Control of flexible joint robotic manipulator using tuning functions design

The goal of this thesis is to design the controller for a single arm manipulator having a flexible joint for the tracking problem in two different cases. A controller is designed for a deterministic case wherein the plant parameters are assumed to be known while another is designed for an adaptive case where all the plant parameters are assumed to be unknown. In general the tracking problem is; given a smooth reference trajectory, the end effector has to track the reference while maintaining the stability. It is assumed that only the output of the manipulator, which is the link angle, is available for measurement. Also without loss of generality, the fast dynamics, that is the dynamics of the driver side of the system are neglected for the sake of simplicity; In the first case, the design procedure adopted is called observer backstepping. Since the states of the system are unavailable for measurement, an observer is designed that estimates the system states. These estimates are fed to the controller which in turn produces the control input to the system; The second case employs a design procedure called tuning functions design. In this case, since the plant parameters are unknown, the observer designed in case one cannot be used for determining the state estimates. For this purpose, parameter update laws and filters are designed for estimation of plant parameters. The filters employed are k-filters. The k-filters and the parameter update laws are given as input to the controller, which generates the control input to the system; For both cases, the mathematical models are simulated using Matlab/Simulink, and the results are verified

Momentum Control of Humanoid Robots with Series Elastic Actuators

Humanoid robots may require a degree of compliance at the joint level for improving efficiency, shock tolerance, and safe interaction with humans. The presence of joint elasticity, however, complexifies the design of balancing and walking controllers. This paper proposes a control framework for extending momentum based controllers developed for stiff actuators to the case of series elastic actuators. The key point is to consider the motor velocities as an intermediate control input, and then apply high-gain control to stabilise the desired motor velocities achieving momentum control. Simulations carried out on a model of the robot iCub verify the soundness of the proposed approach

Stiffness control of pneumatic actuators to simulate human tissues behavior on medical haptic simulators

In order to increase the realism of medical simulators, haptic interfaces could be used to simulate the patient's body behavior. It is especially interesting to reproduce the stiffness of different soft tissues with corresponding haptic behaviors. In this paper, two control laws, impedance control and back-stepping associated with a closed-loop stiffness tuning, are introduced and applied to a pneumatic actuator. Both controllers have been obtained by using the A-T transform which is suitable to model the behavior of a pneumatic system, in a strict-feedback form. Both control laws allow to tune the system stiffness. A comparison of their performances is presented, based on experimental results

A Review of Pneumatic Actuators Used for the Design of Medical Simulators and Medical Tools

International audienc

Nonlinear position and stiffness Backstepping controller for a two Degrees of Freedom pneumatic robot

This paper presents an architecture of a 2 Degrees of Freedom pneumatic robot which can be used as a haptic interface. To improve the haptic rendering of this device, a nonlinear position and stiffness controller without force measurement based on a Backstepping synthesis is presented. Thus, the robot can follow a targeted trajectory in Cartesian position with a variable compliant behavior when disturbance forces are applied. An appropriate tuning methodology of the closed-loop stiffness and closed-loop damping of the robot is given to obtain a desired disturbance response. The models, the synthesis and the stability analysis of this controller are described in this paper. Two models are presented in this paper, the first one is an accurate simulation model which describes the mechanical behavior of the robot, the thermodynamics phenomena in the pneumatic actuators, and the servovalves characteristics. The second model is the model used to synthesize the controller. This control model is obtained by simplifying the simulation model to obtain a MIMO strict feedback form. Finally, some simulation and experimental results are given and the controller performances are discussed and compared with a classical linear impedance controller

On-line estimation of a stability metric including grip conditions and slope: Application to rollover prevention for all-terrain vehicles

International audienceRollover is the principal cause of serious accidents for All-Terrain Vehicles (ATV), especially for light vehicles (e.g.quad bikes). In order to reduce this risk, the development of active devices, contributes a promising solution. With this aim, this paper proposes an algorithm allowing to predict the rollover risk, by means of an on-line estimation of a stability criterion. Among several rollover indicators, the Lateral Load Transfer (LLT) has been chosen because its estimation needs only low cost sensing equipment compared to the price of a light ATV. An adapted backstepping observer associated to a bicycle model is first developed, allowing the estimation of the grip conditions. In addition, the lateral slope is estimated thanks to a classical Kalman filter relying on measured acceleration and roll rate. Then, an expression of the LLT is derived from a roll model taking into account the grip conditions and the slope. Finally, the LLT value is anticipated by means of a prediction algorithm. The capabilities of this system are investigated thanks to full scale experiments with a quad bike

Rollover prevention system dedicated to ATVs on natural ground

In this paper, an algorithm dedicated to light ATVs, which estimates and anticipates the rollover, is proposed. It is based on the on-line estimation of the Lateral Load Transfer (LLT), allowing the evaluation of dynamic instabilities. The LLT is computed thanks to a dynamical model split into two 2D projections. Relying on this representation and a low cost perception system, an observer is proposed to estimate on-line the terrain properties (grip conditions and slope), then allowing to deduce accurately the risk of instability. Associated to a predictive control algorithm, based on the extrapolation of riders action, the risk can be anticipated, enabling to warn the pilot and to consider the implementation of active actions

Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot.

A rehabilitation robot plays an important role in relieving the therapists' burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles' good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM's nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions

Application of Simultaneous Localization and Mapping Algorithms for Haptic Teleoperation of Aerial Vehicles

In this thesis, a new type of haptic teleoperator system for remote control of Unmanned Aerial Vehicles (UAVs) has been developed, where the Simultaneous Localization and Mapping (SLAM) algorithms are implemented for the purpose of generating the haptic feedback. Specifically, the haptic feedback is provided to the human operator through interaction with artificial potential field built around the obstacles in the virtual environment which is located at the master site of the teleoperator system. The obstacles in the virtual environment replicate essential features of the actual remote environment where the UAV executes its tasks. The state of the virtual environment is generated and updated in real time using Extended Kalman Filter SLAM algorithms based on measurements performed by the UAV in the actual remote environment. Two methods for building haptic feedback from SLAM algorithms have been developed. The basic SLAM-based haptic feedback algorithm uses fixed size potential field around the obstacles, while the robust SLAM-based haptic feedback algorithm changes the size of potential field around the obstacle depending on the amount of uncertainty in obstacle location, which is represented by the covariance estimate provided by EKF. Simulations and experimental results are presented that evaluate the performance of the proposed teleoperator system