Development of Robust Control Strategies for Autonomous Underwater Vehicles

The resources of the energy and chemical balance in the ocean sustain mankind in many ways. Therefore, ocean exploration is an essential task that is accomplished by deploying Underwater Vehicles. An Underwater Vehicle with autonomy feature for its navigation and control is called Autonomous Underwater Vehicle (AUV). Among the task handled by an AUV, accurately positioning itself at a desired position with respect to the reference objects is called set-point control. Similarly, tracking of the reference trajectory is also another important task. Battery recharging of AUV, positioning with respect to underwater structure, cable, seabed, tracking of reference trajectory with desired accuracy and speed to avoid collision with the guiding vehicle in the last phase of docking are some significant applications where an AUV needs to perform the above tasks. Parametric uncertainties in AUV dynamics and actuator torque limitation necessitate to design robust control algorithms to achieve motion control objectives in the face of uncertainties. Sliding Mode Controller (SMC), H / μ synthesis, model based PID group controllers are some of the robust controllers which have been applied to AUV. But SMC suffers from less efficient tuning of its switching gains due to model parameters and noisy estimated acceleration states appearing in its control law. In addition, demand of high control effort due to high frequency chattering is another drawback of SMC. Furthermore, real-time implementation of H / μ synthesis controller based on its stability study is restricted due to use of linearly approximated dynamic model of an AUV, which hinders achieving robustness. Moreover, model based PID group controllers suffer from implementation complexities and exhibit poor transient and steady-state performances under parametric uncertainties. On the other hand model free Linear PID (LPID) has inherent problem of narrow convergence region, i.e.it can not ensure convergence of large initial error to zero. Additionally, it suffers from integrator-wind-up and subsequent saturation of actuator during the occurrence of large initial error. But LPID controller has inherent capability to cope up with the uncertainties. In view of addressing the above said problem, this work proposes wind-up free Nonlinear PID with Bounded Integral (BI) and Bounded Derivative (BD) for set-point control and combination of continuous SMC with Nonlinear PID with BI and BD namely SM-N-PID with BI and BD for trajectory tracking. Nonlinear functions are used for all P,I and D controllers (for both of set-point and tracking control) in addition to use of nonlinear tan hyperbolic function in SMC(for tracking only) such that torque demand from the controller can be kept within a limit. A direct Lyapunov analysis is pursued to prove stable motion of AUV. The efficacies of the proposed controllers are compared with other two controllers namely PD and N-PID without BI and BD for set-point control and PD plus Feedforward Compensation (FC) and SM-NPID without BI and BD for tracking control. Multiple AUVs cooperatively performing a mission offers several advantages over a single AUV in a non-cooperative manner; such as reliability and increased work efficiency, etc. Bandwidth limitation in acoustic medium possess challenges in designing cooperative motion control algorithm for multiple AUVs owing to the necessity of communication of sensors and actuator signals among AUVs. In literature, undirected graph based approach is used for control design under communication constraints and thus it is not suitable for large number of AUVs participating in a cooperative motion plan. Formation control is a popular cooperative motion control paradigm. This thesis models the formation as a minimally persistent directed graph and proposes control schemes for maintaining the distance constraints during the course of motion of entire formation. For formation control each AUV uses Sliding Mode Nonlinear PID controller with Bounded Integrator and Bounded Derivative. Direct Lyapunov stability analysis in the framework of input-to-state stability ensures the stable motion of formation while maintaining the desired distance constraints among the AUVs

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Consensus control in robot networks and cooperative teleoperation : an operational space approach

An interesting approach in cooperative control is to design distributed control strategies which use only local information so that a multi-agent system achieves specified behaviors. A basic behavior in cooperative control is the consensus. Given a multi-agent system, like a multiple robot network, it is said that the agents reach a consensus if the state of each agent converges to a common state. Examples of cooperative tasks in which consensus algorithms are employed include formation control, flocking theory, rendezvous problems and synchronization. These cooperative tasks have several possible applications, like: transportation systems (intelligent highways, air-traffic control); military systems (formation flight, surveillance, reconnaissance, cooperative attack and rendezvous) and mobile sensor networks (space-based interferometers, environmental sampling). The solution to the consensus problems involves the design of control algorithms such that the agents can reach an agreement on their states. There are two main problems that are studied in consensus, the leader-follower consensus and the leaderless consensus. In the leader-follower consensus problem, there exists a leader that specifies the state for the whole group while in a leaderless consensus problem, there is not a priori reference state. The main goal of this thesis is the design of operational space controllers that solve the leader-follower and the leaderless consensus problems in networks composed of multiple heterogeneous robots. Furthermore, this document proposes novel operational space control schemes for bilateral teleoperation systems. In both scenarios, different conditions are studied, such as the absence of robot velocity measurements, constant and variable time-delays in the robot's interconnection, and uncertainty in the robot's physical parameters. Most of the previous consensus control algorithms, only work with the position or orientation but not with both. On the contrary, this dissertation deals with the entire pose of the robots that contains both the position and the orientation. Moreover, in order to render a singularity-free description of the orientation, the unit-quaternions are employed. The dissertation provides a rigorous stability analysis of the control algorithms and presents simulations and experiments that validate the effectiveness of the proposed controllers.Un enfoque interesante en el control cooperativo es el diseño de estrategias de control distribuido que requieran sólo información local para que un sistema multi-agente logre comportamientos específicos. Un comportamiento básico del control cooperativo es el consenso. Dado un sistema multi-agente, como una red de múltiples robots, se dice que los agentes llegan a un consenso si el estado de cada agente converge a un estado común. Algunos ejemplos de tareas cooperativas en las que los algoritmos de consenso son utilizados son los siguientes: el control de la formación, flocking, rendezvous y sincronización. Estas tareas cooperativas tienen varias aplicaciones posibles, como: sistemas de transporte (carreteras inteligentes , control de tráfico aéreo); sistemas militares (vuelo en formación, vigilancia, reconocimiento, ataque cooperativo) y redes de sensores móviles (interferómetros en el espacio, el muestreo del ambiente). La solución a los problemas de consenso implica el diseño de algoritmos de control de tal manera que los agentes pueden llegar a un acuerdo sobre sus estados. Hay dos problemas principales que se estudian en el consenso, el consenso líder-seguidor y el consenso sin líder. En el problema de consenso líder-seguidor, existe un líder que especifica el estado de todo el grupo, mientras que en un problema de consenso sin líder, no hay ningún estado de referencia definido a priori. El objetivo principal de esta tesis es el diseño de controladores en el espacio operacional que resuelvan los problemas de consenso líder-seguidor y sin líder en redes compuestas de múltiples robots heterogéneos. Además, este documento propone novedosos esquemas de control en el espacio operacional para sistemas de teleoperación bilateral. En ambos escenarios, se estudian diferentes condiciones, tales como la ausencia de medidas de velocidad de los robots, retardos constantes y variables en la interconexión de los robots y la incertidumbre en los parámetros físicos de los robots. La mayoría de los anteriores algoritmos de control que resuelven el consenso, sólo trabajan con la posición o la orientación, pero no con ambos. Por el contrario, esta tesis doctoral se ocupa de toda la pose de los robots que contiene tanto la posición y la orientación. Por otra parte, a fin de usar una representación de la orientación libre de singularidades, se emplean los cuaterniones unitarios. Esta tesis doctoral proporciona un análisis riguroso de la estabilidad de los algoritmos de control y presenta simulaciones y experimentos que validan la eficacia de los controladores propuesto

Distributed Tracking Control Design for Leader-Follower Multi-Agent Systems

Multi-agent systems (MASs) have been widely recognized as a key way to model, analyze, and engineer numerous kinds of complex systems composed of distributed agents. The aim of this dissertation is to study control design for leader-follower MASs such that a group of followers can track a specified leader via distributed decision making based on distributed information. We identify and consider several critical problems that have stood in the way of distributed tracking control synthesis and analysis. Specifically, they include: 1) limited information access by the followers to the leader, 2) effects of external disturbances, 3) complicated dynamics of agents, and 4) energy efficiency. To overcome the first three problems, we take a lead with the design of distributed-observer-based control, with the insight that distributed observers can enable agents to recover unknown quantities in a collective manner for the purpose of control. To deal with the fourth problem, we propose the first study of MAS tracking control conscious of nonlinear battery dynamics to increase operation time and range. The dissertation will present the following research contributions. First, we propose the notion of designing distributed observers to make all the followers aware of the leader's state and driving input, regardless of the network communication topology, and perform tracking controller design based on the observers. Second, we further develop distributed disturbance observers and observer-based robust tracking control to handle the scenario when all the leader and followers are affected by unknown disturbances only bounded in rates of change. The third contribution lies in treating a leader-follower MAS with high-order, nonlinear dynamics. Assuming the availability of very limited measurement data, we substantively expand the idea of observer-based control to develop a catalog of distributed observers such that the followers can reconstruct large amounts of information necessary for effective tracking control. Finally, we propose a distributed predictive optimization method to integrate onboard battery management with tracking control for long-endurance operation of an electric-powered MAS. The proposed dissertation research offers new insights and a set of novel tools to enhance the control performance of leader-follower MASs. The results also have a promise to find potential applications in other types of MASs

On the Robust Control and Optimization Strategies for Islanded Inverter-Based Microgrids

In recent years, the concept of Microgrids (MGs) has become more popular due to a significant integration of renewable energy sources (RESs) into electric power systems. Microgrids are small-scale power grids consisting of localized grouping of heterogeneous Distributed Generators (DGs), storage systems, and loads. MGs may operate either in autonomous islanded mode or connected to the main power system. Despite the significant benefits of increasing RESs, many new challenges raise in controlling MGs. Hence, a three layered hierarchical architecture consisting of three control loops closed on the DGs dynamics has been introduced for MGs. The inner loop is called Primary Control (PC), and it provides the references for the DG’s DC-AC power converters. In general, the PC is implemented in a decentralized way with the aim to establish, by means of a droop control term, the desired sharing of power among DGs while preserving the MG stability. Then, because of inverterbased DGs have no inertia, a Secondary Control (SC) layer is needed to compensate the frequency and voltage deviations introduced by the PC’s droop control terms. Finally, an operation control is designed to optimize the operation of the MGs by providing power setpoints to the lower control layers. This thesis is mainly devoted to the design of robust distributed secondary frequency and voltage restoration control strategies for AC MGs to avoid central controllers and complexity of communication networks. Different distributed strategies are proposed in this work: (i) Robust Adaptive Distributed SC with Communication delays (ii) Robust Optimal Distributed Voltage SC with Communication Delays and (iii) Distributed Finite-Time SC by Coupled Sliding-Mode Technique. In all three proposed approaches, the problem is addressed in a multi-agent fashion where the generator plays the role of cooperative agents communicating over a network and physically coupled through the power system. The first approach provides an exponentially converging voltage and frequency restoration rate in the presence of both, model uncertainties, and multiple time-varying delays in the DGs’s communications. This approach consist of two terms: 1) a decentralized Integral Sliding Mode Control (ISMC) aimed to enforce each agent (DG) to behaves as reference unperturbed dynamic; 2) an ad-hoc designed distributed protocol aimed to globally, exponentially, achieves the frequency and voltage restoration while fulfilling the power-sharing constraints in spite of the communication delays. The second approach extends the first one by including an optimization algorithm to find the optimal control gains and estimate the corresponding maximum delay tolerated by the controlled system. In the third approach, the problem of voltage and frequency restoration as well as active power sharing are solved in finite-time by exploiting delay-free communications among DGs and considering model uncertainties. In this approach, for DGs with no direct access to their reference values, a finite-time distributed sliding mode estimator is implemented for both secondary frequency and voltage schemes. The estimator determines local estimates of the global reference values of the voltage and frequency for DGs in a finite time and provides this information for the distributed SC schemes. This dissertation also proposes a novel certainty Model Predictive Control (MPC) approach for the operation of islanded MG with very high share of renewable energy sources. To this aim, the conversion losses of storage units are formulated by quadratic functions to reduce the error in storage units state of charge prediction