Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

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

Fuzzy Control Strategy for Cooperative Non-holonomic Motion of Cybercars with Passengers Vibration Analysis

The cybercars are electric road wheeled non-holonomic vehicles with fully automated driving capabilities. They contribute to sustainable mobility and are employed as passenger vehicles. Non-holonomic mechanics describes the motion of the cybercar constrained by non-integrable constraints, i.e. constraints on the system velocities that do not arise from constraints on the configuration alone. First of all there are thus with dynamic nonholonomic constraints, i.e. constraints preserved by the basic Euler-Lagrange equations (Bloch, 2000; Melluso, 2007; Raimondi & Melluso, 2006-a). Of course, these constraints are not externally imposed on the system but rather are consequences of the equations of motion of the cybercar, and so it sometimes convenient to treat them as conservation laws rather than constraints per se. On the other hand, kinematic non-holonomic constraints are those imposed by kinematics, such as rolling constraints. The goal of the motion control of cybercars is to allow the automated vehicle to go from one terminal to another while staying on a defined trajectory and maintaining a set of performance criteria in terms of speeds, accelerations and jerks. There are many results concerning the issue of kinematic motion control for single car (Fierro & Lewis, 1997). The main idea behind the kinematic control algorithms is to define the velocity control inputs which stabilize the closed loop system. These works are based only on the steering kinematics and assume that there exists perfect velocity tracking, i.e. the control signal instantaneously affects the car velocities and this is not true. Other control researchers have target the problems of time varying trajectories tracking, regulating a single car to a desired position/orientation and incorporating the effects of the dynamical model to enhance the overall performance of the closed loop system. The works above are based on a backstepping approach, where the merging of kinematic and dynamic effects leads to the control torques applied to the motors of the wheels. A Fuzzy dynamic closed loop motion control for a single non-holonomic car based on backstepping approach and oriented to stability analysis of the motion errors has been developed by Raimondi & Melluso (2005). In Raimondi & Melluso (2006-b) and Raimondi & Melluso (2007-a) adaptive fuzzy motion control systems for single non-holonomic automated vehicles with unknown dynamic and kinematic parameters and Kalman's filter to localize the car have been presented. With regards to the problems of cooperative control of multiple cybercars, a number of techniques have been developed for omni-directiona

A New Fuzzy Robust Dynamic Fuzzy Controller for Autonomous Vehicle with Nonholonomic Constraints

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

Fuzzy EKF Control for Wheeled Nonholonomic Vehicles

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

Stochastical Real Time Finite State Machine LPC for Planar Manipulator Control System Model estimation

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

Predictive Intelligent Fuzzy Control for Cooperative Motion of Two Nonholonomic Wheeled Cars

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

Model Identification using a Statistical Cluster LPC approach with Application to Motion of a Brushless Motor

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

Fuzzy Adaptive EKF Motion Control for Nonholonomic and Underactuated Cars with Parametric and non Parametric Uncertainties

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