This study addresses the problem of adaptive controllingof both a nonredundant and a redundant robotic manipulator with state-dependent constraints. The task of the robot is to follow a prescribed ...geometric path given in the taskspace, by the end-effector. The aforementioned robot task has been solved on the basis of the Lyapunov stability theory, which is used to derive the control scheme. A new adaptive Jacobian controller is proposed in the paper for the path following of the robot, withboth uncertain kinematics and dynamics. The numerical simulation results carried outfor a planar redundant three-DOF (three degrees of freedom) manipulator whose end-effector follows a prescribed geometric path given in a two-dimensional (2D) task space, illustrate the trajectory performance of the proposed control scheme.
This paper presents the solution at the control feedback level to the inverse kinematics problem for mobile manipulators operating in both obstacle-free task spaces and task spaces including ...obstacles. Using the Frechet differential of a certain criterion function, the fully specified system of algebraic and differential equations of the minimal amount has been obtained to solve the inverse kinematics problem. Based on the Lyapunov stability theory, a full differential form generating the mobile manipulator trajectory, whose attractor attained in a finite time fulfills the above system of algebraic and differential equations, has been derived. The problem of both singularity and collision avoidance is solved here based on a concept of (local) velocity perturbation which results in continuous mobile manipulator velocities near singularities and obstacles. The numerical simulation results carried out for a mobile manipulator consisting of a nonholonomic wheel and a holonomic manipulator of two revolute kinematic pairs, operating in both an obstacle-free task space and task space including obstacles, illustrate the trajectory performance of the proposed solution scheme.
This paper addresses the optimal kinematic control problem of the mobile manipulators. A computationally simple class of the Jacobian transpose control algorithms is proposed for the end-effector ...trajectory tracking. These controllers use a new non-singular Terminal Sliding Mode (TSM) manifold, as being a non-linear integral mapping of the second order with respect to the task space tracking error. Based on the Lyapunov stability theory, Jacobian transpose control schemes proposed are shown to be finite-time stable provided that some reasonable assumptions are fulfilled during the mobile manipulator movement. The performance of the proposed control strategies is illustrated through computer simulations for a planar mobile manipulator which accomplishes trajectory tracking by the end-effector in a two-dimensional task space and simultaneously minimises the Euclidean instantaneous distance between initial configuration and the current one.
In the present work, a new task space nonsingular terminal sliding mode (TSM) manifold defined by non-linear integral equation of the first order with respect to the task tracking error and a kind of ...computed torque method are introduced to control four mecanum wheeled mobile robot (FMWMR). On account of the full rank of the Jacobian matrix of the omni-directional holonomic mechanism, the proposed control scheme is shown to be globally finite-time stable despite uncertain dynamic equations and (globally) unbounded disturbances acting on the FMWMR. Moreover, the proposed control law provides (locally) optimal solution. The numerical simulations carried out for a youBot platform with four mecanum wheels illustrate both the performance of the proposed control scheme and simultaneously its minimizing property for some practically useful objective function.
This study addresses the problem of controlling a redundant manipulator with both state and control dependent constraints. The task of the robot is to follow by the end-effector a prescribed ...geometric path given in the task space. The control constraints resulting from the physical abilities of robot actuators are also taken into account during the robot movement. Provided that a solution to the aforementioned robot task exists, the Lyapunov stability theory is used to derive the control scheme. The numerical simulation results, carried out for a planar manipulator whose end-effector follows a prescribed geometric path given in a task space, illustrate the trajectory performance of the proposed control scheme.
In this study a generalised dynamic neural network (GDNN) was designed to process gait analysis parameters to evaluate equinus deformity in ambulatory children with cerebral palsy. The aim was to ...differentiate dynamic calf muscle tightness from fixed muscle contracture. Patients underwent clinical examination and had instrumented gait analysis before evaluating their equinus under anaesthesia and muscle relaxation at the time of surgery to improve gait. The performance of the clinical examination, the subjective interpretation of gait analysis results, and the application of the neural network to assess ankle function were compared to the examination under anaesthesia. Evaluation of equinus by a Neural Network showed high sensitivity and specificity values with a likelihood ratio of +14.63. The results indicate that dynamic calf muscle tightness can be differentiated from fixed calf muscle contracture with considerable precision that might facilitate clinical decision-making.
This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's ...maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is demonstrated by three different real-world examples taken from the fields of anesthesiology, orthopedics, and radiology
This work addresses the problem of improving the generalization capabilities of continuous recurrent neural networks. The learning task is transformed into an optimal control framework in which the ...weights and the initial network state are treated as unknown controls. A new learning algorithm based on a variational formulation of Pontrayagin's maximum principle is proposed. Under reasonable assumptions, its convergence is discussed. Numerical examples are given that demonstrate an essential improvement of generalization capabilities after the learning process of a dynamic network.
