Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach

This letter studies a distributed collision avoidance control problem for a group of rigid bodies on a sphere. A rigid body network, consisting of multiple rigid bodies constrained to a spherical surface and an interconnection topology, is first formulated. In this formulation, it is shown that motion coordination on a sphere is equivalent to attitude coordination on the 3-dimensional Special Orthogonal group. Then, an angle-based control barrier function that can handle a geodesic distance constraint on a spherical surface is presented. The proposed control barrier function is then extended to a relative motion case and applied to a collision avoidance problem for a rigid body network operating on a sphere. Each rigid body chooses its control input by solving a distributed optimization problem to achieve a nominal distributed motion coordination strategy while satisfying constraints for collision avoidance. The proposed collision-free motion coordination law is validated via simulation

Adaptive Robot Navigation with Collision Avoidance subject to 2nd-order Uncertain Dynamics

This paper considers the problem of robot motion planning in a workspace with obstacles for systems with uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers with adaptive control techniques to guarantee the collision-free robot navigation to a predefined goal while compensating for the dynamic model uncertainties. We base our findings on sphere world-based configuration spaces, but extend our results to arbitrary star-shaped environments by using previous results on configuration space transformations. Moreover, we propose an algorithm for extending the control scheme to decentralized multi-robot systems. Finally, extensive simulation results verify the theoretical findings

Cooperative collision avoidance control and coordination for multiagent Lagrangian systems with disturbances

Multi-agent systems like a network of autonomous robots, have tremendous potential in many military and civilian applications. But, even viewed as a pure academic problem, designing controllers for such complex systems is a matter of much interest. Controller design for multi-agent system might focus on achieving several objectives, such as formation control, coverage control, consensus, target capture, pursuit evasion etc., while all at the same time aiming to be optimal in some sense, or following certain constraints imposed by the environment or communication limitations. Whatever is the objective, we always want to have a safety guarantee for the agents; the agents should avoid collisions with themselves and any static obstacles, while performing an objective. This thesis studies one such controller, which guarantees collision avoidance among the agents, in presence of bounded disturbances, while the agents carry out a coordination objective. The agents are assumed to follow a Lagrangian dynamics. The collision avoidance controller takes up the second part of the thesis. In the first part of this thesis, a particular Lagrangian system, the Raven II surgical robot, is studied in with the aim of highlighting the process of modelling and identifying such system. This is done for two reasons. One because Lagrangian dynamics is commonly used to model the agents in a multi-agent system. And second reason that motivates the modelling Raven II in part I, is to aid in future research direction pertaining to the control of Raven II

Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach

This letter studies a distributed collision avoidance control problem for a group of rigid bodies on a sphere. A rigid body network, consisting of multiple rigid bodies constrained to a spherical surface and an interconnection topology, is first formulated. In this formulation, it is shown that motion coordination on a sphere is equivalent to attitude coordination on the 3-dimensional Special Orthogonal group. Then, an angle-based control barrier function that can handle a geodesic distance constraint on a spherical surface is presented. The proposed control barrier function is then extended to a relative motion case and applied to a collision avoidance problem for a rigid body network operating on a sphere. Each rigid body chooses its control input by solving a distributed optimization problem to achieve a nominal distributed motion coordination strategy while satisfying constraints for collision avoidance. The proposed collision-free motion coordination law is validated via simulation

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty

Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations

Advances in Constrained Spacecraft Relative Motion Planning

This dissertation considers Spacecraft Relative Motion Planning (SRMP), where maneuvers are planned for one or more spacecraft to execute in close proximity to obstacles or to each other. The need for this type of maneuver planning has grown in recent years as the space environment becomes more cluttered, and the focus on space situational awareness increases. In SRMP, maneuvers must accommodate non-linear and non-convex constraints, be robust to disturbances, and be implementable on-board spacecraft with limited computational capabilities. Consequently, many standard optimization or path planning techniques cannot be directly applied to SRMP. In this dissertation, three novel SRMP techniques are developed and simulations are presented to illustrate the implementation of each method. Firstly, an invariance-based SRMP technique is proposed. Maneuvers are planned to transition a spacecraft between specified natural motion trajectories, which require no control to follow, while avoiding obstacles and accommodating minimum and maximum actuation limits. The method is based on a graph search applied to a ``virtual net'' with nodes corresponding to natural motion trajectories. Adjacency rules in the virtual net are based on safe positively invariant tubes built around each natural motion trajectory. These rules guarantee safe transitions between adjacent natural motion trajectories, even when set-bounded disturbances are present. Procedures to construct the safe positively invariant tubes and the virtual net are developed. Methods to reduce calculations are proposed and shown to significantly reduce computation time, with tradeoffs related to maneuver planning flexibility. Secondly, a SRMP technique is developed for the specific problem of satellite inspection. In this setting, an inspector spacecraft maneuvers to gather information about a target spacecraft. An information collection model is developed and used to construct a rapidly computable analytical control law based on the local gradient of the information rate. This control law drives the inspector spacecraft on a path along which the rate of information collection is strictly increasing. To ensure constraint satisfaction, the local gradient control law is combined with a state feedback control law, and rules are developed to govern switches between the two controllers. The method is shown to be effective in generating trajectories to gather information about a specified target point while accommodating disturbances. Finally, a control strategy is proposed to generate a formation containing an arbitrary number of vehicles. This strategy is based on an add-on predictive control mechanism known as a parameter governor. Parameter governors work by modifying parameters, such as gains or offsets, in a nominal closed-loop system to enforce constraints and improve performance. The parameter governor is first developed in a general setting, using generic non-linear system dynamics and an arbitrary formation design. Required calculations are minimized, and non-convex constraints are accommodated through use of a parameter update strategy based on graph colorability theory, and by limiting parameter values to a discrete set. A convergence analysis is presented, proving that under reasonable assumptions, the parameter governor is guaranteed to generate the desired formation. Two specific parameter governors, referred to as the Scale Shift Governor and Time Shift Governor, are proposed and applied to generate formations of spacecraft. These parameter governors enforce constraints by modifying either scale- or time-shifts applied to the target trajectory provided to each spacecraft in formation. Simulation case studies show the effectiveness of each method and demonstrate robustness to disturbances.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145795/1/gfrey_1.pd

Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles