Synchronized closed-path following for a mobile robot and an Euler-Lagrange system

We propose and solve a synchronized path following problem for a differential drive robot modeled as a dynamic unicycle and an Euler-Lagrange system. Each system is assigned a simple closed curve in its output space. The outputs of systems must approach and traverse their assigned curves while synchronizing their motions along the paths. The synchronization problems we study in this thesis include velocity synchronization and position synchronization. Velocity synchronization aims to force the velocities of the systems be equal on the desired paths. Position synchronization entails enforcing a positional constraint between the systems modeled as a constraint function on the paths. After characterizing feasible positional constraints, a finite-time stabilizing control law is used to enforce the position constraint

Compliant control of Uni/ Multi- robotic arms with dynamical systems

Accomplishment of many interactive tasks hinges on the compliance of humans. Humans demonstrate an impressive capability of complying their behavior and more particularly their motions with the environment in everyday life. In humans, compliance emerges from different facets. For example, many daily activities involve reaching for grabbing tasks, where compliance appears in a form of coordination. Humans comply their handsâ motions with each other and with that of the object not only to establish a stable contact and to control the impact force but also to overcome sensorimotor imprecisions. Even though compliance has been studied from different aspects in humans, it is primarily related to impedance control in robotics. In this thesis, we leverage the properties of autonomous dynamical systems (DS) for immediate re-planning and introduce active complaint motion generators for controlling robots in three different scenarios, where compliance does not necessarily mean impedance and hence it is not directly related to control in the force/velocity domain. In the first part of the thesis, we propose an active compliant strategy for catching objects in flight, which is less sensitive to the timely control of the interception. The soft catching strategy consists in having the robot following the object for a short period of time. This leaves more time for the fingers to close on the object at the interception and offers more robustness than a âhardâ catching method in which the hand waits for the object at the chosen interception point. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction. Stability and convergence of the approach are assessed through Lyapunov stability theory. In the second part, we propose a unified compliant control architecture for coordinately reaching for grabbing a moving object by a multi-arm robotic system. Due to the complexity of the task and of the system, each arm complies not only with the objectâs motion but also with the motion of other arms, in both task and joint spaces. At the task-space level, we propose a unified dynamical system that endows the multi-arm system with both synchronous and asynchronous behaviors and with the capability of smoothly transitioning between the two modes. At the joint space level, the compliance between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under self-collision avoidance constraints; formulated as a quadratic programming problem (QP) and solved in real-time. In the last part, we propose a compliant dynamical system for stably transitioning from free motions to contacts. In this part, by modulating the robot's velocity in three regions, we show theoretically and empirically that the robot can (I) stably touch the contact surface (II) at a desired location, and (III) leave the surface or stop on the surface at a desired point

Neuro-fuzzy modelling and control of robotic manipulators

The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for robotic manipulators using Machine Learning Techniques, Fuzzy Logic Controllers, and Fuzzy Neural Networks. The main idea is to integrate these intelligent techniques to develop an adaptive position controller for robotic manipulators. This will finally lead to utilising one or two coordinated manipulators to perform upper-limb rehabilitation. The main target is to benefit from these intelligent techniques in a systematic way that leads to an efficient control and coordination system. The suggested control system possesses self-learning features so that it can maintain acceptable performance in the presence of uncertain loads. Simulation and modelling stages were performed using dynamical virtual reality programs to demonstrate the ideas of the control and coordination techniques. The first part of the thesis focuses on the development of neuro-fuzzy models that meet the above requirement of mimicking both kinematics and dynamics behaviour of the manipulator. For this purpose, an initial stage for data collection from the motion of the manipulator along random trajectories was performed. These data were then compacted with the help of inductive learning techniques into two sets of if-then rules that form approximation for both of the inverse kinematics and inverse dynamics of the manipulator. These rules were then used in fuzzy neural networks with differentiation characteristics to achieve online tuning of the network adjustable parameters. The second part of the thesis introduces the proposed adaptive neuro-fuzzy joint-based controller. To achieve this target, a feedback Fuzzy-Proportional-Integral-Derivative incremental controller was developed. This controller was then applied as a joint servo-controller for each robot link in addition to the main neuro-fuzzy feedforward controller used to compensate for the dynamics interactions between robot links. A feedback error learning scheme was applied to tune the feedforward neuro-fuzzy controller online using the error back-propagation algorithm. The third part of the thesis presents a neuro-fuzzy Cartesian internal model control system for robotic manipulators. The neuro-fuzzy inverse kinematics model of the manipulator was used in addition to the joint-based controller proposed and the forward mathematical model of the manipulator in an adaptive internal model controller structure. Feedback-error learning scheme was extended to tune both of the joint-based neuro-fuzzy controller and the neuro-fuzzy internal model controller online. The fourth part of the thesis suggests a simple fuzzy hysteresis coordination scheme for two position-controlled robot manipulators. The coordination scheme is based on maintaining certain kinematic relationships between the two manipulators using reference motion synchronisation without explicitly involving the hybrid position/force control or modifying the existing controller structure for either of the manipulators. The key to the success of the new method is to ensure that each manipulator is capable of tracking its own desired trajectory using its own position controller, while synchronizing its motion with the other manipulator motion so that the differential position error between the two manipulators is reduced to zero or kept within acceptable limits. A simplified test-bench emulating upper-limb rehabilitation was used to test the proposed coordination technique experimentally