Search

This paper considers motion control for a cooperative system of automated passenger vehicles. It develops a cooperative scheme based on a decentralized planning algorithm which considers the vehicles in an initial open chain configuration. In this scheme the trajectories are intersections-free, and each trajectory is planned independently of the others. To ensure the stabilization of each vehicle in the planned trajectory, a fuzzy closed loop motion control is presented, where, based on the properties of the Fuzzy maps, the Lyapunov’s stability of the motion errors is demonstrated for all the vehicles. Based on the ISO 2631-1 standard, the saturation property of the Fuzzy maps guarantees low values of longitudinal and lateral accelerations, to ensure comfort of the human body during the motion. The validity of this control algorithm is supported by simulation experiments.

In this paper a novel algorithm with a dynamic fuzzy controller applied to the control of trajectory of vehicles with two independent wheels is proposed. An automatic control of trajectory of a vehicle can behave in a not efficient way. It is necessary to consider the friction of the actuators and possible perturbations coming from the outside environment, as for instance the variable characteristics of the ground where the vehicle moves. These perturbations, which depend also on the contact between the wheel and the ground, involve violations of nonholonomic constraints. Thus it is necessary to compensate for these perturbations to obtain a robust control system. The controller synthetized in this work is able to obtain a term of additive adaptation to the dynamic control law by means of a fuzzy inference mechanism. Asymptotic stability of equilibrium state of fuzzy control system is developed by the Direct Lyapunov method and Barbalat's lemma. A series of simulations in Matlab 6.5 confirms the validity of the algorithm.

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies.

A new hierarchical closed loop fuzzy control system for horizontal plane trajectory tracking of underactuated Autonomous Underwater Vehicles (AUV) is presented. A model for the AUV is formulated introducing a polar coordinates transformation for the velocities in the body fixed frame. It is employed to control the unactuated sway direction, the longitudinal position and the yaw by using the surge force and the yaw torque only. The highest level control is developed by employing a fuzzy inference system for obtaining the guidance control laws. The properties of the fuzzy system ensure forward surge velocity, fast convergence and Lyapunov's stability of the motion errors. A new low level kinetic controller is presented for ensuring that the actual velocities of the AUV converge to the fuzzy guidance control laws. The control algorithm efficiency is confirmed through simulation experiments in a Matlab environment.

A new closed loop fuzzy motion control system including on-line Kalman's filter (KF) for the two dimensional motion of underactuated and underwater Remotely Operated Vehicle (ROV) is presented. Since the sway force is unactuated, new continuous and discrete time models are developed using a polar transformation. A new hierarchical control architecture is developed, where the high level fuzzy guidance controller generates the surge speed and the yaw rate needed to achieve the objective of planar motion, while the low level controller gives the thruster surge force and the yaw torque control signals. The Fuzzy controller ensures robustness with respect to uncertainties due to the marine environment, forward surge speed and saturation of the control signals. Also Lyapunov's stability of the motion errors is proved based on the properties of the fuzzy maps. If Inertial Measurement Unit data (IMU) is employed for the feedback directly, aleatory noises due to accelerometers and gyros damage the performances of the motion control. These noises denote a kind of non parametric uncertainty which perturbs the model of the ROV. Therefore a KF is inserted in the feedback of the control system to compensate for the above uncertainties and estimate the feedback signals with more precision.

In this paper a new Fuzzy/Kalman navigation system for Unmanned Aerial Vehicles (UAV) is presented. A closed loop velocity Fuzzy navigation system is proposed for stabilizing the UAV in a reference trajectory generated dynamically and for obtaining a forward velocity command. The Kalman's filter (KF) is included in the feedback line of the fuzzy control system to filter the internal noise of the sensors and to evaluate the external noise due to possible perturbations of the nominal motion. The efficiency of the navigation system has been shown through experimental tests in a Matlab environment.

This paper considers a complete position and heading rate control system for multiple unmanned aerial vehicles (UAVs) with constant altitude. A decentralized trajectory planning algorithm is proposed, where the UAVs will avoid collisions while moving. In order to stabilize the UAVs in the reference planned trajectories and ensure the boundedness of the control velocities, a fuzzy control law is proposed with Lyapunov's stability proof. Simulation experiments developed in Matlab environment confirm the effectiveness and the robustness of the proposed control algorithm with respect to possible turbulence disturbances perturbing the nominal motion of the UAVs.

In this paper a fuzzy motion control for cooperative passenger automated vehicles where there are not collisions between the closest ones is proposed. Based on the position of the target and on the initial position of each cooperative vehicle, a supervisory plans nonholonomic circular trajectories which are without intersections, while a fuzzy control strategy assures the asymptotical stability of the motion errors and the reaching of the target with low acceleration values along the planned trajectories. Based on the ISO 2631-1 standard, the saturation properties of the fuzzy maps guarantees low values of the longitudinal and lateral accelerations to assure the comfort of the passengers. The effectiveness of the approach is investigated by means of simulation experiments.

In this paper a problem of intelligent cooperative motion control of two wheeled nonholonomic cars (target and follower) is considered. Once a target car converges to a fixed state (position and orientation), a follower car coming from different position and orientation, converges to the state above, without excessive delay between the known arrival time of the target car and the arrival time of the follower. In this sense we present a new predictive fuzzy control system. A Kalman's filter and an odometric model are used to predict the future position and orientation of the target car. The prediction above is employed to plane a circular nonholonomic reference motion for the follower car. A fuzzy closed loop motion control for the follower, where the asymptotical stability of the motion errors is based on the properties of the Fuzzy maps, assures the stabilization of the follower car in the circular planned motion and the reaching of the target car with good dynamical performances.

A new fuzzy adaptive motion control system including on-line extended Kalman''s filter (EKF) for wheeled underactuated cars with non-holonomic constraints on the motion is presented. The presence of parametric uncertainties in the kinematics and in the dynamics is treated using suitable differential adaptation laws. We merge adaptive control with fuzzy inference system. By using fuzzy system, the parameters of the kinematical controller are functions of the lateral, longitudinal and orientation errors of the motion. In this way we have a robust control system where the dynamics of the motion errors is with lower time response than the adaptive control without fuzzy. Also Lyapunov''s stability of the motion errors is proved based on the properties of the fuzzy maps. If data from incremental encoders are employed for the feedback directly, sensor noises can damage the performance of the motion control in terms of the motion errors and of the parametric adaptation. These noises are aleatory and denote a kind of non-parametric uncertainties which perturb the nominal model of the car. Therefore an EKF is inserted in the adaptive control system to compensate for the above non-parametric uncertainties. The control algorithm efficiency is confirmed through simulation tests in Matlab environment.

In this paper a new Fuzzy extended Kalman robust control system for position and orientation tracking of nonholonomic vehicles with two wheels actuated by two independent DC motors is presented. The problem of robustness and localization are solved simultaneously. About the robustness, some perturbations coming from the outside environment and depending on the contact between the wheels and the ground, involve violations of the nonholonomic constraints. The fuzzy controller of this work is able to obtain a dynamic term of robustness with respect to the perturbations above. However, by using encoders only, the measures of actual position and orientation of the vehicle are with Gaussian noises. Therefore, before the feedback, we use a discrete time Extended Kalman Filter (EKF), to obtain on-line estimates of the filtered state from the observations of the noised outputs provided from more odometric sensors. Simulations with Matlab 7.0 software confirm the goodness of the proposed control system.

Remote control involves several issues that degrade seriously the performance of the plant to be controlled. This paper presents a strategy improving the characteristics of the remote control system, using an on-line adaptive neural net, in order to learn the variations of the remote system parameters to minimize the errors. This strategy is successfully applied to a client-server remote control system for a two link robot arm. Tests show that an error position in a remote control brushless motor can be highly reduced since its first "reference command" using a prevision of that error to modify the original reference. The neural net, used only by the client, is previously trained using local test data and then it is trained using on-line feedback data front the remote plant.

This paper presents a new statistical method based on Cluster Last Principal Component (CLPC) algorithm to identify nonlinear, time-varying, dynamical models from input-output data clusters of black boxes. Each of data clusters is on a time window. For every data cluster an appraiser updates the parameters of a Gaussian time-varying model via an optimality design criterion that maximises the Likelihood function and the estimated steady-state parameters of this model are quasi-constant values. An application to identify the nonlinear model of a control system of a brushless motor is developed. By applying of CLPC algorithm to this system, the actual angular positions of the brushless motor and the control torque have been estimated. Numerical tests of simulation in Matlab envinronment demonstrate the effectiveness of the proposed algorithm.

This paper considers the motion control problem of ground vehicles with nonholonomic constraints and parametric uncertainties both in the kinematic and in the dynamic model. The presence of uncertainties above is treated using adaptation laws where the Lyapunov's stability of the position and orientation errors is proved. Now, if the feedback signals are position and orientation provided by incremental encoders only, then noises of the odometric sensors above can damage the control in terms of difference between the desired and the actual motion of the vehicle and in terms of performances of the parametric adaptation. So an extended Kalman's filter (EKF) is inserted in the feedback for measuring and reducing the odometric noises above. Based on fusion of data provided by multiple proprioceptive sensors (i.e. incremental encoders, vector compass and position sensor), the EKF estimates the state of the vehicle, i.e. position and orientation, to obtain a filtered feedback signal. The adaptive control and the on-line EKF lead to the hybrid adaptive/EKF control of this paper. The control algorithm efficiency is confirmed through simulation experiments.

In this paper a new kinematic and dynamic adaptive Fuzzy control is applied to a trajectory tracking problem of a constrained vehicle with two indipendent wheels. The vehicle dynamics and kinematics are completely unknown. A dynamical and kinematical adaptive control provide to on line estimation of the dynamic and kinematic parameters of the vehicle model. Moreover the parameters of the kinematic control law are obtained using a Fuzzy controller and they are time varying and dependent on tracking errors. The stabililty of the kinematic and dynamic adaptive Fuzzy control system and the convergence of tracking errors to zero are proved using Lyapunov's method and Barbalat's Lemma. The effectiveness and efficiency of the proposed controller are demonstrated through simulation studies

This paper solves a trajectory tracking control problem whit outside perturbations for wheeled nonholonomic vehicles using a Fuzzy Lyapunov method. Trough a symbiosis between classical backstepping techniques and fuzzy logic, the control system ensures a good robustness with respect to outside perturbations. Possible causes of perturbations are the characteristics of the ground and the contact between the wheels and the ground. These perturbations can interact with the vehicle, they are sources of uncertainty for the system model and can perturb the validity of the nonholonomic constraints. The convergence of the tracking errors to the origin and the asymptotic stability of the equilibrium state of the fuzzy kinematical control system are guaranteed by Lyapunov's method. The algorithm efficiency, error minimization and noises reject are confirmed through simulation examples in Matlab environment.

This paper presents a new statistical method based on a real-time Last Principal Component (LPC) algorithm to estimate single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) varying time dynamical models from input output data clusters of non stationary black boxes. Each of data clusters is on a time window. A real time estimation algorithm via statistical LPC algorithm and an appraiser called "finite state machine" is then described. For every data cluster the finite state machine updates the parameters of a Gaussian varying time model via an optimality design criterion that maximises the Likelihood function. Using the LPC algorithm and the finite state machine, the estimated steady-state parameters of the model are constant values. An application to estimate the control system model of an industrial manipulator is developed. In fact many mathematical models of physical systems are non stationary such as industrial manipulator model. By applying of the real time LPC algorithm to control system of a two links planar industrial manipulator, the actual positions of the joints and the control torque have been estimated and numerical tests of simulation in Matlab 6.5 envinronment demonstrate the effectiveness of this algorithm.

This paper presents a new stochastical real-time LPC (Last Principal Component) algorithm to estimate single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) varying time models from input output data clusters of non stationary black boxes. Each of data clusters is on a time window. An application to estimate the control system model of a planar manipulator is developed. In fact many mathematical models of physical systems are non stationary such as industrial manipulator model. A real time estimation algorithm via stochastical LPC algorithm and an appraiser called "finite state machine" is then described For every data cluster the finite state machine updates the parameters of a Gaussian varying time model via an optimality design criterion that maximises the Likelihood function. The estimated steady-state parameters are constant values. By applying to two links planar manipulator, numerical tests of simulation in Matlab 6.5 demonstrate the effectiveness of this algorithm.