OPTIMAL LEADER-FOLLOWER FORMATION CONTROL USING DYNAMIC GAMES

Formation control is one of the salient features of multi-agent robotics. The main goal of this field is to develop distributed control methods for interconnected multi-robot systems so that robots will move with respect to each other in order to keep a formation throughout their joint mission. Numerous advantages and vast engineering applications have drawn a great deal of attention to the research in this field. Dynamic game theory is a powerful method to study dynamic interactions among intelligent, rational, and self-interested agents. Differential game is among the most important sub-classes of dynamic games, because many important problems in engineering can be modeled as differential games. The underlying goal of this research is to develop a reliable formation control algorithm for multi-robot systems based on differential games. The main idea is to benefit from powerful machinery provided by dynamic games, and design an improved formation control scheme with careful attention to practical control design requirements, namely state feedback, and computation costs associated to implementation. In this work, results from algebraic graph theory is used to develop a quasi-static optimal control for heterogeneous leader{follower formation problem. The simulations are provided to study capabilities as well as limitations associated to this approach. Based on the obtained results, a finite horizon open-loop Nash differential game is developed as adaptation of differential games methodology to formation control problems in multi-robot systems. The practical control design requirements dictate state-feedback; therefore, proposed controller is complimented by adding receding horizon approach to its algorithm. It leads to a closed loop state-feedback formation control. The simulation results are presented to show the effectiveness of proposed control scheme

Data-Gathering and Aggregation Protocol for Networked Carrier Ad Hoc Networks: The Optimal and Heuristic Approach

In this chapter, we address the problem of data-gathering and aggregation (DGA) in navigation carrier ad hoc networks (NC-NET), in order to reduce energy consumption and enhance network scalability and lifetime. Several clustering algorithms have been presented for vehicle ad hoc network (VANET) and other mobile ad hoc network (MANET). However, DGA approach in harsh environments, in terms of long-range transmission, high dynamic topology and three-dimensional monitor region, is still an open issue. In this chapter, we propose a novel clustering-based DGA approach, namely, distributed multiple-weight data-gathering and aggregation (DMDG) protocol, to guarantee quality of service (QoS)-aware DGA for heterogeneous services in above harsh environments. Our approach is explored by the synthesis of three kernel features. First, the network model is addressed according to specific conditions of networked carrier ad hoc networks (NC-NET), and several performance indicators are selected. Second, a distributed multiple-weight data-gathering and aggregation protocol (DMDG) is proposed, which contains all-sided active clustering scheme and realizes long-range real-time communication by tactical data link under a time-division multiple access/carrier sense multiple access (TDMA/CSMA) channel sharing mechanism. Third, an analytical paradigm facilitating the most appropriate choice of the next relay is proposed. Experimental results have shown that DMDG scheme can balance the energy consumption and extend the network lifetime notably and outperform LEACH, PEACH and DEEC in terms of network lifetime and coverage rate, especially in sparse node density or anisotropic topologies

Decentralized receding horizon control with application to multiple vehicle systems

Receding horizon control (RHC) has been one of the most popular control approaches recently due to its capability to achieve optimal performance in the presence of saturation constraints. There have been numerous new research results for RHC (also referred to as model predictive control) in the process control community. However, due to the high computational cost, associated with the numerical optimization problem, RHC has not often been successfully implemented on multiple vehicle systems with fast dynamics. Decentralized receding horizon control (DRHC) is a new promising approach to reduce the computational burden of RHC. It allows the division of the computation problem into smaller parts which are solved using a group of computational nodes. This results in a substantial reduction in the computational time required for RHC. This thesis involves modeling of wheeled and hovercraft vehicles including actuator dynamics. It then applies the DRHC approach to the vehicles and implements the DRHC systems in virtual reality simulations and an experimental setup. Together, these results establish a new and useful framework for applying RHC to multiple vehicle problems

Communication Efficiency in Information Gathering through Dynamic Information Flow

This thesis addresses the problem of how to improve the performance of multi-robot information gathering tasks by actively controlling the rate of communication between robots. Examples of such tasks include cooperative tracking and cooperative environmental monitoring. Communication is essential in such systems for both decentralised data fusion and decision making, but wireless networks impose capacity constraints that are frequently overlooked. While existing research has focussed on improving available communication throughput, the aim in this thesis is to develop algorithms that make more efficient use of the available communication capacity. Since information may be shared at various levels of abstraction, another challenge is the decision of where information should be processed based on limits of the computational resources available. Therefore, the flow of information needs to be controlled based on the trade-off between communication limits, computation limits and information value. In this thesis, we approach the trade-off by introducing the dynamic information flow (DIF) problem. We suggest variants of DIF that either consider data fusion communication independently or both data fusion and decision making communication simultaneously. For the data fusion case, we propose efficient decentralised solutions that dynamically adjust the flow of information. For the decision making case, we present an algorithm for communication efficiency based on local LQ approximations of information gathering problems. The algorithm is then integrated with our solution for the data fusion case to produce a complete communication efficiency solution for information gathering. We analyse our suggested algorithms and present important performance guarantees. The algorithms are validated in a custom-designed decentralised simulation framework and through field-robotic experimental demonstrations

Cooperative control for multi-vehicle swarms

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying su itable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle le vel that demonstrates autonomous, cooperative and optimal behaviour whilst the preserving precision and reliability at the local vehicle level

Formation control of autonomous vehicles with emotion assessment

Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks

Robust distributed control of constrained linear systems

This thesis presents new algorithms for the distributed control of a group of contrained, linear time-invariant (LTl) dynamic subsystems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Model predictive control for load frequency control of an interconnected power system.

Masters Degree. University of KwaZulu-Natal, Durban.Reliable load frequency control (LFC) is of importance in modern power system generation, transmission and distribution, it has been the basis of research on advanced control theory and application in recent years. In LFC scheme, local load disturbance, inter-area ties power fluctuation, frequency deviation, generation rate constraints (GRC), and governor dead band (GDB) are the major nonlinear factors on the control scheme that affect the dynamic response of the system to a large extent. Over the years, many methods have been designed for LFC problem of which model predictive controller (MPC) stands out due to its advantages. MPC is a control approach that simulates the feature behaviour of a system it controls and based on the result of the simulation attempt to find a control output such that the simulated system behaves optimally. When applied to LFC it copes with the perturbation. In this dissertation, robust distributed model predictive control (RDMPC) is developed as a controller scheme for LFC and is compared with a proportional integral derivative (PID) controller using MATLAB/Simulink for two-area and three-area hydro-thermal interconnected power system. From the simulation result, RDMPC significantly shows robustness over PID when compared in frequency deviation and area control error. It is observed that RDMPC still lags, from system varying dynamics and uncertainty despite its robustness over PID, hence an adaptive model predictive control (AMPC) is developed to improve on the performance of RDMPC. In order to evaluate the efficacy of this proposed controller, robustness and comparative analysis is performed using MATLAB/Simulink between the conventional PID, RDMPC, and AMPC with respect to performance indices such as settling time, undershoot and peak overshoot when subjected to frequency deviation, tie-line active power deviation, and area control error. Also, the dynamic response of the hydrothermal systems is analysed and compared in the presence of nonlinear constraints such as generator rate constraint (GRC) and governor dead band (GDB). The result from the simulation tests shows that AMPC has a better dynamic response when compared with PID, and RDMPC with a significant improvement