Advanced control designs for output tracking of hydrostatic transmissions

The work addresses simple but efficient model descriptions in a combination with advanced control and estimation approaches to achieve an accurate tracking of the desired trajectories. The proposed control designs are capable of fully exploiting the wide operation range of HSTs within the system configuration limits. A new trajectory planning scheme for the output tracking that uses both the primary and secondary control inputs was developed. Simple models or even purely data-driven models are envisaged and deployed to develop several advanced control approaches for HST systems

Modélisation et commande du moteur piézoélectrique à onde progressive

Piezoelectric motors are resonant vibromotors. They represent a new actuator generation in the field of servo-drives. In particular, the travelling wave ultrasonic motor presents a high torque at low speed, a zero speed torque without feeding, low sensitivity to electromagnetic disturbances as well as being a more compact solution if compared to conventional electromagnetic motors. Much researches has been performed by others to determine an analytical model based on the identification of an electromagnetic equivalent circuit or on exploitation of a theoretical model based on numerical approaches, which use finite elements methods. While leading to satisfactory analysis, these modeling methods can hardly be exploited in the design of control algorithms. Indeed, they require considerable processing resources to generate and visualize the results. For this reason, we introduce in this thesis, an analytical model that is easily adaptable to operational applications and control techniques. The proposed analytical model has been validated by comparing measured characteristics with those obtained in simulations, which was possible thanks to the realization of a modular test bench. The travelling wave ultrasonic motor is characterized by strong non-linearity. It also depends highly on the wear state of the materials, which is difficult to model, and on the contact surface between stator and rotor. In addition, the mechanical resonance frequency experiences drift due to the variations of temperature. These considerations of strong non-linearities and parameter sensitivities of the motor represent a challenge for the study and design of an efficient and robust control strategy. We introduce with this thesis a new control approach that guarantees a closed loop response which is independent of the motor operating point. Moreover, the proposed control method allows to avoid the discontinuities typically present with this type of actuator with a very reasonnable hardware requierments. Finally, an important extension in the product range of the piezoelectric actuators is proposed in the last part of this thesis. It acts to develop an fMRI (functional Magnetic Resonance Imaging) compatible haptic interface with one degree of freedom. The use of a robotic interface in conjunction with an fMRI environment would enable neuroscientists to investigate the brain mechanism used to perform tasks with arbitrary dynamics, and could become a critical tool in neuroscience and rehabilitaiton. There is, however, a major problem for robot working within an fMRI environment : conventional actuators and materials interfere with the strong permanent magnetic field and the fast switching magnetic field gradients. Consequently, non-ferromagnetic materials must be used to avoid forces on the device itself, that can compromise its performance and may result in hazardous conditions for the patient or the medical staff. In addition, the materials should be non-conducting to avoid the generation of eddy currents. The travelling wave ultrasonic motor was used because it provides benefits compared to the conventional electromagnetic actuators. Non-ferromagnetic piezoelectric ceramic material is used and as a result motor operation is not affected by the presence of the strong magnetic fields ecountered in the clinical scanners

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