Fuzzy sliding mode control of a multi-DOF parallel robot in rehabilitation environment

Multi-degrees of freedom (DOF) parallel robot, due to its compact structure and high operation accuracy, is a promising candidate for medical rehabilitation devices. However, its controllability relating to the nonlinear characteristics challenges its interaction with human subjects during the rehabilitation process. In this paper, we investigated the control of a parallel robot system using fuzzy sliding mode control (FSMC) for constructing a simple controller in practical rehabilitation, where a fuzzy logic system was used as the additional compensator to the sliding mode controller (SMC) for performance enhancement and chattering elimination. The system stability is guaranteed by the Lyapunov stability theorem. Experiments were conducted on a lower limb rehabilitation robot, which was built based on kinematics and dynamics analysis of the 6-DOF Stewart platform. The experimental results showed that the position tracking precision of the proposed FSMC is sufficient in practical applications, while the velocity chattering had been effectively reduced in comparison with the conventional FSMC with parameters tuned by fuzzy systems

Streamlined sim-to-real transfer for deep-reinforcement learning in robotics locomotion

Legged robots possess superior mobility compared to other machines, yet designing controllers for them can be challenging. Classic control methods require engineers to distill their knowledge into controllers, which is time-consuming and limiting when approaching dynamic tasks in unknown environments. Conversely, learning- based methods that gather knowledge from data can potentially unlock the versatility of legged systems. In this thesis, we propose a novel approach called CPG-Actor, which incor- porates feedback into a fully differentiable Central Pattern Generator (CPG) formulation using neural networks and Deep-Reinforcement Learning (RL). This approach achieves approximately twenty times better training performance compared to previous methods and provides insights into the impact of training on the distribution of parameters in both the CPGs and MLP feedback network. Adopting Deep-RL to design controllers comes at the expense of gathering extensive data, typically done in simulation to reduce time. However, controllers trained with data collected in simulation often lose performance when deployed in the real world, referred to as the sim-to-real gap. To address this, we propose a new method called Extended Random Force Injection (ERFI), which randomizes only two parameters to allow for sim-to-real transfer of locomotion controllers. ERFI demonstrated high robustness when varying masses of the base, or attaching a manipulator arm to the robot during testing, and achieved competitive performance comparable to standard randomization techniques. Furthermore, we propose a new method called Roll-Drop to enhance the robustness of Deep-RL policies to observation noise. Roll-Drop introduces dropout during rollout, achieving an 80% success rate when tested with up to 25% noise injected in the observations. Finally, we adopted model-free controllers to enable omni-directional bipedal lo- comotion on point feet with a quadruped robot without any hardware modification or external support. Despite the limitations posed by the quadruped’s hardware, the study considers this a perfect benchmark task to assess the shortcomings of sim- to-real techniques and unlock future avenues for the legged robotics community. Overall, this thesis demonstrates the potential of learning-based methods to design dynamic and robust controllers for legged robots while limiting the effort needed for sim-to-real transfer

Performing heavy transfers for offshore wind maintenance

As offshore wind farms become larger and further from the shore, there are strong economic and climate incentives to perform transfers required for operations and maintenance from floating vessels, rather than employing expensive and slow jack up rigs. However, successful transfers of heavy and sensitive equipment from a floating vessel (in all but benign sea/wind conditions) are heavily dependent on multiple degrees of freedom, high performance control. This project aims to bring a novel modelling and simulation methodology in Simulink that could be used to assess offshore wind installation and maintenance procedures. More specifically, the goal is to demonstrate that a crane prototype assumed to be located on a floating ship can transfer loads of hundreds of tons onto a fixed platform. Furthermore, this process should be completed with good precision and minimal impact force during equipment loading onto the stand. This problem has not yet been answered in research, with the only relevant patent in the field being the Ampelmann platform, a motionless bridge allowing technicians to access the offshore turbine. The first main contribution to knowledge of this thesis was the design of a 90 m crane that could handle a 660 tons load. This thesis presents a procedure, based on both mechanical/hydraulics design as well as empirical findings, which could be re-used for scaling the crane model to a more realistic dimension. It is worth noting that the goal here was to assess whether a realistically weighing piece of equipment could be stably handled, while the actual size of the crane was deemed unimportant. Another missing gap in literature this project wanted to fill was achieving active motion compensation for a larger scale system such as the current one. This refers to balancing out the base motions on multiple axes, so the payload can be moved on a given trajectory unaffected by them. Currently, research in the field mainly consists of crane mechanisms that feature active heave compensation, which only refers to the vertical axis. Hence, two control design methods were employed to assess the viability of heavy payload positioning from floating vessels through the development of a simulation approach using Simulink. The crane prototype was designed and modelled to operate under simulated vessel motions given by sea states with a significant wave height of 5 m and maximum wave frequency of 1 rad/s. Then, traditional control (feedback and feedforward) was designed to achieve active motion compensation with steady-state position errors under 20 cm. A second controller architecture was then designed/implemented as a comparison basis for the first one, with the aim being to find the most robust solution of the two. The nonlinear generalised minimum variance (NGMV) control algorithm was chosen for control design in this application. Due to its ability to compensate for significant system nonlinearities and the ease of implementation, NGMV was a good candidate for the task at hand. Tuning controller parameters to stabilize the system could also be based on the previously determined traditional control solutions. An investigation of controllers’ robustness against model mismatch was carried out by introducing various levels of uncertainty which influence actuators’ natural frequency to assess system sensitivity. The outcome of the investigation determined that traditional and NGMV controllers provided comparable regulating performance in terms of reference tracking and disturbance rejection, for the nominal case. This confirmed the assertion that the PID-based NGMV weightings selection is a useful starting point for controller tuning. Increasing the mismatch between the nominal system based on which the controllers’ were designed and the actual plant showed that the traditional control was marginally more robust in this application. The final contribution to knowledge this thesis aimed to bring was minimising the impact force during load placement on a fixed and rigid platform. To that end, the contact forces between the payload and a platform were first successfully modelled and measured. A switching algorithm between position and force control was then developed based on a methodology found in literature but on a microscopic scale project. To execute smooth load placement, an automated hybrid force/position control scheme was implemented. The proposed algorithm enabled position control on x and y axes, while minimising impact forces on the z-axis. Unfortunately, preliminary findings showed that there is still work to be done to claim any success in this regard. However, the author hopes this offers a good starting point for future work.As offshore wind farms become larger and further from the shore, there are strong economic and climate incentives to perform transfers required for operations and maintenance from floating vessels, rather than employing expensive and slow jack up rigs. However, successful transfers of heavy and sensitive equipment from a floating vessel (in all but benign sea/wind conditions) are heavily dependent on multiple degrees of freedom, high performance control. This project aims to bring a novel modelling and simulation methodology in Simulink that could be used to assess offshore wind installation and maintenance procedures. More specifically, the goal is to demonstrate that a crane prototype assumed to be located on a floating ship can transfer loads of hundreds of tons onto a fixed platform. Furthermore, this process should be completed with good precision and minimal impact force during equipment loading onto the stand. This problem has not yet been answered in research, with the only relevant patent in the field being the Ampelmann platform, a motionless bridge allowing technicians to access the offshore turbine. The first main contribution to knowledge of this thesis was the design of a 90 m crane that could handle a 660 tons load. This thesis presents a procedure, based on both mechanical/hydraulics design as well as empirical findings, which could be re-used for scaling the crane model to a more realistic dimension. It is worth noting that the goal here was to assess whether a realistically weighing piece of equipment could be stably handled, while the actual size of the crane was deemed unimportant. Another missing gap in literature this project wanted to fill was achieving active motion compensation for a larger scale system such as the current one. This refers to balancing out the base motions on multiple axes, so the payload can be moved on a given trajectory unaffected by them. Currently, research in the field mainly consists of crane mechanisms that feature active heave compensation, which only refers to the vertical axis. Hence, two control design methods were employed to assess the viability of heavy payload positioning from floating vessels through the development of a simulation approach using Simulink. The crane prototype was designed and modelled to operate under simulated vessel motions given by sea states with a significant wave height of 5 m and maximum wave frequency of 1 rad/s. Then, traditional control (feedback and feedforward) was designed to achieve active motion compensation with steady-state position errors under 20 cm. A second controller architecture was then designed/implemented as a comparison basis for the first one, with the aim being to find the most robust solution of the two. The nonlinear generalised minimum variance (NGMV) control algorithm was chosen for control design in this application. Due to its ability to compensate for significant system nonlinearities and the ease of implementation, NGMV was a good candidate for the task at hand. Tuning controller parameters to stabilize the system could also be based on the previously determined traditional control solutions. An investigation of controllers’ robustness against model mismatch was carried out by introducing various levels of uncertainty which influence actuators’ natural frequency to assess system sensitivity. The outcome of the investigation determined that traditional and NGMV controllers provided comparable regulating performance in terms of reference tracking and disturbance rejection, for the nominal case. This confirmed the assertion that the PID-based NGMV weightings selection is a useful starting point for controller tuning. Increasing the mismatch between the nominal system based on which the controllers’ were designed and the actual plant showed that the traditional control was marginally more robust in this application. The final contribution to knowledge this thesis aimed to bring was minimising the impact force during load placement on a fixed and rigid platform. To that end, the contact forces between the payload and a platform were first successfully modelled and measured. A switching algorithm between position and force control was then developed based on a methodology found in literature but on a microscopic scale project. To execute smooth load placement, an automated hybrid force/position control scheme was implemented. The proposed algorithm enabled position control on x and y axes, while minimising impact forces on the z-axis. Unfortunately, preliminary findings showed that there is still work to be done to claim any success in this regard. However, the author hopes this offers a good starting point for future work

Affordances And Control Of A Spine Morphology For Robotic Quadrupedal Locomotion

How does a robot\u27s body affect what it can do? This thesis explores the question with respect to a body morphology common to biology but rare in contemporary robotics: the presence of a bendable back. In this document, we introduce the Canid and Inu quadrupedal robots designed to test hypotheses related to the presence of a robotic sagittal-plane bending back (which we refer to as a ``spine morphology\u27\u27). The thesis then describes and quantifies several advantages afforded by this morphological design choice that can be evaluated against its added weight and complexity, and proposes control strategies to both deal with the increase in degrees-of-freedom from the spine morphology and to leverage an increase in agility to reactively navigate irregular terrain. Specifically, we show using the metric of ``specific agility\u27\u27 that a spine can provides a reservoir of elastic energy storage that can be rapidly converted to kinetic energy, that a spine can augment the effective workspace of the legs without diminishing their force generation capability, and that -- in cases of direct-drive or nearly direct-drive leg actuation -- the spine motors can contribute more work in stance than the same actuator weight used in the legs, but can do so without diminishing the platform\u27s proprioceptive capabilities. To put to use the agility provided by a suitably designed robotic platform, we introduce a formalism to approximate a set of transitional navigational tasks over irregular terrain such as leaping over a gap that lend itself to doubly reactive control synthesis. We also directly address the increased complexity introduced by the spine joint with a modular compositional control framework with nice stability properties that begins to offer insight into the role of spines for steady-state running. A central theme to both the reactive navigation and the modular control frameworks is that analytical tractability is achieved by approximating the modes driving the environmental interactions with constant-acceleration dynamics

Design, modelling and control of a rotorcraft landing gear for uneven ground conditions

The ability to perform vertical take-off and landing, hovering and lateral flight provides rotorcrafts crucial advantages over other aircrafts and land vehicles for operations in remote areas. However, a major limitation of rotorcrafts is the requirement of a flat surface to land, increasing the difficulty and risk of landing operations on rough terrain or unstable surfaces. This limitation is mainly due to the use of conventional landing gear like skids or wheels. The growing use of Unmanned Aerial Vehicles (UAVs) also increases the necessity for more landing autonomy of these systems. This thesis presents the investigation into the development of an adaptive robotic landing gear for a small UAV that enhances the landing capabilities of current rotorcrafts. This landing gear consists in a legged system that is able to sense and adapt the position of its legs to the terrain conditions. This research covers the development of effective tools for the design and testing of the control system using software and hardware platforms. Mathematical models using multibody system dynamics are developed and implemented in software simulations. A hardware robot is designed and built to validate the simulation results. The system proposed in this thesis consists in a landing gear with four robotic legs that uses an Inertial Measurement Unit (IMU) to sense the body attitude, Force Sensing Resistors (FSR) to measure feet pressure and a distance sensor to detect ground approach. The actuators used are position-controlled servo motors that also provide angular position feedback. The control strategy provides position commands to coordinate the motion of all joints based on attitude and foot pressure information. It offers the advantage of being position-controlled, so it is easier to be implemented in hardware systems using low-cost components, and at the same time, the feet forcecontrol and leg design add compliance to the system. Through software simulations and laboratory experiments the system successfully landed on a 20° slope surface, substantially increasing the current slope landing limit

Online Optimization-based Gait Adaptation of Quadruped Robot Locomotion

Quadruped robots demonstrated extensive capabilities of traversing complex and unstructured environments. Optimization-based techniques gave a relevant impulse to the research on legged locomotion. Indeed, by designing the cost function and the constraints, we can guarantee the feasibility of a motion and impose high-level locomotion tasks, e.g., tracking of a reference velocity. This allows one to have a generic planning approach without the need to tailor a specific motion for each terrain, as in the heuristic case. In this context, Model Predictive Control (MPC) can compensate for model inaccuracies and external disturbances, thanks to the high-frequency replanning. The main objective of this dissertation is to develop a Nonlinear MPC (NMPC)-based locomotion framework for quadruped robots. The aim is to obtain an algorithm which can be extended to different robots and gaits; in addition, I sought to remove some assumptions generally done in the literature, e.g., heuristic reference generator and user-defined gait sequence. The starting point of my work is the definition of the Optimal Control Problem to generate feasible trajectories for the Center of Mass. It is descriptive enough to capture the linear and angular dynamics of the robot as a whole. A simplified model (Single Rigid Body Dynamics model) is used for the system dynamics, while a novel cost term maximizes leg mobility to improve robustness in the presence of nonflat terrain. In addition, to test the approach on the real robot, I dedicated particular effort to implementing both a heuristic reference generator and an interface for the controller, and integrating them into the controller framework developed previously by other team members. As a second contribution of my work, I extended the locomotion framework to deal with a trot gait. In particular, I generalized the reference generator to be based on optimization. Exploiting the Linear Inverted Pendulum model, this new module can deal with the underactuation of the trot when only two legs are in contact with the ground, endowing the NMPC with physically informed reference trajectories to be tracked. In addition, the reference velocities are used to correct the heuristic footholds, obtaining contact locations coherent with the motion of the base, even though they are not directly optimized. The model used by the NMPC receives as input the gait sequence, thus with the last part of my work I developed an online multi-contact planner and integrated it into the MPC framework. Using a machine learning approach, the planner computes the best feasible option, even in complex environments, in a few milliseconds, by ranking online a set of discrete options for footholds, i.e., which leg to move and where to step. To train the network, I designed a novel function, evaluated offline, which considers the value of the cost of the NMPC and robustness/stability metrics for each option. These methods have been validated with simulations and experiments over the three years. I tested the NMPC on the Hydraulically actuated Quadruped robot (HyQ) of the IIT’s Dynamic Legged Systems lab, performing omni-directional motions on flat terrain and stepping on a pallet (both static and relocated during the motion) with a crawl gait. The trajectory replanning is performed at high-frequency, and visual information of the terrain is included to traverse uneven terrain. A Unitree Aliengo quadruped robot is used to execute experiments with the trot gait. The optimization-based reference generator allows the robot to reach a fixed goal and recover from external pushes without modifying the structure of the NMPC. Finally, simulations with the Solo robot are performed to validate the neural network-based contact planning. The robot successfully traverses complex scenarios, e.g., stepping stones, with both walk and trot gaits, choosing the footholds online. The achieved results improved the robustness and the performance of the quadruped locomotion. High-frequency replanning, dealing with a fixed goal, recovering after a push, and the automatic selection of footholds could help the robots to accomplish important tasks for the humans, for example, providing support in a disaster response scenario or inspecting an unknown environment. In the future, the contact planning will be transferred to the real hardware. Possible developments foresee the optimization of the gait timings, i.e., stance and swing duration, and a framework which allows the automatic transition between gaits

Pattern Generation for Rough Terrain Locomotion with Quadrupedal Robots:Morphed Oscillators & Sensory Feedback

Animals are able to locomote on rough terrain without any apparent difficulty, but this does not mean that the locomotor system is simple. The locomotor system is actually a complex multi-input multi-output closed-loop control system. This thesis is dedicated to the design of controllers for rough terrain locomotion, for animal-like quadrupedal robots. We choose the problem of blind rough terrain locomotion as the target of experiments. Blind rough terrain locomotion requires continuous and momentary corrections of leg movements and body posture, and provides a proper testbed to observe the interaction of different mod- ules involved in locomotion control. As for the specific case of this thesis, we have to design rough terrain locomotion controllers that do not depend on the torque-control capability, have limited sensing, and have to be computationally light, all due to the properties of the robotics platform that we use. We propose that a robust locomotion controller, taking into account the aforementioned constraints, is constructed from at least three modules: 1) pattern generators providing the nominal patterns of locomotion; 2) A posture controller continuously adjusting the attitude of the body and keeping the robot upright; and 3) quick reflexes to react to unwanted momentary events like stumbling or an external force impulse. We introduce the framework of morphed oscillators to systematize the design of pattern gen- erators realized as coupled nonlinear oscillators. Morphed oscillators are nonlinear oscillators that can encode arbitrary limit cycle shapes and simultaneously have infinitely large basins of attraction. More importantly, they provide dynamical systems that can assume the role of feedforward locomotion controllers known as Central Pattern Generators (CPGs), and accept discontinuous sensory feedback without the risk of producing discontinuous output. On top of the CPG module, we add a kinematic model-based posture controller inspired by virtual model control (VMC), to control the body attitude. Virtual model control produces forces, and through the application of the Jacobian transpose method, generates torques which are added to the CPG torques. However, because our robots do not have a torque- control capability, we adapt the posture controller by producing task-space velocities instead of forces, thus generating joint-space velocity feedback signals. Since the CPG model used for locomotion generates joint velocities and accepts feedback without the fear of instability or discontinuity, the posture control feedback is easily integrated into the CPG dynamics. More- over, we introduce feedback signals for adjusting the posture by shifting the trunk positions, which directly update the limit cycle shape of the morphed oscillator nodes of the CPG. Reflexes are added, with minimal complexity, to react to momentary events. We implement simple impulse-based feedback mechanisms inspired by animals and successful rough terrain robots to 1) flex the leg if the robot is stumbling (stumbling correction reflex); 2) extend the leg if an expected contact is missing (leg extension reflex); or 3) initiate a lateral stepping sequence in response to a lateral external perturbation. CPG, posture controller, and reflexes are put together in a modular control architecture alongside additional modules that estimate inclination, control speed and direction, maintain timing of feedback signals, etc. [...

Development of a Wearable Mechatronic Elbow Brace for Postoperative Motion Rehabilitation

This thesis describes the development of a wearable mechatronic brace for upper limb rehabilitation that can be used at any stage of motion training after surgical reconstruction of brachial plexus nerves. The results of the mechanical design and the work completed towards finding the best torque transmission system are presented herein. As part of this mechatronic system, a customized control system was designed, tested and modified. The control strategy was improved by replacing a PID controller with a cascade controller. Although the experiments have shown that the proposed device can be successfully used for muscle training, further assessment of the device, with the help of data from the patients with brachial plexus injury (BPI), is required to improve the control strategy. Unique features of this device include the combination of adjustability and modularity, as well as the passive adjustment required to compensate for the carrying angle

Bio-Inspired Soft Artificial Muscles for Robotic and Healthcare Applications

Soft robotics and soft artificial muscles have emerged as prolific research areas and have gained substantial traction over the last two decades. There is a large paradigm shift of research interests in soft artificial muscles for robotic and medical applications due to their soft, flexible and compliant characteristics compared to rigid actuators. Soft artificial muscles provide safe human-machine interaction, thus promoting their implementation in medical fields such as wearable assistive devices, haptic devices, soft surgical instruments and cardiac compression devices. Depending on the structure and material composition, soft artificial muscles can be controlled with various excitation sources, including electricity, magnetic fields, temperature and pressure. Pressure-driven artificial muscles are among the most popular soft actuators due to their fast response, high exertion force and energy efficiency. Although significant progress has been made, challenges remain for a new type of artificial muscle that is easy to manufacture, flexible, multifunctional and has a high length-to-diameter ratio. Inspired by human muscles, this thesis proposes a soft, scalable, flexible, multifunctional, responsive, and high aspect ratio hydraulic filament artificial muscle (HFAM) for robotic and medical applications. The HFAM consists of a silicone tube inserted inside a coil spring, which expands longitudinally when receiving positive hydraulic pressure. This simple fabrication method enables low-cost and mass production of a wide range of product sizes and materials. This thesis investigates the characteristics of the proposed HFAM and two implementations, as a wearable soft robotic glove to aid in grasping objects, and as a smart surgical suture for perforation closure. Multiple HFAMs are also combined by twisting and braiding techniques to enhance their performance. In addition, smart textiles are created from HFAMs using traditional knitting and weaving techniques for shape-programmable structures, shape-morphing soft robots and smart compression devices for massage therapy. Finally, a proof-of-concept robotic cardiac compression device is developed by arranging HFAMs in a special configuration to assist in heart failure treatment. Overall this fundamental work contributes to the development of soft artificial muscle technologies and paves the way for future comprehensive studies to develop HFAMs for specific medical and robotic requirements