Feedback control of underactuated cable-driven parallel robots

In this work an Underactuated Cable-Driven Parallel Robot (UACDPR) that operates in the three dimensional Euclidean space is considered. The End-Effector has 6 degrees of freedom and is actuated by 4 cables, therefore from a mechanical point of view the robot is defined underconstrained. However, considering only three controlled pose variables, the degree of redundancy for the control theory can be considered one. The aim of this thesis is to design a feedback controller for a point-to-point motion that satisfies the transient requirements, and is capable of reducing oscillations that derive from the reduced number of constraints. A force control is chosen for the positioning of the End-Effector, and error with respect to the reference is computed through data measure of several sensors (load cells, encoders and inclinometers) such as cable lengths, tension and orientation of the platform. In order to express the relation between pose and cable tension, the inverse model is derived from the kinematic and dynamic model of the parallel robot. The intrinsic non-linear nature of UACDPRs systems introduces an additional level of complexity in the development of the controller, as a result the control law is composed by a partial feedback linearization, and damping injection to reduce orientation instability. The fourth cable allows to satisfy a further tension distribution constraint, ensuring positive tension during all the instants of motion. Then simulations with different initial conditions are presented in order to optimize control parameters, and lastly an experimental validation of the model is carried out, the results are analysed and limits of the presented approach are defined.

Substructuring approache in state space models for dynamic system parameters identification

In the recent decade, the request for structural health monitoring expertise increased exponentially in the United States. The aging issues that most of the transportation structures are experiencing can put in serious jeopardy the economic system of a region as well as of a country. At the same time, the monitoring of structures is a central topic of discussion in Europe, where the preservation of historical buildings has been addressed over the last four centuries. More recently, various concerns arose about security performance of civil structures after tragic events such the 9/11 or the 2011 Japan earthquake: engineers looks for a design able to resist exceptional loadings due to earthquakes, hurricanes and terrorist attacks. After events of such a kind, the assessment of the remaining life of the structure is at least as important as the initial performance design. Consequently, it appears very clear that the introduction of reliable and accessible damage assessment techniques is crucial for the localization of issues and for a correct and immediate rehabilitation. The System Identification is a branch of the more general Control Theory. In Civil Engineering, this field addresses the techniques needed to find mechanical characteristics as the stiffness or the mass starting from the signals captured by sensors. The objective of the Dynamic Structural Identification (DSI) is to define, starting from experimental measurements, the modal fundamental parameters of a generic structure in order to characterize, via a mathematical model, the dynamic behavior. The knowledge of these parameters is helpful in the Model Updating procedure, that permits to define corrected theoretical models through experimental validation. The main aim of this technique is to minimize the differences between the theoretical model results and in situ measurements of dynamic data. Therefore, the new model becomes a very effective control practice when it comes to rehabilitation of structures or damage assessment. The instrumentation of a whole structure is an unfeasible procedure sometimes because of the high cost involved or, sometimes, because it’s not possible to physically reach each point of the structure. Therefore, numerous scholars have been trying to address this problem. In general two are the main involved methods. Since the limited number of sensors, in a first case, it’s possible to gather time histories only for some locations, then to move the instruments to another location and replay the procedure. Otherwise, if the number of sensors is enough and the structure does not present a complicate geometry, it’s usually sufficient to detect only the principal first modes. This two problems are well presented in the works of Balsamo [1] for the application to a simple system and Jun [2] for the analysis of system with a limited number of sensors. Once the system identification has been carried, it is possible to access the actual system characteristics. A frequent practice is to create an updated FEM model and assess whether the structure fulfills or not the requested functions. Once again the objective of this work is to present a general methodology to analyze big structure using a limited number of instrumentation and at the same time, obtaining the most information about an identified structure without recalling methodologies of difficult interpretation. A general framework of the state space identification procedure via OKID/ERA algorithm is developed and implemented in Matlab. Then, some simple examples are proposed to highlight the principal characteristics and advantage of this methodology. A new algebraic manipulation for a prolific use of substructuring results is developed and implemented.

Impact of irrigation in the Po valley (Italy) on meteorological parameters through numerical simulation

Recent studies found that soil-atmosphere coupling features, through soil moisture, have been crucial to simulate well heat waves amplitude, duration and intensity. Moreover, it was found that soil moisture depletion both in Winter and Spring anticipates strong heat waves during the Summer. Irrigation in geophysical studies can be intended as an anthropogenic forcing to the soil-moisture, besides changes in land proprieties. In this study, the irrigation was add to a LAM hydrostatic model (BOLAM) and coupled with the soil. The response of the model to irrigation perturbation is analyzed during a dry Summer season. To identify a dry Summer, with overall positive temperature anomalies, an extensive climatological characterization of 2015 was done. The method included a statistical validation on the reference period distribution used to calculate the anomalies. Drought conditions were observed during Summer 2015 and previous seasons, both on the analyzed region and the Alps. Moreover July was characterized as an extreme event for the referred distribution. The numerical simulation consisted on the summer season of 2015 and two run: a control run (CTR), with the soil coupling and a perturbed run (IPR). The perturbation consists on a mask of land use created from the Cropland FAO dataset, where an irrigation water flux of 3 mm/day was applied from 6 A.M. to 9 A.M. every day. The results show that differences between CTR and IPR has a strong daily cycle. The main modifications are on the air masses proprieties, not on to the dynamics. However, changes in the circulation at the boundaries of the Po Valley are observed, and a diagnostic spatial correlation of variable differences shows that soil moisture perturbation explains well the variation observed in the 2 meters height temperature and in the latent heat fluxes.On the other hand, does not explain the spatial shift up and downslope observed during different periods of the day. Given the results, irrigation process affects the atmospheric proprieties on a larger scale than the irrigation, therefore it is important in daily forecast, particularly during hot and dry periods.

Modeling and Model Predictive Control of an Industrial Hydraulic System

This thesis aims to illustrate the construction of a mathematical model of a hydraulic system, oriented to the design of a model predictive control (MPC) algorithm. The modeling procedure starts with the basic formulation of a piston-servovalve system. The latter is a complex non linear system with some unknown and not measurable effects that constitute a challenging problem for the modeling procedure. The first level of approximation for system parameters is obtained basing on datasheet informations, provided workbench tests and other data from the company. Then, to validate and refine the model, open-loop simulations have been made for data matching with the characteristics obtained from real acquisitions. The final developed set of ODEs captures all the main peculiarities of the system despite some characteristics due to highly varying and unknown hydraulic effects, like the unmodeled resistive elements of the pipes. After an accurate analysis, since the model presents many internal complexities, a simplified version is presented. The latter is used to linearize and discretize correctly the non linear model. Basing on that, a MPC algorithm for reference tracking with linear constraints is implemented. The results obtained show the potential of MPC in this kind of industrial applications, thus a high quality tracking performances while satisfying state and input constraints. The increased robustness and flexibility are evident with respect to the standard control techniques, such as PID controllers, adopted for these systems. The simulations for model validation and the controlled system have been carried out in a Python code environment.

Application of the Extended Kalman filter for speed sensorless control of PM synchronous machines

The increasing interest in the decarbonization process led to a rapidly growing trend of electrification strategies in the automotive industry. In particular, OEMs are pushing towards the development and production of efficient electric vehicles. Moreover, research on electric motors and their control are exploding in popularity. The increase of computational power in embedded control hardware is allowing the development of new control algorithm, such as sensorless control strategy. Such control strategy allows the reduction of the number of sensors, which implies reduced costs and increased system reliability. The thesis objective is to realize a sensorless control for high-performance automotive motors. Several algorithms for rotor angle observers are implemented in the MATLAB and Simulink environment, with emphasis on the Kalman observer. One of the Kalman algorithms already available in the literature has been selected, implemented and benchmarked, with emphasis on its comparison with the Sliding Mode observer. Different models characterized by increasing levels of complexity are simulated. A simplified synchronous motor with ”constant parameters”, controlled by an ideal inverter is first analyzed; followed by a complete model defined by real motor maps, and controlled by a switching inverter. Finally, it was possible to test the developed algorithm on a real electric motor mounted on a test bench. A wide range of different electric motors have been simulated, which led to an exhaustive review of the sensorless control algorithm. The final results underline the capability of the Kalman observer to effectively control the motor on a real test bench.

Development of a health parameters tracking wearable device for detection of stress episodes and principles of panic attacks

A proof of concept for a wearable device is presented to help patients who suffer from panic attacks due to panic disorder. The aim of this device is to enable such patients manage these stressful episodes by guiding them to regulate their breathing and by informing the care taker. Panic attack prediction is deployed that can enable the healthcare providers to not only monitor and manage the panic attacks of a patient but also carry out an early intervention to reduce the symptom severity of the approaching panic attack. The patient can acquire the help they need, ultimately regaining control. The concept of panic attack prediction can lead to a personalized treatment of the patient. The study is conducted using a small real-world dataset, and only two primary symptoms of panic attack are used. These symptoms include pacing heart rate and hyperventilation or abnormal breathing rate. This thesis project is developed in collaboration with ALTEN italia and all the required hardware is provided by them.

Axis control optimization: autotuning techniques and frequency response analysis of industrial mechatronic systems

In the field of industrial automation, there is an increasing need to use optimal control systems that have low tracking errors and low power and energy consumption. The motors we are dealing with are mainly Permanent Magnet Synchronous Motors (PMSMs), controlled by 3 different types of controllers: a position controller, a speed controller, and a current controller. In this thesis, therefore, we are going to act on the gains of the first two controllers by going to find, through the TwinCAT 3 software, what might be the best set of parameters. To do this, starting with the default parameters recommended by TwinCAT, two main methods were used and then compared: the method of Ziegler and Nichols, which is a tabular method, and advanced tuning, an auto-tuning software method of TwinCAT. Therefore, in order to analyse which set of parameters was the best,several experiments were performed for each case, using the Motion Control Function Blocks. Moreover, some machines, such as large robotic arms, have vibration problems. To analyse them in detail, it was necessary to use the Bode Plot tool, which, through Bode plots, highlights in which frequencies there are resonance and anti-resonance peaks. This tool also makes it easier to figure out which and where to apply filters to improve control.