A Robust controller for micro-sized agents: The prescribed performance approach

Applications such as micromanipulation and minimally invasive surgery can be performed using micro-sized agents. For instance, drug-loaded magnetic micro-/nano- particles can enable targeted drug delivery. Their precise manipulation can be assured using a robust motion controller. In this paper, we design a closed-loop controller-observer pair for regulating the position of microagents. The prescribed performance technique is applied to control the microagents to follow desired motion trajectories. The position of the microagents are obtained using microscopic images and image processing. The velocities of the microagents are obtained using an iterative learning observer. The algorithm is tested experimentally on spherical magnetic microparticles that have an average diameter of 100 m. The steady-state errors obtained by the algorithm are 20 m. The errors converge to the steady-state in approximately 8 second

Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions

The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while ensuring stability guarantees. In this paper we present the first real-time realization of a unified NMPC and CLF controller on a robotic system with limited computational resources. These limitations motivate a set of approaches for efficiently incorporating CLF stability constraints into a general NMPC formulation. We evaluate the performance of the proposed methods compared to baseline CLF and NMPC controllers with a robotic Segway platform both in simulation and on hardware. The addition of a prediction horizon provides a performance advantage over CLF based controllers, which operate optimally point-wise in time. Moreover, the explicitly imposed stability constraints remove the need for difficult cost function and parameter tuning required by NMPC. Therefore the unified controller improves the performance of each isolated controller and simplifies the overall design process

Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions

The theoretical unification of Nonlinear Model Predictive Control (NMPC) with Control Lyapunov Functions (CLFs) provides a framework for achieving optimal control performance while ensuring stability guarantees. In this paper we present the first real-time realization of a unified NMPC and CLF controller on a robotic system with limited computational resources. These limitations motivate a set of approaches for efficiently incorporating CLF stability constraints into a general NMPC formulation. We evaluate the performance of the proposed methods compared to baseline CLF and NMPC controllers with a robotic Segway platform both in simulation and on hardware. The addition of a prediction horizon provides a performance advantage over CLF based controllers, which operate optimally point-wise in time. Moreover, the explicitly imposed stability constraints remove the need for difficult cost function and parameter tuning required by NMPC. Therefore the unified controller improves the performance of each isolated controller and simplifies the overall design process

Experimental evaluation of some classical and adaptive iterative learning control schemes on a 5DOF robot manipulator

In many process industries (e.g., VLSI production lines, Automotive industries, IC welding process, inspections, manipulations), robot manipulators are used to perform the same tasks repeatedly over a finite time interval. The ultimate goal of robotic research is to design intelligent and autonomous robot control systems to perform repetitive tasks that are dull, hazardous, or require skill beyond the capability of humans. The nonlinear nature of the robot dynamics has made this problem a challenging one in robotics research. This highly demanding control problem of driving an industrial robot to follow a desired trajectory perfectly under constrained or unconstrained environment has led to the application of sophisticated control techniques. From the classical or modern control view point, it is a very difficult task to design an intelligent robot control system that can achieve perfect tracking over a finite time interval due to the effect of highly coupled robot dynamics and the presence of the unmodeled dynamics such as friction and backlash that are usually exhibited in the robot system during actual operation

Safe Robot Planning and Control Using Uncertainty-Aware Deep Learning

In order for robots to autonomously operate in novel environments over extended periods of time, they must learn and adapt to changes in the dynamics of their motion and the environment. Neural networks have been shown to be a versatile and powerful tool for learning dynamics and semantic information. However, there is reluctance to deploy these methods on safety-critical or high-risk applications, since neural networks tend to be black-box function approximators. Therefore, there is a need for investigation into how these machine learning methods can be safely leveraged for learning-based controls, planning, and traversability. The aim of this thesis is to explore methods for both establishing safety guarantees as well as accurately quantifying risks when using deep neural networks for robot planning, especially in high-risk environments. First, we consider uncertainty-aware Bayesian Neural Networks for adaptive control, and introduce a method for guaranteeing safety under certain assumptions. Second, we investigate deep quantile regression learning methods for learning time-and-state varying uncertainties, which we use to perform trajectory optimization with Model Predictive Control. Third, we introduce a complete framework for risk-aware traversability and planning, which we use to enable safe exploration of extreme environments. Fourth, we again leverage deep quantile regression and establish a method for accurately learning the distribution of traversability risks in these environments, which can be used to create safety constraints for planning and control.Ph.D

On Iterative Learning in Multi-agent Systems Coordination and Control

Ph.DDOCTOR OF PHILOSOPH

Advances in Spacecraft Systems and Orbit Determination

"Advances in Spacecraft Systems and Orbit Determinations", discusses the development of new technologies and the limitations of the present technology, used for interplanetary missions. Various experts have contributed to develop the bridge between present limitations and technology growth to overcome the limitations. Key features of this book inform us about the orbit determination techniques based on a smooth research based on astrophysics. The book also provides a detailed overview on Spacecraft Systems including reliability of low-cost AOCS, sliding mode controlling and a new view on attitude controller design based on sliding mode, with thrusters. It also provides a technological roadmap for HVAC optimization. The book also gives an excellent overview of resolving the difficulties for interplanetary missions with the comparison of present technologies and new advancements. Overall, this will be very much interesting book to explore the roadmap of technological growth in spacecraft systems

Development and applications of new sliding mode control approaches

Ph.DDOCTOR OF PHILOSOPH