Marvin K. Bugeja

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This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function(More)
This paper presents a novel dual adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed entirely in discrete-time and the and the robot's nonlinear dynamic functions are assumed to be unknown. A Gaussian radial basis function neural network is employed for function approximation, and its(More)
This paper presents experimental results acquired from the implementation of an adaptive control scheme for nonholonomic mobile robots, which was recently proposed by the same authors and tested only by simulations. The control system comprises a trajectory tracking kinematic controller, which generates the reference wheel velocities, and a cascade dynamic(More)
Sigmoidal multilayer perceptron neural networks are proposed to effect functional adaptive control for handling the trajectory tracking problem in a nonholonomic wheeled mobile robot. The scheme is developed in discrete time and the multilayer perceptron neural networks are used for the estimation of the robot’s nonlinear kinematic functions, which are(More)
This paper proposes and investigates the application of sliding mode control to the ball and plate problem. The nonlinear properties of the ball and plate control system are first presented. Then the experimental setup designed and built specifically for the purpose of this research is discussed. The paper then focuses on the implementation and thorough(More)
This paper describes the design of a novel nonlinear kinematic controller which allows a wheeled mobile robot to track a moving target at a given separation distance. The Double Exponential Smoothing algorithm is employed to deal with uncertainties in the measurements and to acquire a predictive estimate for the robot's relative position. This estimate is(More)
The paper proposes a multilayer perceptron neural network controller for dual adaptive control of a class of stochastic MIMO nonlinear systems subject to functional uncertainty. The neural network parameters are adjusted in real-time using the Unscented Kalman filter algorithm and no pre-operational training phase is required. Dual adaptive control aims to(More)
Literature reviews on Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Other reviews only consider works that aim to address a specific problem or one particular application(More)