A novel sensorless control for multiphase induction motor drives based on singularly perturbed sliding mode observer-experimental validation

This paper aims to develop an innovative sensorless control approach for a five-phase induction motor (IM) drive. The operation principle of the sensorless scheme is based on the sliding mode theory, within which a sliding mode observer (SMO) estimates the speed and rotor resistance simultaneously. The operation methodology of the proposed control technique is formulated using the mathematical model of the machine and the two-time-scale approach. The observation technique offers a simple and robust solution of speed and rotor resistance estimation for the sensorless control approach of the multiphase drive. The paper considers the five-phase induction motor (IM) as a case study; however, the proposed control algorithm can be employed by different types of multiphase machines. To test the applicability of the proposed sensorless control approach, the drive performance is firstly validated using MATLAB/Simulink-based simulation. Then, the simulation results are verified using real-time simulation and experimentally using TMS320C32 DSP-based control board. The obtained results confirm and validate the ability of the proposed control procedure in achieving a robust dynamic performance of the drive against the system uncertainties such as parameter variation.This research was supported by department of electrical engineering and computer science, Khalifa University, Abu Dhabi 127788, UAE.Scopu

Health Monitoring of Nonlinear Systems with Application to Gas Turbine Engines

Health monitoring and prognosis of nonlinear systems is mainly concerned with system health tracking and its evolution prediction to future time horizons. Estimation and prediction schemes constitute as principal components of any health monitoring framework. In this thesis, the main focus is on development of novel health monitoring techniques for nonlinear dynamical systems by utilizing model-based and hybrid prognosis and health monitoring approaches. First, given the fact that particle filters (PF) are known as a powerful tool for performing state and parameter estimation of nonlinear dynamical systems, a novel dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the prediction error (PE) concept and the particle filtering scheme. Estimation of system parameters along with the states generate an updated model that can be used for a long-term prediction problem. Next, an improved particle filtering-based methodology is developed to address the prediction step within the developed health monitoring framework. In this method, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizons. Particles are then propagated to future time instants according to a resampling algorithm in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models (DLM) for development of an observation forecasting scheme. A novel hybrid architecture is then proposed to develop prognosis and health monitoring methodologies for nonlinear systems by integration of model-based and computationally intelligent-based techniques. Our proposed hybrid health monitoring methodology is constructed based on a framework that is not dependent on the structure of the neural network model utilized in the implementation of the observation forecasting scheme. Moreover, changing the neural network model structure in this framework does not significantly affect the prediction accuracy of the entire health prediction algorithm. Finally, a method for formulation of health monitoring problems of dynamical systems through a two-time scale decomposition is introduced. For this methodology the system dynamical equations as well as the affected damage model, are investigated in the two-time scale system health estimation and prediction steps. A two-time scale filtering approach is developed based on the ensemble Kalman filtering (EnKF) methodology by taking advantage of the model reduction concept. The performance of the proposed two-time scale ensemble Kalman filters is shown to be more accurate and less computationally intensive as compared to the well-known particle filtering approach for this class of nonlinear systems. All of our developed methods have been applied for health monitoring and prognosis of a gas turbine engine when it is affected by various degradation damages. Extensive comparative studies are also conducted to validate and demonstrate the advantages and capabilities of our proposed frameworks and methodologies

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Development of monitoring and control systems for biotechnological processes

The field of biotechnology represents an important research area that has gained increasing success in recent times. Characterized by the involvement of biological organisms in manufacturing processes, its areas of application are broad and include the pharmaceuticals, agri-food, energy, and even waste treatment. The implication of living microorganisms represents the common element in all bioprocesses. Cell cultivations is undoubtedly the key step that requires maintaining environmental conditions in precise and defined ranges, having a significant impact on the process yield and thus on the desired product quality. The apparatus in which this process occurs is the bioreactor. Unfortunately, monitoring and controlling these processes can be a challenging task because of the complexity of the cell growth phenomenon and the limited number of variables can be monitored in real-time. The thesis presented here focuses on the monitoring and control of biotechnological processes, more specifically in the production of bioethanol by fermentation of sugars using yeasts. The study conducted addresses several issues related to the monitoring and control of the bioreactor, in which the fermentation takes place. First, the topic concerning the lack of proper sensors capable of providing online measurements of key variables (biomass, substrate, product) is investigated. For this purpose, nonlinear estimation techniques are analyzed to reconstruct unmeasurable states. In particular, the geometric observer approach is applied to select the best estimation structure and then a comparison with the extended Kalman filter is reported. Both estimators proposed demonstrate good estimation capabilities as input model parameters vary. Guaranteeing the achievement of the desired ethanol composition is the main goal of bioreactor control. To this end, different control strategies, evaluated for three different scenarios, are analzyed. The results show that the MIMO system, together with an estimator for ethanol composition, ensure the compliance with product quality. After analyzing these difficulties through numeric simulations, this research work shifts to testing a specific biotechnological process such as manufacturing bioethanol from brewery’s spent grain (BSG) as renewable waste biomass. Both acid pre-treatment, which is necessary to release sugars, and fermentation are optimized. Results show that a glucose yield of 18.12 per 100 g of dried biomass is obtained when the pre-treatment step is performed under optimized conditions (0.37 M H2SO4, 10% S-L ratio). Regarding the fermentation, T=25°C, pH=4.5, and inoculum volume equal to 12.25% v/v are selected as the best condition, at which an ethanol yield of 82.67% evaluated with respect to theoretical one is obtained. As a final step, the use of Raman spectroscopy combined with chemometric techniques such as Partial Least Square (PLS) analysis is evaluated to develop an online sensor for fermentation process monitoring. The results show that the biomass type involved significantly affects the acquired spectra, making them noisy and difficult to interpret. This represents a nontrivial limitation of the applied methodology, for which more experimental data and more robust statistical techniques could be helpful

Robust Position-based Visual Servoing of Industrial Robots

Recently, the researchers have tried to use dynamic pose correction methods to improve the accuracy of industrial robots. The application of dynamic path tracking aims at adjusting the end-effector’s pose by using a photogrammetry sensor and eye-to-hand PBVS scheme. In this study, the research aims to enhance the accuracy of industrial robot by designing a chattering-free digital sliding mode controller integrated with a novel adaptive robust Kalman filter (ARKF) validated on Puma 560 model on simulation. This study includes Gaussian noise generation, pose estimation, design of adaptive robust Kalman filter, and design of chattering-free sliding mode controller. The designed control strategy has been validated and compared with other control strategies in Matlab 2018a Simulink on a 64bits PC computer. The main contributions of the research work are summarized as follows. First, the noise removal in the pose estimation is carried out by the novel ARKF. The proposed ARKF deals with experimental noise generated from photogrammetry observation sensor C-track 780. It exploits the advantages of adaptive estimation method for states noise covariance (Q), least square identification for measurement noise covariance (R) and a robust mechanism for state variables error covariance (P). The Gaussian noise generation is based on the collected data from the C-track when the robot is in a stationary status. A novel method for estimating covariance matrix R considering both effects of the velocity and pose is suggested. Next, a robust PBVS approach for industrial robots based on fast discrete sliding mode controller (FDSMC) and ARKF is proposed. The FDSMC takes advantage of a nonlinear reaching law which results in faster and more accurate trajectory tracking compared to standard DSMC. Substituting the switching function with a continuous nonlinear reaching law leads to a continuous output and thus eliminating the chattering. Additionally, the sliding surface dynamics is considered to be a nonlinear one, which results in increasing the convergence speed and accuracy. Finally, the analysis techniques related to various types of sliding mode controller have been used for comparison. Also, the kinematic and dynamic models with revolutionary joints for Puma 560 are built for simulation validation. Based on the computed indicators results, it is proven that after tuning the parameters of designed controller, the chattering-free FDSMC integrated with ARKF can essentially reduce the effect of uncertainties on robot dynamic model and improve the tracking accuracy of the 6 degree-of-freedom (DOF) robot

Lateral Control for Autonomous and Connected Following Vehicles with Limited Preview Information

Lateral control of an autonomous and connected vehicle (ACV), especially in emergency situations, is important from the safety viewpoint. In these situations, the trajectory to be followed by an ACV must either be planned in real-time (e.g., for a possible evasion maneuver if the obstacle to be avoided is detected) or be communicated from its preceding vehicle. Typically, the trajectory information is available to the following ACV in the form of GPS time samples. From the viewpoint of lateral control, the lateral velocity information is not readily available, and the feedback structure must reflect this reality. In this work, we develop a methodology to synthesize a lateral control algorithm for a following ACV in a two-vehicle platoon in two steps: (1) From the limited preview information of the trajectory to be tracked via samples of GPS waypoints, and we estimate the radius of curvature of the trajectory using ``least-square'' estimation and (2) develop a fixed-structure feedback control scheme for following the predecessor by synthesizing the set of stabilizing gains corresponding to lateral position error, heading error and heading rate error. Numerical simulation and experimental results corroborate the effectiveness of the proposed feedback-feedforward schemes. Based on this proposed feedback-feedforward controller, we investigated Emergency Lane Change (ELC) control problem for a convoy of autonomous and connected vehicles. Typically, an ELC maneuver is triggered by emergency cues from the front or the end of convoy as a response to either avoiding an obstacle or making way for other vehicles to pass. From a safety viewpoint, connectivity of ACVs is essential as it entails obtaining or exchanging information about other ACVs in the convoy. This thesis assumes that ACVs have reliable connectivity and that every following ACV has the information about GPS position traces of the lead and immediately preceding vehicles in the convoy. This information provides a ``discretized'' preview of the trajectory to be tracked. Based on the available information, this thesis focuses on two schemes for synthesizing lateral control of ACVs based on (a) a single composite ELC trajectory that fuses lead and preceding vehicle's GPS traces and (b) separate ELC trajectories based on preview data of preceding and lead vehicles. The former case entails the construction of a single composite ELC trajectory, determination of the cross track error, heading and yaw rate errors with respect to this trajectory and synthesis of a lateral control action. The latter case entails the construction of two separate trajectories corresponding to the lead vehicle's and preceding vehicle's data separately and the subsequent computation of two sets of associated errors and lateral control actions and combining them to provide a steering command. Numerical and experimental results corroborate the effectiveness of these two schemes. For multiple vehicle control in a convoy/platooning, identifying vehicles and objects in the vicinity of platooning vehicles is critical for convoy/platooning safety. A neighboring vehicle may change lanes and enter a lane with platooning vehicles. Any deployable platooning system must be able to first identify the presence of a cut-in vehicle before performing any control actions on the vehicles. In this work, we present a sensor fusion algorithm that combines radar and vision data obtained onboard a moving truck to identify the states of the cut-in vehicle. We then present experimental results to corroborate the performance of the proposed algorithms. First, I investigate temporal patterns of user trust and reliance in XAI systems (Objective 3). My study results show that model explanations not only affected user final trust but also shape how user trust evolves over time; indicating the importance of user behavior for evaluating XAI systems. Lastly, I propose an open-sourced human-attention evaluation baseline for direct evaluation of saliency map explanations (Objective 4). I demonstrate my human-attention benchmark's utility for quantitative evaluation of model explanations by comparing it with single-layer feature masks baseline. My experiments also show the advantage of my evaluation baseline by revealing different user biases in the subjective rating evaluation of model saliency explanations

Control and Estimation Oriented Model Order Reduction for Linear and Nonlinear Systems

Optimization based controls are advantageous in meeting stringent performance requirements and accommodating constraints. Although computers are becoming more powerful, solving optimization problems in real-time remains an obstacle because of associated computational complexity. Research efforts to address real-time optimization with limited computational power have intensified over the last decade, and one direction that has shown some success is model order reduction. This dissertation contains a collection of results relating to open- and closed-loop reduction techniques for large scale unconstrained linear descriptor systems, constrained linear systems, and nonlinear systems. For unconstrained linear descriptor systems, this dissertation develops novel gramian and Riccati solution approximation techniques. The gramian approximation is used for an open-loop reduction technique following that of balanced truncation proposed by (Moore, 1981) for ordinary linear systems and (Stykel, 2004) for linear descriptor systems. The Riccati solution is used to generalize the Linear Quadratic Gaussian balanced truncation (LQGBT) of (Verriest, 1981) and (Jonckheere and Silverman, 1983). These are applied to an electric machine model to reduce the number of states from $>$100000 to 8 while improving accuracy over the state-of-the-art modal truncation of (Zhou, 2015) for the purpose of condition monitoring. Furthermore, a link between unconstrained model predictive control (MPC) with a terminal penalty and LQG of a linear system is noted, suggesting an LQGBT reduced model as a natural model for reduced MPC design. The efficacy of such a reduced controller is demonstrated by the real-time control of a diesel airpath. Model reduction generally introduces modeling errors, and controlling a constrained plant subject to modeling errors falls squarely into robust control. A standard assumption of robust control is that inputs/states/outputs are constrained by convex sets, and these sets are ``tightened'' for robust constraint satisfaction. However, robust control is often overly conservative, and resulting control strategies cannot take advantage of the true admissible sets. A new reduction problem is proposed that considers the reduced order model accuracy and constraint conservativeness. A constant tube methodology for reduced order constrained MPC is presented, and the proposed reduced order model is found to decrease the constraint conservativeness of the reduced order MPC law compared to reduced order models obtained by gramian and LQG reductions. For nonlinear systems, a reformulation of the empirical gramians of (Lall et al., 1999) and (Hahn et al., 2003) into simpler, yet more general forms is provided. The modified definitions are used in the balanced truncation of a nonlinear diesel airpath model, and the reduced order model is used to design a reduced MPC law for tracking control. Further exploiting the link between the gramian and Riccati solution for linear systems, the new empirical gramian formulation is extended to obtain empirical Riccati covariance matrices used for closed-loop model order reduction of a nonlinear system. Balanced truncation using the empirical Riccati covariance matrices is demonstrated to result in a closer-to-optimal nonlinear compensator than the previous balanced truncation techniques discussed in the dissertation.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140839/1/riboch_1.pd

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

Near real time satellite event detection, characterization, and operational assessment via the exploitation of remote photoacoustic signatures

Active satellites frequently maneuver to maintain nominal mission orbits and to mitigate conjunctions with debris or other active spacecraft. With an ever-growing Resident Space Object (RSO) population, the need to detect and predict changes in active satellite trajectories has become increasingly important. Satellite behavior typically produces a lag on the order of hours to days from time of maneuver to unmodeled dynamic event detection depending on the magnitude of the Δv. For uncooperative objects, this detection lag poses a hazard to other satellites. Implementing an active photoacoustic signature change detection methodology to detect and predict unmodeled dynamic events can reduce the overall conjunction risk and provide a means for a near real time pulse of satellite events. Remote photoacoustic sensing is defined herein as the acoustic playback of hypertemporal photometric data. This thesis demonstrates the conversion of both photometric and luminance data into the acoustic domain in support of RSO characterization. If photometric data is collected at a kilohertz (kHz) level sampling rate, optimally above 40 kHz, any changes in outgoing photon flux due to satellite body vibrations caused by on-board events may be detected. Using the detected event epochs allows for accurate estimation of the Δv, direction, and maneuver type in near real time. The analysis of hypertemporal light curve data in the photometric, frequency, and photoacoustic domains can thus help characterize the event and provide mission-specific intelligence. This thesis also investigates the use of signal processing methods, primarily cross-correlation, to improve the minimum vibration-induced satellite body displacement detection threshold in the presence of noise generated by the turbulent atmosphere. In addition to the basic parameters related to the Δv calculation, it is shown that thruster mass flow rate, fuel consumption, exhaust velocity, and specific impulse can be derived with a sufficiently accurate and precise a priori estimate of mass. The operational assessment and satellite characterization made possible by remote photoacoustic sensing ultimately provides a means to support a more sustainable space environment for long-term human space flight operationsAerospace Engineerin