzycontrol algorithm isusedinalljoint servoloops, requiring only multiplexing andproperscaling ofthefuzzy controller inputs andoutputs. Theelectric dc(direct-drive) servomotors used foractuating thejoints ofa manipulator. Theinfluence of nonlinear loads, gravitation-dependent load, viscous friction torque, Coulomb's friction andtorqueduebyreducer on position tracking performance wasalsoconsidered.

Abstract In this paper, we propose two level control system for a mobile robot. The first level subsystem deals with the control of the linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot represents the second level control, which is nonlinear. The nonlinear control design is implemented by a modified backstepping algorithm whose parameters are adjusted by a genetic algorithm, which is a robust nonlinear optimization method. The performance of the proposed system is investigated using a dynamic model of a nonholonomic mobile robot with friction. We present a new dynamic model in which the angular velocities of wheels are main variables. Simulation results show the good quality of position tracking capabilities a mobile robot with the various viscous friction torques.

Abstract This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system. The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control input. The results confirm the effectiveness of the proposed neural network based identification scheme and control architecture.

This work is considering three significant factors that affect blood glucose level: food intake, hereditary predisposition and stress. Goal of this paper is to observe blood sugar level in human organism as a dynamic MISO (Multi Input, Single Output) system, and to describe it with differential equations and control system blocks. The system has three inputs; food (carbohydrates), hereditary factor and stress, and a single output--blood glucose level. Basically, several logical assumptions have been made, as the result of few medical researches. A model that gives outputs, very similar to real ones (measurements of glucose level in human body) is used for more detailed analysis. This model is very suitable for computer simulations and it can easily be tested for different input arrangements. Using this property of the system, several modes of food consumption have been proposed, in order to retain blood sugar level inside recommended limits.