PRE-ANALYTICAL VARIABLES IN CLINICAL CHEMISTRY: TRAINING MEDICAL UNDERGRADUATES THROUGH CASE BASED DISCUSSION

Background: Pre-analytical variables in clinical chemistry are factors prior to the biochemical analysis of samples affecting laboratory test results accounting for 32-75% of errors leading to misdiagnosis, decreased quality of medical care services and wastage of monetary resources. Aim: To educate first year medical undergraduates about pre-analytical variables through case based discussion and lecture method of teaching and assess the gain in knowledge by these methods. Methods and material: Two batches of medical students namely A (N=50) and B (N=52) were assessed for their background knowledge on the topic using an MCQ based questionnaire (pre-test). Batch A and B were taught through didactic lecture and case based discussion respectively. Post-test questionnaire was conducted to test the gain in knowledge of both batches. Delayed post-test was conducted after 2 weeks to assess retention of knowledge amongst students. Results: Pre-test scores of Batch A and B were not significantly different indicating that both batches had similar background knowledge of topic. Post-test scores vs. pre-test scores were significantly higher in both batches implying that both batches benefitted from their respective teaching sessions. But post-test score of Batch B was significantly higher than that of Batch A indicating higher gain of knowledge through case based discussion. Delayed post-test score was also significantly higher in Batch B vs. A implying better retention of knowledge through case based discussions. Conclusion: Topic ˜Pre-analytical variables in clinical chemistry must be included in undergraduate medical curriculum. Case based discussion could be an effective module for teaching the same. Key words: Case based discussion; Didactic lecture; Medical students; Medical education; Pre-analytical variables

PRE-ANALYTICAL VARIABLES IN CLINICAL CHEMISTRY: TRAINING MEDICAL UNDERGRADUATES THROUGH CASE BASED DISCUSSION

Background: Pre-analytical variables in clinical chemistry are factors prior to the biochemical analysis of samples affecting laboratory test results accounting for 32-75% of errors leading to misdiagnosis, decreased quality of medical care services and wastage of monetary resources. Aim: To educate first year medical undergraduates about pre-analytical variables through case based discussion and lecture method of teaching and assess the gain in knowledge by these methods. Methods and material: Two batches of medical students namely A (N=50) and B (N=52) were assessed for their background knowledge on the topic using an MCQ based questionnaire (pre-test). Batch A and B were taught through didactic lecture and case based discussion respectively. Post-test questionnaire was conducted to test the gain in knowledge of both batches. Delayed post-test was conducted after 2 weeks to assess retention of knowledge amongst students. Results: Pre-test scores of Batch A and B were not significantly different indicating that both batches had similar background knowledge of topic. Post-test scores vs. pre-test scores were significantly higher in both batches implying that both batches benefitted from their respective teaching sessions. But post-test score of Batch B was significantly higher than that of Batch A indicating higher gain of knowledge through case based discussion. Delayed post-test score was also significantly higher in Batch B vs. A implying better retention of knowledge through case based discussions. Conclusion: Topic ˜Pre-analytical variables in clinical chemistry must be included in undergraduate medical curriculum. Case based discussion could be an effective module for teaching the same. Key words: Case based discussion; Didactic lecture; Medical students; Medical education; Pre-analytical variables

Visual Tracking Nonlinear Model Predictive Control Method for Autonomous Wind Turbine Inspection

Automated visual inspection of on-and offshore wind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes are open-sourced.Comment: 8 pages, accepted for publication at ICAR conferenc

Optimization-based learning control of aerial robots operating in uncertain environments

Places that were hard to reach are now well accessible to the world with the help of aerial robots. As one of the biggest inventions of mankind in robotics, these robots place no risk on human lives because they are unmanned and remotely/autonomously operated in hostile situations. Together, these reasons make them the most promising candidate in numerous applications. Howbeit, their coupled and significantly nonlinear dynamics accompanied by open-loop instabilities lead to a complicated control problem. Although conventional control approaches such as proportional-integral-derivative (PID) and linear-quadratic-regulator (LQR), have been widely adopted, the underlying linearization leads to suboptimal performance during agile operations. Besides, there are environment-specific difficulties, like external disturbances during offshore operations, that result in an uncertain system model. Since the performance of model-based controllers is critically linked to the model accuracy, modeling uncertainties may significantly degrade their performance to the extent of instability. Therefore, rather than utilizing a sophisticated robot which is trained -- and tuned -- for a scenario in a specific environment perfectly, most people are interested in seeing them operating in unexplored conditions. In that vein, an aerial robot must learn from its own experiences and interactions with the environment for daily operations in real application scenarios. Moreover, realtime implementations of the control algorithms necessitate a tuning process that is arduous yet dangerous when performed directly on the real robot. Taking inspiration and identifying an opportunity in these issues, this thesis throws light on the development of various learning algorithms to facilitate precise tracking control of multirotor aerial robots in uncertain environmental conditions. Firstly, to cater to the nonlinear dynamics, it implements two control algorithms, namely, the nonlinear model predictive controller (NMPC) and the feedback linearization control (FLC) method. Both the control approaches explicitly accommodate the nonlinear dynamics rather than linearizing the system. Their overall efficacy is demonstrated for the position and attitude tracking problems of the aerial robots. Secondly, to accommodate the limited processing power that is available onboard aerial robots, this thesis employs fast solutions methodologies. Thanks to the efficient C++ scripts and the direct multiple shooting method along with the special realtime iteration scheme adopted in automatic-control-and-dynamic-optimization (\texttt{ACADO}) toolkit; successful onboard implementation of the control algorithms is achieved for all the real-world tests. What is more, in the case of the NMPC-NMHE framework, a complete onboard implementation is realized on a low-cost embedded processor, named Raspberry Pi 3. Thirdly, to tackle the uncertainties in the system model, this thesis proposes a few learning-based control approaches that are broadly categorized as: instantaneous learning control (InLC) and iterative learning control (ILC). In essence, the InLC technique utilizes an estimator to learn the model parameters, whereas the ILC scheme identifies the uncertain dynamics based on the experience from system repetitions. Besides, the learned system model is subsequently updated within the controller definition in both the approaches. In terms of the InLC scheme, two control frameworks are developed. The first incorporates a nonlinear moving horizon estimator (NMHE) to estimate the time-varying model parameters, thus making NMPC adaptive to the changing working conditions. The second framework constitutes a simple learning (SL) strategy to cater to the limitations of the traditional FLC method by updating controller gains and disturbance estimate within the feedback control law. In the ILC scheme, on the other hand, a Gaussian process (GP)-based regression technique models the disturbance forces that are encountered during the offshore visualization operation. Several simulations and real-world tests manifest that both InLC and ILC schemes have compelling abilities to substantially reduce the tracking error over their conventional counterparts throughout the operation. Lastly, to circumvent the tedious tuning process, an active exploration approach is proposed to obtain the NMPC's weights. The auto-tuning framework extends the basic trial-and-error method to intelligently tune the weight sets. In essence, it benefits from the retrospective knowledge gained over previous trials, and thus, expedites the tuning procedure. Moreover, the safety of the robot is ensured by employing a deep neural network-based robot model. What is more, a seamless sim-to-real transition is exhibited via the direct deployment of the weight sets from simulation tuning for the real-world trajectory tracking application.Doctor of Philosoph

Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters

This study presents a numerical implementation of fast nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (NMHE) for the trajectory tracking problem of a 3 degree of freedom (DOF) helicopter. The motivation behind using the NMPC instead of its linear counterpart is that the helicopter is operated over nonlinear regions. Moreover, this system has cross-couplings that make the control of the system even more complicated. What is more, according to our simulation scenario, the system has a time-varying dynamical model because it has time-varying parameters which are estimated online using NMHE and the extended Kalman filter (EKF) throughout the control. Although NMHE is computationally more demanding, its capability of incorporating the constraints encourages us to utilize NMHE rather than EKF. Two reference trajectories, namely, sinusoidal and square-like, are tracked, and owing to the better learning capability of NMHE over EKF, the NMPC-NMHE closed-loop control framework is able to track both reference signals with more accuracy than the NMPC-EKF control framework, even under parameter uncertainties. Thanks to the ACADO toolkit, the combined average execution time is 4 milliseconds, demonstrating the potential of the proposed framework for real-time aerospace applications using relatively cheaper processors.Ministry of Education (MOE)Accepted versionThis work was financially supported by the Singapore Ministry of Education (RG191/14) and Singapore Center for 3D Printing

A constrained instantaneous learning approach for aerial package delivery robots : onboard implementation and experimental results

Rather than utilizing a sophisticated robot which is trained—and tuned—for a scenario in a specific environment perfectly, most people are interested in seeing robots operating in various conditions where they have never been trained before. In accordance with the goal of utilizing aerial robots for daily operations in real application scenarios, an aerial robot must learn from its own experience and its interactions with the environment. This paper presents an instantaneous learning-based control approach for the precise trajectory tracking of a 3D-printed aerial robot which can adapt itself to the changing working conditions. Considering the fact that model-based controllers suffer from lack of modeling, parameter variations and disturbances in their working environment, we observe that the presented learning-based control method has a compelling ability to significantly reduce the tracking error under aforementioned uncertainties throughout the operation. Three case scenarios are considered: payload mass variations on an aerial robot for a package delivery problem, ground effect when the aerial robot is hovering/flying close to the ground, and wind-gust disturbances encountered in the outdoor environment. In each case study, parameter variations are learned using nonlinear moving horizon estimation (NMHE) method, and the estimated parameters are fed to the nonlinear model predictive controller (NMPC). Thanks to learning capability of the presented framework, the aerial robot can learn from its own experience, and react promptly—unlike iterative learning control which allows the system to improve tracking accuracy from repetition to repetition—to reduce the tracking error. Additionally, the fast C++ execution of NMPC and NMHE codes facilitates a complete onboard implementation of the proposed framework on a low-cost embedded processor.NRF (Natl Research Foundation, S’pore)Accepted versio

A Simple Learning Strategy for Feedback Linearization Control of Aerial Package Delivery Robot

This paper develops a simple learning (SL) strategy for feedback linearization control (FLC) algorithm for uncertain nonlinear systems. The SL strategy that uses desired closed-loop error dynamics updates the controller coefficients and the disturbance term in the feedback control law, while traditional feedforward control law is designed based on the nominal model by using FLC method. In this strategy, the desired closed-loop error function is minimized by using the gradient-descent method to find the adaptation rules for feedback controller gains and estimated disturbance. In addition, the system stability for an nth order uncertain nonlinear system is proven by using a Lyapunov function candidate. To test the efficiency and efficacy of the SL-FLC framework, the package delivery problem of a tilt-rotor tricopter unmanned aerial vehicle is studied in real-time. The experimental results show that the SL-FLC framework results in a better path tracking performance than the traditional FLC method, while maintaining the nominal control performance in the absence of uncertainties and exhibiting robust control performance in the presence of uncertainties

Numerical investigation of Gaussian filters with a combined type Bayesian filter for nonlinear state estimation

This study presents a numerical comparison of three filtering techniques for a nonlinear state estimation problem. We consider an Extended Kalman Filter (EKF), an Unscented Kalman Filter (UKF) and a combined type of Particle Filter, so-called Extended Particle Filter (EPF), for the state estimation for a re-entry vehicle system. The challenge in state estimation for this system is presence of significant nonlinearities in the process and measurement models. The performance aspects for the comparison include computation time, simulation time step, and effect of the choice of the initial conditions for the state estimate and covariance. Also, an investigation of the effect of the number of particles for EPF is performed. Simulation results illustrate that although EPF is computationally more expensive than EKF and UKF, it is less affected by the choice of initial conditions and simulation time step size.Published versio

QuadPlus : design, modeling, and receding horizon-based control of a hyperdynamic quadrotor

The maneuverability of standard quadrotors with coplanar propellers is limited by their inherent underactuation. To overcome this challenge, in-flight, active propeller tilting has been widely investigated in the literature. However, biaxial propeller tilting that renders extensive thrust vectoring has not been explored much owing to the ensuing mechanical complexities. Therefore, this paper presents an innovative design capable of achieving independent bi-axial tilting of 100° and 180° about two perpendicular axes while keeping the mechanical complexity relatively low. The developed quadrotor, aptly named QuadPlus, efficiently combines actuator redundancy and propeller rotation in a compact package, compared to its stateof-the-art counterparts. The hyperdynamic QuadPlus with a total of 12-DoFs can control its attitude independent of the position, thus enabling effective maneuvering through narrow spaces. Moreover, a novel cascade approach comprising of a high-level nonlinear model predictive control (NMPC) algorithm is adopted to obtain the optimal actuator configuration for an underdetermined system while dealing with the physical constraints. Also, proportional-integral-derivative controllers are employed at low-level to track attitude references generated by the navigation algorithm. Finally, with the help of realistic Gazebo simulations, the efficacy of the system is demonstrated by tracking complex 3-D trajectories which replicate the motion in a constrained environment. Overall, the obtained results manifest QuadPlus’s capability of achieving independent position and attitude control even with multiple actuator saturation. The Authors envision that the proposed simplistic design would stimulate interest in the community for exploring the benefits offered by bi-axial propeller tilting platforms.Ministry of Education (MOE)Accepted versionThis research is supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG69/20

Automated tuning of nonlinear model predictive controller by reinforcement learning

One of the major challenges of model predictive control (MPC) for robotic applications is the non-trivial weight tuning process while crafting the objective function. This process is often executed using the trial-and-error method by the user. Consequently, the optimality of the weights and the time required for the process become highly dependent on the skill set and experience of the user. In this study, we present a generic and user-independent framework which automates the tuning process by reinforcement learning. The proposed method shows competency in tuning a nonlinear MPC (NMPC) which is employed for trajectory tracking control of aerial robots. It explores the desirable weights within less than an hour in iterative Gazebo simulations running on a standard desktop computer. The real world experiments illustrate that the NMPC weights explored by the proposed method result in a satisfactory trajectory tracking performance.Ministry of Education (MOE)Accepted versionThis work was financially supported by the Singapore Ministry of Education (RG185/17) and Aarhus University, Department of Engineering (28173)