Advances in PID Control

Since the foundation and up to the current state-of-the-art in control engineering, the problems of PID control steadily attract great attention of numerous researchers and remain inexhaustible source of new ideas for process of control system design and industrial applications. PID control effectiveness is usually caused by the nature of dynamical processes, conditioned that the majority of the industrial dynamical processes are well described by simple dynamic model of the first or second order. The efficacy of PID controllers vastly falls in case of complicated dynamics, nonlinearities, and varying parameters of the plant. This gives a pulse to further researches in the field of PID control. Consequently, the problems of advanced PID control system design methodologies, rules of adaptive PID control, self-tuning procedures, and particularly robustness and transient performance for nonlinear systems, still remain as the areas of the lively interests for many scientists and researchers at the present time. The recent research results presented in this book provide new ideas for improved performance of PID control applications

Selected topics in robotics for space exploration

Papers and abstracts included represent both formal presentations and experimental demonstrations at the Workshop on Selected Topics in Robotics for Space Exploration which took place at NASA Langley Research Center, 17-18 March 1993. The workshop was cosponsored by the Guidance, Navigation, and Control Technical Committee of the NASA Langley Research Center and the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE) at RPI, Troy, NY. Participation was from industry, government, and other universities with close ties to either Langley Research Center or to CIRSSE. The presentations were very broad in scope with attention given to space assembly, space exploration, flexible structure control, and telerobotics

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Applications of Mathematical Models in Engineering

The most influential research topic in the twenty-first century seems to be mathematics, as it generates innovation in a wide range of research fields. It supports all engineering fields, but also areas such as medicine, healthcare, business, etc. Therefore, the intention of this Special Issue is to deal with mathematical works related to engineering and multidisciplinary problems. Modern developments in theoretical and applied science have widely depended our knowledge of the derivatives and integrals of the fractional order appearing in engineering practices. Therefore, one goal of this Special Issue is to focus on recent achievements and future challenges in the theory and applications of fractional calculus in engineering sciences. The special issue included some original research articles that address significant issues and contribute towards the development of new concepts, methodologies, applications, trends and knowledge in mathematics. Potential topics include, but are not limited to, the following: Fractional mathematical models; Computational methods for the fractional PDEs in engineering; New mathematical approaches, innovations and challenges in biotechnologies and biomedicine; Applied mathematics; Engineering research based on advanced mathematical tools

Experimental validation of an L1 controller on a single robotic manipulator on a moving platform and a robotic cooperative network

This thesis reports on laboratory and in-field experimental results for a single robotic manipulator on a moving platform with unmodeled dynamics that aim to validate theoretical predictions for the dependence on control parameters of an L1 adaptive control strategy. The experiments verify expected bounds on the tracking error in terms of the bandwidth of a filter introduced in the control loop. Moreover, the results provide insight into different discretizations of the continuous-time formulation, suggesting that a partial discretization introduced by Cao and Hovakimyan is most suitable for a hardware implementation. A second set of experimental results, obtained from an implementation of the L1 control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays and network topology

Stability and robustness of adaptive controllers for underactuated Lagrangian systems and robotic networks

This dissertation studies the stability and robustness of an adaptive control framework for underactuated Lagrangian systems and robotic networks. In particular, an adaptive control framework is designed for a manipulator, which operates on an underactuated dynamic platform. The framework promotes the use of a filter in the control input to improve the system robustness. The characteristics of the controller are represented by two decoupled indicators. First, the adaptation gain determines the rate of adaptation, as well as the deviation between the adaptive control system and a nonadaptive reference system governing the ideal response. Second, the filter bandwidth determines the tracking performance, as well as the system robustness. The ability of the control scheme to tolerate time delay in the control loop, which is an indicator of robustness, is explored using numerical simulations, estimation of the time-delay margin of an equivalent linear, time-invariant system, and parameter continuation for Hopf bifurcation analysis. This dissertation also performs theoretical study of the delay robustness of the control framework. The analysis shows that the controller has a positive lower bound for the time-delay margin by exploring a number of properties of delay systems, especially the continuity of their solutions in the delay, uniformly in time. In particular, if the input delay is below the lower bound, then the state and control input of the closed-loop system follow those of a nonadaptive, robust reference system closely. A method for computing the lower bound for the delay robustness using a Pad\'{e} approximant is proposed. The results show that the minimum delay that destabilizes the system, which may also be estimated by forward simulation, is always larger than the value computed by the proposed method. The control framework is extended to the synchronization and consensus of networked manipulators operating on an underactuated dynamic platform in the presence of communication delays. The theoretical analysis based on input-output maps of functional differential equations shows that the adaptive control system's behavior matches closely that of a nonadaptive reference system. The tracking-synchronization objective is achieved despite the effects of communication delays and unknown dynamics of the platform. When there is no desired trajectory common to the networked manipulators, a modified controller drives all robots to a consensus configuration. A further modification is proposed that allows for the control of the constant and time-varying consensus values using a leader-follower scheme. Simulation results illustrate the performance of the proposed control algorithms

Adaptive reinforcement learning for heterogeneous network selection

Next generation 5G mobile wireless networks will consist of multiple technologies for devices to access the network at the edge. One of the keys to 5G is therefore the ability for device to intelligently select its Radio Access Technology (RAT). Current fully distributed algorithms for RAT selection although guaranteeing convergence to equilibrium states, are often slow, require high exploration times and may converge to undesirable equilibria. In this dissertation, we propose three novel reinforcement learning (RL) frameworks to improve the efficiency of existing distributed RAT selection algorithms in a heterogeneous environment, where users may potentially apply a number of different RAT selection procedures. Although our research focuses on solutions for RAT selection in the current and future mobile wireless networks, the proposed solutions in this dissertation are general and suitable to apply for any large scale distributed multi-agent systems. In the first framework, called RL with Non-positive Regret, we propose a novel adaptive RL for multi-agent non-cooperative repeated games. The main contribution is to use both positive and negative regrets in RL to improve the convergence speed and fairness of the well-known regret-based RL procedure. Significant improvements in performance compared to other related algorithms in the literature are demonstrated. In the second framework, called RL with Network-Assisted Feedback (RLNF), our core contribution is to develop a network feedback model that uses network-assisted information to improve the performance of the distributed RL for RAT selection. RLNF guarantees no-regret payoff in the long-run for any user adopting it, regardless of what other users might do and so can work in an environment where not all users use the same learning strategy. This is an important implementation advantage as RLNF can be implemented within current mobile network standards. In the third framework, we propose a novel adaptive RL-based mechanism for RAT selection that can effectively handle user mobility. The key contribution is to leverage forgetting methods to rapidly react to the changes in the radio conditions when users move. We show that our solution improves the performance of wireless networks and converges much faster when users move compared to the non-adaptive solutions. Another objective of the research is to study the impact of various network models on the performance of different RAT selection approaches. We propose a unified benchmark to compare the performances of different algorithms under the same computational environment. The comparative studies reveal that among all the important network parameters that influence the performance of RAT selection algorithms, the number of base stations that a user can connect to has the most significant impact. This finding provides some guidelines for the proper design of RAT selection algorithms for future 5G. Our evaluation benchmark can serve as a reference for researchers, network developers, and engineers. Overall, the thesis provides different reinforcement learning frameworks to improve the efficiency of current fully distributed algorithms for heterogeneous RAT selection. We prove the convergence of the proposed reinforcement learning procedures using the differential inclusion (DI) technique. The theoretical analyses demonstrate that the use of DI not only provides an effective method to study the convergence properties of adaptive procedures in game-theoretic learning, but also yields a much more concise and extensible proof as compared to the classical approaches.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

An Interactive and Dynamic Search-Based Approach to Software Refactoring Recommendations

Successful software products evolve through a process of continual change. However, this process may weaken the design of the software and make it unnecessarily complex, leading to significantly reduced productivity and increased fault-proneness. Refactoring improves the software design while preserving overall functionality and behavior, and is an important technique in managing the growing complexity of software systems. Most of the existing work on software refactoring uses either an entirely manual or a fully automated approach. Manual refactoring is time-consuming, error-prone and unsuitable for large-scale, radical refactoring. On the other hand, fully automated refactoring yields a static list of refactorings which, when applied, leads to a new and often hard to comprehend design. Furthermore, it is difficult to merge these refactorings with other changes performed in parallel by developers. In this paper, we propose a refactoring recommendation approach that dynamically adapts and interactively suggests refactorings to developers and takes their feedback into consideration. Our approach uses NSGA-II to find a set of good refactoring solutions that improve software quality while minimizing the deviation from the initial design. These refactoring solutions are then analyzed to extract interesting common features between them such as the frequently occurring refactorings in the best non-dominated solutions. Based on this analysis, the refactorings are ranked and suggested to the developer in an interactive fashion as a sequence of transformations. The developer can approve, modify or reject each of the recommended refactorings, and this feedback is then used to update the proposed rankings of recommended refactorings. After a number of introduced code changes and interactions with the developer, the interactive NSGA-II algorithm is executed again on the new modified system to repair the set of refactoring solutions based on the new changes and the feedback received from the developer. We evaluated our approach on a set of eight open source systems and two industrial projects provided by an industrial partner. Statistical analysis of our experiments shows that our dynamic interactive refactoring approach performed significantly better than four existing search-based refactoring techniques and one fully-automated refactoring tool not based on heuristic search

Formation Flight in Dense Environments

Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method which adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the swarm global planning and local trajectory optimizations efficiently. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.Comment: Submitted for IEEE Transactions on Robotic