Joint Dynamics and Adaptive Feedforward Control of Lightweight Industrial Robots

The use of lightweight strain-wave transmissions in collaborative industrial robots leads to structural compliance and a complex nonlinear behavior of the robot joints. Furthermore, wear and temperature changes lead to variations in the joint dynamics behavior over time. The immediate negative consequences are related to the performance of motion and force control, safety, and lead-through programming.This thesis introduces and investigates new methods to further increase the performance of collaborative industrial robots subject to complex nonlinear and time-varying joint dynamics behavior. Within this context, the techniques of mathematical modeling, system identification, and adaptive estimation and control are applied. The methods are experimentally validated using the collaborative industrial robots by Universal Robots.Mathematically, the robot and joint dynamics are considered as two coupled subsystems. The robot dynamics are derived and linearly parametrized to facilitate identification of the inertial parameters. Calibrating these parameters leads to improvements in torque prediction accuracy of 16.5 %-28.5 % depending on the motion.The joint dynamics are thoroughly analyzed and characterized. Based on a series of experiments, a comprehensive model of the robot joint is established taking into account the complex nonlinear dynamics of the strain-wave transmission, that is the nonlinear compliance, hysteresis, kinematic error, and friction. The steady-state friction is considered to depend on angular velocity, load torque, and temperature. The dynamic friction characteristics are described by an Extended Generalized Maxwell-Slip (E-GMS) model which describes in a combined framework; hysteresis characteristics that depend on angular position and Coulomb friction that depend on load torque. E-GMS model-based feedforward control improves the torque prediction accuracy by a factor 2.1 and improve the tracking error by a factor 1.5.An E-GMS model-based adaptive feedforward controller is developed to address the issue of friction changing with wear and temperature. The adaptive control strategy leads to improvements in torque prediction of 84 % and tracking error of 20 %

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

These days, biomimetic and compliant actuators have been made available to the main applications of rehabilitation and assistive robotics. In this context, the interaction control of soft robots, mechatronic surgical instruments and robotic prostheses can be improved through the adoption of pneumatic artificial muscles (PAMs), a class of compliant actuators that exhibit some similarities with the structure and function of biological muscles. Together with the advantage of implementing adaptive compliance control laws, the nonlinear and hysteretic force/length characteristics of PAMs pose some challenges in the design and implementation of tracking control strategies. This paper presents a parsimonious and accurate model of the asymmetric hysteresis observed in the force response of PAMs. The model has been validated through the experimental identification of the mechanical response of a small-sized PAM where the asymmetric effects of hysteresis are more evident. Both the experimental results and a comparison with other dynamic friction models show that the proposed model could be useful to implement efficient compensation strategies for the tracking control of soft robots

Dynamic Friction Parameter Identification Method with LuGre Model for Direct-Drive Rotary Torque Motor

Attainment of high-performance motion/velocity control objectives for the Direct-Drive Rotary (DDR) torque motor should fully consider practical nonlinearities in controller design, such as dynamic friction. The LuGre model has been widely utilized to describe nonlinear friction behavior; however, parameter identification for the LuGre model remains a challenge. A new dynamic friction parameter identification method for LuGre model is proposed in this study. Static parameters are identified through a series of constant velocity experiments, while dynamic parameters are obtained through a presliding process. Novel evolutionary algorithm (NEA) is utilized to increase identification accuracy. Experimental results gathered from the identification experiments conducted in the study for a practical DDR torque motor control system validate the effectiveness of the proposed method

Dynamic Model Identification and Trajectory Correction for Virtual Process Planning in Multi-Axis Machine Tools

In today’s industry, the capability to effectively reduce production time and cost gives a manufacturer a vital advantage against its competitors. Specifically, in the machining industry, the ability to simulate the dynamic performance of machine tools, and the physics of cutting processes, is critical to taking corrective actions, achieving process and productivity improvements, thereby enhancing competitiveness. In this context, being able to estimate mathematical models which describe the dynamic response of machine tools to commanded tool trajectories and external disturbance forces plays a key role in establishing virtual and intelligent manufacturing capability. These models can also be used in virtual simulations for process improvement, such as compensating for dynamic positioning errors by making small corrections to the commanded trajectory. This, in turn, can facilitate further productivity improvement and part quality in multi-axis manufacturing operations, such as machining. This thesis presents new methods for identifying the positioning response and friction characteristics of machine tool servo drives in a nonintrusive manner, and an approach for enhancing dynamic positioning accuracy through commanded trajectory correction via Iterative Learning Control (ILC). As the first contribution, the linear transfer functions correlating the positioning response to the commanded trajectory and friction disturbance inputs are identified using a new pole search method in conjunction with least squares (LS) projection. It is validated that this approach can work with in-process collected data, and demonstrates superior convergence and numerical characteristics, and model prediction accuracy, compared to an earlier ‘rapid identification’ approach based on the application of classical Least Squares for the full model. Effectiveness of the new method is demonstrated in simulations, and in experimental case studies for planar motion on two different machine tools, a gear grinding machine and a 5-axis machining center. Compared to the earlier approach, which could predict servo errors with 10-68% closeness, the new method improves the prediction accuracy to 0.5-2%. In the simulation of feed drives used in multi-axis machines, high fidelity prediction of the nonlinear stick-slip friction plays an important role. Specifically, time-dependent (i.e., dynamic) friction models help to improve the accuracy of virtual predictions. While many elaborate models have been proposed for this purpose, such as the generalized Maxwell-slip (GMS) model, their parameters can be numerous and difficult to identify from limited field data. In this thesis, as the second contribution, a new and highly efficient method of parameterizing the pre-sliding (hysteretic) portion of the GMS friction model is presented. This approach drastically reduces the number of unknown variables to identify, by estimating only the affective breakaway force, breakaway displacement, and ‘shape factor’ describing the shape of the pre-sliding virgin curve. Reduction in the number of unknowns enables this ‘reduced parameter’ GMS model to be identified much more easily from in-process data, compared to the fully parameterized GMS model, and the time-dependent friction dynamics can still be simulated accurately. Having improved the positioning response transfer function estimation and friction modeling, as the third contribution of this thesis, these two elements are combined together in a 3-step process. First, the servo response is estimated considering simplified Coulomb friction dynamics. Then, the friction model is replaced and identified as a reduced parameter GMS model. In the third step, the transfer function poles and zeros, and the reduced parameter GMS model, are concurrently optimized to replicate the observed experimental response with even greater fidelity. This improvement has been quantified as 12-44% in RMS and 28-54% in MAX values. This approach is successful in servo systems with predominantly rigid body behavior. However, its extension to a servo system with vibratory dynamics did not produce an immediately observed improvement. This is attributed to the dominance of vibrations in response to the commanded trajectory, and further investigation is recommended for future research. Having an accurate model of a multi-axis machine’s feed drive response allows for the dynamic positioning errors, which can lead to workpiece inaccuracy or defects, to be predicted and corrected ahead of time. For this purpose, ILC has been investigated. It is shown that through ILC, 1-2 orders of magnitude reduction in the servo errors is possible. While ILC is already available in certain commercial CNC systems, its training cycle (which is performed during the operation of the machine tool) can lead to part defects and wasted productive machining time. The new idea proposed in this thesis is to perform ILC on a virtual model, which is continuously updated via real-time production data using the identification methods developed in this work. This would minimize the amount of trial and error correction needed on the actual machine. In the course of this thesis research, after validating the effectiveness of ILC in simulations, to reliably and safely migrate the virtual modeling and trajectory correction results into industry (such as on a gear grinding machine tool), the author initiated and led the design and fabrication of an industry-scale testing platform, comprising a Siemens 840D SolutionLine CNC with a multi-axis feed drive setup. Majority of this implementation has been completed, and in near future work, the dynamic accuracy and productivity improvements facilitated with ‘virtually’ tuned ILC are expected to be demonstrated experimentally and tested in industry

Modelling and control of position and velocity drives subject to friction

Performance degradation in most mechanical systems with friction which are not easily eliminated through design mechanisms can be greatly reduced within acceptable limits through the process of friction compensation. Generally friction compensators are used to improve system performance in terms of error reduction, transient response, thereby countering the effects of friction. Model-based techniques for friction compensation require an accurate model of the system friction. This is very important for high precision mechanical systems where excellent positioning and motion tracking, especially in the low velocities, is critical. This thesis proposes a new integrated friction model structure capable of modelling known friction dynamics. The new friction model incorporates a pre-sliding friction function with non-local hysteretic features. Analysis of the model shows the model to possess dissipative, boundedness, passivity and uniqueness properties. Results of sensitivity and robustness analysis indicate the new friction model is robust to parameter variations. A friction characterisation test-bed was designed and constructed for the purposes of friction identification, compensation and control. A set of experiments were designed and implemented on the test rig/bed to demonstrate friction dynamics. The input- output results of the experiments were used for parameter estimation of the proposed new friction model and some other relevant friction model structures. The performance of the new friction model for position and velocity control was studied using the experimental friction test-bed and simulations. The result of such analysis underscores the advantage of integrating a friction observer in the system control loop. The new friction model provided better position and velocity control of the experimental friction test-rig when compared with other well known models of friction

Patterns of stress and strain distribution during deep mining at Boulby, N. Yorkshire

The understanding of stress-deformation state transmission within the rock mass above deep mine workings is a key factor to the comprehension of the response of rock masses to changes of stress regime brought about by the mining activity for the safety of surface and subsurface structures. Based on monitoring data from active actual mine workings, this study numerically analyzes factors controlling stress and deformation using the 2D Fast Lagrangian Analysis of Continua (FLAC 2D) code and a strain-softening model to approximate creep behaviour of rock masses. The results show that distribution of stress and deformation at Boulby mine is primarily governed by the lithological heterogeneity of the overlying strata and the geological structure, including its nature within the undermined area. Data from a bespoke roof-to-floor monitoring closuremeter indicate that convergence of openings is a function of local variables, including the site location, geometry and age of the site. Patterns of ground subsidence are compared to the pattern of levelling-based measured ground subsidence. Furthermore, the analysis shows that the strain-softening model reasonably approximates the creep behaviour of the excavations. The results have implications for how we monitor and model subsidence due to mining deep excavations

A Smoothed GMS Friction Model suited for Gradient-Based Friction State Estimation

The multi-state Generalized Maxwell-Slip (GMS) friction model is known to describe all essential friction characteristics in presliding and sliding motion. It is also known that due to its switching state conditions between presliding and sliding, gradient-based parameter and state estimation methods cannot be implemented efficiently. Efficient on-line state and parameter estimation is essential for model-based friction compensation in order to track friction characteristics changes in time and space. This paper presents a Smoothed GMS (S-GMS) friction model with an analytic set of differential equations well suited for gradient-based estimation techniques. Friction state estimation is implemented with an Extended Kalman Filter (EKF) to validate the S-GMS model numerically and experimentally, and compare its behavior with the GMS model.status: publishe

Towards Learning Feasible Hierarchical Decision-Making Policies in Urban Autonomous Driving

Modern learning-based algorithms, powered by advanced deep structured neural nets, have multifacetedly facilitated automated driving platforms, spanning from scene characterization and perception to low-level control and state estimation schemes. Nonetheless, urban autonomous driving is regarded as a challenging application for machine learning (ML) and artificial intelligence (AI) since the learnt driving policies must handle complex multi-agent driving scenarios with indeterministic intentions of road participants. In the case of unsignalized intersections, automating the decision-making process at these safety-critical environments entails comprehending numerous layers of abstractions associated with learning robust driving behaviors to allow the vehicle to drive safely and efficiently. Based on our in-depth investigation, we discern that an efficient, yet safe, decision-making scheme for navigating real-world unsignalized intersections does not exist yet. The state-of-the-art schemes lacked practicality to handle real-life complex scenarios as they utilize Low-fidelity vehicle dynamic models which makes them incapable of simulating the real dynamic motion in real-life driving applications. In addition, the conservative behavior of autonomous vehicles, which often overreact to threats which have low likelihood, degrades the overall driving quality and jeopardizes safety. Hence, enhancing driving behavior is essential to attain agile, yet safe, traversing maneuvers in such multi-agent environments. Therefore, the main goal of conducting this PhD research is to develop high-fidelity learning-based frameworks to enhance the autonomous decision-making process at these safety-critical environments. We focus this PhD dissertation on three correlated and complementary research challenges. In our first research challenge, we conduct an in-depth and comprehensive survey on the state-of-the-art learning-based decision-making schemes with the objective of identifying the main shortcomings and potential research avenues. Based on the research directions concluded, we propose, in Problem II and Problem III, novel learning-based frameworks with the objective of enhancing safety and efficiency at different decision-making levels. In Problem II, we develop a novel sensor-independent state estimation for a safety-critical system in urban driving using deep learning techniques. A neural inference model is developed and trained via deep-learning training techniques to obtain accurate state estimates using indirect measurements of vehicle dynamic states and powertrain states. In Problem III, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at four-way unsignalized intersections with feasibility guarantees. The proposed technique involves an integration of two main decision-making layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a motion planning layer that uses low-level Model Predictive Control (MPC) principles to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angle for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands considering several vehicle dynamic constraints and ride comfort