Networked and event-triggered control systems

In this thesis, control algorithms are studied that are tailored for platforms with limited computation and communication resources. The interest in such control algorithms is motivated by the fact that nowadays control algorithms are implemented on small and inexpensive embedded microprocessors and that the sensors, actuators and controllers are connected through multipurpose communication networks. To handle the fact that computation power is no longer abundant and that communication networks do not have in finite bandwidth, the control algorithms need to be either robust for the deficiencies induced by these constraints, or they need to optimally utilise the available computation and communication resources. In this thesis, methodologies for the design and analysis of control algorithms with such properties are developed. Networked Control Systems: In the first part of the thesis, so-called networked control systems (NCSs) are studied. The control algorithms studied in this part of the thesis can be seen as conventional sampled-data controllers that need to be robust against the artefacts introduced by using a finite bandwidth communication channel. The network-induced phenomena that are considered in this thesis are time-varying transmission intervals, time-varying delays, packet dropouts and communication constraints. The latter phenomenon causes that not all sensor and actuator data can be transmitted simultaneously and, therefore, a scheduling protocol is needed to orchestrate when to transmit what data over the network. To analyse the stability of the NCSs, a discrete-time modelling framework is presented and, in particular, two cases are considered: in the first case, the transmission intervals and delays are assumed to be upper and lower bounded, and in the second case, they are described by a random process, satisfying a continuous joint probability distribution. Both cases are relevant. The former case requires a less detailed description of the network behaviour than the latter case, while the latter results in a less conservative stability analysis than the former. This allows to make a tradeoff between modelling accuracy (of network-induced effects) and conservatism in the stability analysis. In both cases, linear plants and controllers are considered and the NCS is modelled as a discrete-time switched linear parameter-varying system. To assess the stability of this system, novel polytopic overapproximations are developed, which allows the stability of the NCS to be studied using a finite number of linear matrix inequalities. It will be shown that this approach reduces conservatism significantly with respect to existing results in the literature and allows for studying larger classes of controllers, including discrete-time dynamical output-based controllers. Hence, the main contribution of this part of the thesis is the development of a new and general framework to analyse the stability of NCSs subject to four network-induced phenomena in a hardly conservative manner. Event-Triggered Control Systems: In the second part of the thesis, socalled event-triggered control (ETC) systems are studied. ETC is a control strategy in which the control task is executed after the occurrence of an external event, rather than the elapse of a certain period of time as in conventional periodic control. In this way, ETC can be designed to only provide control updates when needed and, thereby, to optimally utilise the available computation and communication resources. This part of the thesis consists of three main contributions in this appealing area of research. The first contribution is the extension of the existing results on ETC towards dynamical output-based feedback controllers, instead of state-feedback control, as is common in the majority of the literature on ETC. Furthermore, extensions towards decentralised event triggering are presented. These extensions are important for practical implementations of ETC, as in many control applications the full state is hardly ever available for feedback, and sensors and actuators are often physically distributed, which prohibits the use of centralised event-triggering conditions. To study the stability and the L1-performance of this ETC system, a modelling framework based on impulsive systems is developed. Furthermore, for the novel output-based decentralised event-triggering conditions that are proposed, it is shown how nonzero lower bounds on the minimum inter-event times can be guaranteed and how they can be computed. The second contribution is the proposition of the new class of periodic event-triggered control (PETC) algorithms, where the objective is to combine the benefits that, on the one hand, periodic control and, on the other hand, ETC offer. In PETC, the event-triggering condition is monitored periodically and at each sampling instant it is decided whether or not to transmit the data and to use computation resources for the control task. Such an event-triggering condition has several benefits, including the inherent existence of a minimum inter-event time, which can be tuned directly. Furthermore, the fact that the event-triggering condition is only verified at the periodic sampling times, instead of continuously, makes it possible to implement this strategy in standard time-sliced embedded software architectures. To analyse the stability and the L2-performance for these PETC systems, methodologies based on piecewiselinear systems models and impulsive system models will be provided, leading to an effective analysis framework for PETC. Finally, a novel approach to solving the codesign problem of both the feedback control algorithm and the event-triggering condition is presented. In particular, a novel way to solve the minimum attention and anytime attention control problems is proposed. In minimum attention control, the `attention' that a control task requires is minimised, and in anytime attention control, the performance under the `attention' given by a scheduler is maximised. In this context, `attention' is interpreted as the inverse of the time elapsed between two consecutive executions of a control task. The two control problems are solved by formulating them as linear programs, which can be solved efficiently in an online fashion. This offers a new and elegant way to solve both the minimum attention control problem and the anytime attention control problem in one unifying framework. The contributions presented in this thesis can form a basis for future research explorations that can eventually lead to a mature system theory for both NCSs and ETC systems, which are indispensable for the deployment of NCSs and ETC systems in a large variety of practical control applications

Measurements, Models, Systems and Design

531 s.

Autonomous system control in unknown operating conditions

Autonomous systems have become an interconnected part of everyday life with the recent increases in computational power available for both onboard computers and offline data processing. The race by car manufacturers for level 5 (full) autonomy in self-driving cars is well underway and new flying taxi service startups are emerging every week, attracting billions in investments. Two main research communities, Optimal Control and Reinforcement Learning stand out in the field of autonomous systems, each with a vastly different perspective on the control problem. Controllers from the optimal control community are based on models and can be rigorously analyzed to ensure the stability of the system is maintained under certain operating conditions. Learning-based control strategies are often referred to as model-free and typically involve training a neural network to generate the required control actions through direct interactions with the system. This greatly reduces the design effort required to control complex systems. One common problem both learning- and model- based control solutions face is the dependency on a priori knowledge about the system and operating conditions such as possible internal component failures and external environmental disturbances. It is not possible to consider every possible operating scenario an autonomous system can encounter in the real world at design time. Models and simulators are approximations of reality and can only be created for known operating conditions. Autonomous system control in unknown operating conditions, where no a priori knowledge exists, is still an open problem for both communities and no control methods currently exist for such situations. Multiple model adaptive control is a modular control framework that divides the control problem into supervisory and low-level control, which allows for the combination of existing learning- and model-based control methods to overcome the disadvantages of using only one of these. The contributions of this thesis consist of five novel supervisory control architectures, which have been empirically shown to improve a system’s robustness to unknown operating conditions, and a novel low- level controller tuning algorithm that can reduce the number of required controllers compared to traditional tuning approaches. The presented methods apply to any autonomous system that can be controlled using model-based controllers and can be integrated alongside existing fault-tolerant control systems to improve robustness to unknown operating conditions. This impacts autonomous system designers by providing novel control mechanisms to improve a system’s robustness to unknown operating conditions

Distributed time-critical coordination strategies for unmanned aerial systems in cluttered environments

This thesis addresses the problem of cooperative motion planning and control for a group of cooperating unmanned aerial systems through cluttered and uncertain environments, subject to a broad range of coordination and temporal constraints. The proposed solution expands the type of time-critical missions that can be automated using cooperative motion control frameworks. This work introduces the use of novel geometric queries to aid a sample-based motion-planning algorithm guide the growth of a rapidly-exploring random tree through the narrow passages in cluttered and uncertain scenarios. To this effect, specific silhouette and tolerance verification queries are designed for the geometric objects that represent vehicle motion and environmental obstacles. The combination of the silhouette-informed path planner with a CNC-inspired path-smoothing method, and a centralized cooperative speed-assignment algorithm yields a set of C2 continuous trajectories that maintain safe separation with all uncertain obstacles and cooperating peers, meet desired mission constraints, and satisfy a set of simplified dynamic constraints. The vehicles are then tasked to follow their assigned paths and coordinate online to meet mission objectives, desired inter-agent spacing constraints, and temporal constraints—such as a time of arrival or a window of arrival. The thesis introduces two types of inter-agent spacing constraints—tight and loose coordination—and three types of temporal constraints—unenforced, relaxed, and strict—that result in six general time-critical coordination strategies. This thesis presents six distributed coordination protocols to enforce this range of constraints. These coordination protocols rely on a lossy communication network that can be disconnected pointwise in time at all times, but is connected in an integral sense over a sliding temporal window. This work derives transient and steady-state performance bounds for the tight coordination protocols. Simulation results through a cluttered urban-like environment, where vehicles are subject to wind disturbances, corroborate the theoretical results

Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research

A Framework for Controlling Quality of Sessions in Multimedia Systems

Collaborative multimedia systems demand overall session quality control beyond the level of quality of service (QoS) pertaining to individual connections in isolation of others. At every instant in time, the quality of the session depends on the actual QoS offered by the system to each of the application streams, as well as on the relative priorities of these streams according to the application semantics. We introduce a framework for achieving QoSess control and address the architectural issues involved in designing a QoSess control laver that realizes the proposed framework. In addition, we detail our contributions for two main components of the QoSess control layer. The first component is a scalable and robust feedback protocol, which allows for determining the worst case state among a group of receivers of a stream. This mechanism is used for controlling the transmission rates of multimedia sources in both cases of layered and single-rate multicast streams. The second component is a set of inter-stream adaptation algorithms that dynamically control the bandwidth shares of the streams belonging to a session. Additionally, in order to ensure stability and responsiveness in the inter-stream adaptation process, several measures are taken, including devising a domain rate control protocol. The performance of the proposed mechanisms is analyzed and their advantages are demonstrated by simulation and experimental results

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design