Formation control of mobile robots and unmanned aerial vehicles

In this dissertation, the nonlinear control of nonholonomic mobile robot formations and unmanned aerial vehicle (UAV) formations is undertaken and presented in six papers. In the first paper, an asymptotically stable combined kinematic/torque control law is developed for leader-follower based formation control of mobile robots using backstepping. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. Subsequently, in the second paper, a novel NN observer is designed to estimate the linear and angular velocities of both the follower and its leader robot and a NN output feedback control law is developed. On the other hand, in the third paper, a NN-based output feedback control law is presented for the control of an underactuated quad rotor UAV, and a NN virtual control input scheme is proposed which allows all six degrees of freedom to be controlled using only four control inputs. The results of this paper are extended to include the control of quadrotor UAV formations, and a novel three-dimensional leader-follower framework is proposed in the fourth paper. Next, in the fifth paper, the discrete-time nonlinear optimal control is undertaken using two online approximators (OLA\u27s) to solve the infinite horizon Hamilton-Jacobi-Bellman (HJB) equation forward-in-time to achieve nearly optimal regulation and tracking control. In contrast, paper six utilizes a single OLA to solve the infinite horizon HJB and Hamilton-Jacobi-Isaacs (HJI) equations forward-intime for the near optimal regulation and tracking control of continuous affine nonlinear systems. The effectiveness of the optimal tracking controllers proposed in the fifth and sixth papers are then demonstrated using nonholonomic mobile robot formation control --Abstract, page iv

Control and coordination for a group of mobile robots in unknown environments

This thesis studies the trajectory tracking and cooperative behavior for a team of mobile robots using nonlinear and intelligent algorithms to more efficiently achieve the mission outcome. There are many practical applications where specific tasks are more resourcefully achieved by using a group of mobile robots rather than a single robot. Mobile robots can subdivide and multi-task the mission with speed and accuracy and the ability to be individually modified for precise tasks makes them ideally suited for applications such as search and rescue, exploration or entertainment. When comparing the mission outcome of a group of multi mobile robots (MMR) to that of a single robot, we see that the performance of the MMR group improves the specific task allocation, safety, the time duration required and the system effectiveness to achieve the outcome. In order to create the most effective control algorithm for trajectory tracking, we present three different techniques including Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. The developed algorithms instruct a robot to keep moving on their desired trajectory while simultaneously reducing tracking errors. The experimental results when using a single mobile robot are presented to demonstrate the potential and capability of the developed algorithms. In order to coordinate a group of mobile robots to achieve a common outcome, the goal is to create efficient system architecture and a control algorithm that enables them to work both individually and in meaningful robot formations. This is achieved by employing coordination and trajectory tracking techniques with the knowledge derived by the localization of the robots from their environment. Three different hierarchical controllers are presented based on nonlinear and intelligent techniques in order to construct an algorithm that exhibits both group cooperation and coordination for a team of mobile robots. These controllers consist of Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. For improved trajectory tracking, each robot is fitted with onboard sensors. When an obstacle is detected by any of the robots’ sensors, they direct that robot to move around the obstacle by changing its velocity and direction. As well as obstacle avoidance, the controllers work to make the MMR group arrive concurrently at their target points by adjusting each of the individual robots’ velocities as they move along their desired trajectories. This means the group will arrive at their destination within the same time duration, regardless of the length of each individual trajectory or number of obstacles that confront each robot. The experimental results obtained using three mobile robots display the performance of these control algorithms in producing a cooperative and coordinated behavior for the robot group

Behavior-based model predictive control for networked multi-agent systems

We present a motion control framework which allows a group of robots to work together to decide upon their motions by minimizing a collective cost without any central computing component or any one agent performing a large portion of the computation. When developing distributed control algorithms, care must be taken to respect the limited computational capacity of each agent as well as respect the information and communication constraints of the network. To address these issues, we develop a distributed, behavior-based model predictive control (MPC) framework which alleviates the computational difficulties present in many distributed MPC frameworks, while respecting the communication and information constraints of the network. In developing the multi-agent control framework, we make three contributions. First, we develop a distributed optimization technique which respects the dynamic communication restraints of the network, converges to a collective minimum of the cost, and has transients suitable for robot motion control. Second, we develop a behavior-based MPC framework to control the motion of a single-agent and apply the framework to robot navigation. The third contribution is to combine the concepts of distributed optimization and behavior-based MPC to develop the mentioned multi-agent behavior-based MPC algorithm suitable for multi-robot motion control.Ph.D

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Bio-inspired robotic control in underactuation: principles for energy efficacy, dynamic compliance interactions and adaptability.

Biological systems achieve energy efficient and adaptive behaviours through extensive autologous and exogenous compliant interactions. Active dynamic compliances are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. Underactuated systems with viscoelastic property are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment. One important question to raise is: How can we design control systems to achieve efficient locomotion, while adapt to dynamic conditions as the living systems do? In this thesis, a trajectory planning algorithm is developed for underactuated microrobotic systems with bio-inspired self-propulsion and viscoelastic property to achieve synchronized motion in an energy efficient, adaptive and analysable manner. The geometry of the state space of the systems is explicitly utilized, such that a synchronization of the generalized coordinates is achieved in terms of geometric relations along the desired motion trajectory. As a result, the internal dynamics complexity is sufficiently reduced, the dynamic couplings are explicitly characterised, and then the underactuated dynamics are projected onto a hyper-manifold. Following such a reduction and characterization, we arrive at mappings of system compliance and integrable second-order dynamics with the passive degrees of freedom. As such, the issue of trajectory planning is converted into convenient nonlinear geometric analysis and optimal trajectory parameterization. Solutions of the reduced dynamics and the geometric relations can be obtained through an optimal motion trajectory generator. Theoretical background of the proposed approach is presented with rigorous analysis and developed in detail for a particular example. Experimental studies are conducted to verify the effectiveness of the proposed method. Towards compliance interactions with the environment, accurate modelling or prediction of nonlinear friction forces is a nontrivial whilst challenging task. Frictional instabilities are typically required to be eliminated or compensated through efficiently designed controllers. In this work, a prediction and analysis framework is designed for the self-propelled vibro-driven system, whose locomotion greatly relies on the dynamic interactions with the nonlinear frictions. This thesis proposes a combined physics-based and analytical-based approach, in a manner that non-reversible characteristic for static friction, presliding as well as pure sliding regimes are revealed, and the frictional limit boundaries are identified. Nonlinear dynamic analysis and simulation results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements. The thesis also performs elaborative studies on trajectory tracking. Control schemes are designed and extended for a class of underactuated systems with concrete considerations on uncertainties and disturbances. They include a collocated partial feedback control scheme, and an adaptive variable structure control scheme with an elaborately designed auxiliary control variable. Generically, adaptive control schemes using neural networks are designed to ensure trajectory tracking. Theoretical background of these methods is presented with rigorous analysis and developed in detail for particular examples. The schemes promote the utilization of linear filters in the control input to improve the system robustness. Asymptotic stability and convergence of time-varying reference trajectories for the system dynamics are shown by means of Lyapunov synthesis

Adaptive Formation Control of Cooperative Multi-Vehicle Systems

The literature comprises many approaches and results for the formation control of multi-vehicle systems; however, the results established for the cases where the vehicles contain parametric uncertainties are limited. Motivated by the need for explicit characterization of the effects of uncertainties on multi-vehicle formation motions, we study distributed adaptive formation control of multi-vehicle systems in this thesis, focusing on different interrelated sub-objectives. We first examine the cohesive motion control problem of minimally persistent formations of autonomous vehicles. Later, we consider parametric uncertainties in vehicle dynamics in such autonomous vehicle formations. Following an indirect adaptive control approach and exploiting the features of the certainty equivalence principle, we propose control laws to solve maneuvering problem of the formations, robust to parametric modeling uncertainties. Next, as a formation acquisition/closing ranks problem, we study the adaptive station keeping problem, which is defined as positioning an autonomous mobile vehicle $A$ inside a multi-vehicle network, having specified distances from the existing vehicles of the network. In this setting, a single-integrator model is assumed for the kinematics for the vehicle $A$, and $A$ is assumed to have access to only its own position and its continuous distance measurements to the vehicles of the network. We partition the problem into two sub-problems; localization of the existing vehicles of the network using range-only measurements and motion control of $A$ to its desired location within the network with respect to other vehicles. We design an indirect adaptive control scheme, provide formal stability and convergence analysis and numerical simulation results, demonstrating the characteristics and performance of the design. Finally, we study re-design of the proposed station keeping scheme for the more challenging case where the vehicle $A$ has non-holonomic motion dynamics and does not have access to its self-location information. Overall, the thesis comprises methods and solutions to four correlated formation control problems in the direction of achieving a unified distributed adaptive formation control framework for multi-vehicle systems

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

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