Stability analysis of event-triggered anytime control with multiple control laws

To deal with time-varying processor availability and lossy communication channels in embedded and networked control systems, one can employ an event-triggered sequence-based anytime control (E-SAC) algorithm. The main idea of E-SAC is, when computing resources and measurements are available, to compute a sequence of tentative control inputs and store them in a buffer for potential future use. State-dependent Random-time Drift (SRD) approach is often used to analyse and establish stability properties of such E-SAC algorithms. However, using SRD, the analysis quickly becomes combinatoric and hence difficult to extend to more sophisticated E-SAC. In this technical note, we develop a general model and a new stability analysis for E-SAC based on Markov jump systems. Using the new stability analysis, stochastic stability conditions of existing E-SAC are also recovered. In addition, the proposed technique systematically extends to a more sophisticated E-SAC scheme for which, until now, no analytical expression had been obtained.Comment: Accepted for publication in IEEE Transactions on Automatic Contro

Event-triggered Consensus Control of Heterogeneous Multi-agent Systems: Model- and Data-based Analysis

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an $\mathcal{H}_{\infty}$ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method.Comment: 13 pages, 6 figures. This draft was firstly submitted to IEEE Open Journal of Control Systems on April 30, 2022, but rejected on June 19, 2022. Later, on July 23, 2022, this paper was submitted to the journal SCIENCE CHINA information scienc

Optimal adaptive control of time-delay dynamical systems with known and uncertain dynamics

Delays are found in many industrial pneumatic and hydraulic systems, and as a result, the performance of the overall closed-loop system deteriorates unless they are explicitly accounted. It is also possible that the dynamics of such systems are uncertain. On the other hand, optimal control of time-delay systems in the presence of known and uncertain dynamics by using state and output feedback is of paramount importance. Therefore, in this research, a suite of novel optimal adaptive control (OAC) techniques are undertaken for linear and nonlinear continuous time-delay systems in the presence of uncertain system dynamics using state and/or output feedback. First, the optimal regulation of linear continuous-time systems with state and input delays by utilizing a quadratic cost function over infinite horizon is addressed using state and output feedback. Next, the optimal adaptive regulation is extended to uncertain linear continuous-time systems under a mild assumption that the bounds on system matrices are known. Subsequently, the event-triggered optimal adaptive regulation of partially unknown linear continuous time systems with state-delay is addressed by using integral reinforcement learning (IRL). It is demonstrated that the optimal control policy renders asymptotic stability of the closed-loop system provided the linear time-delayed system is controllable and observable. The proposed event-triggered approach relaxed the need for continuous availability of state vector and proven to be zeno-free. Finally, the OAC using IRL neural network based control of uncertain nonlinear time-delay systems with input and state delays is investigated. An identifier is proposed for nonlinear time-delay systems to approximate the system dynamics and relax the need for the control coefficient matrix in generating the control policy. Lyapunov analysis is utilized to design the optimal adaptive controller, derive parameter/weight tuning law and verify stability of the closed-loop system”--Abstract, page iv

Riccati-based design of event-triggered controllers for linear systems with delays

\u3cp\u3eIn event-triggered control (ETC) systems, the measured state or output of the plant is sent to the controller at so-called event times. In many ETC systems, these event times are generated based on a static function of the current state or output measurement of the system and its sampled-and-held version that is available to the controller. Hence, the event-generator does not include any dynamics of its own. In contrast, dynamic event-generators trigger events based on additional dynamic variables, whose dynamics depend on the state or output of the system. In this paper, we propose new static and dynamic continuous event-generators (which require continuous measuring of the plant output) and periodic eventgenerators (which only require periodic sampling of the plant output) for linear control systems with communication delays. All event-generators we propose lead to closed-loop systems which are globally exponentially stable with a guaranteed decay rate, L2 -stable with a guaranteed L2 - gain, and have a guaranteed positive minimum inter-event time. By using new Riccati-based analysis tools tailored to linear systems, the conservatism in our decay rate and L2 -gain estimates is small. The dynamic event-generators even further reduce this conservatism, and as a result typically generate significantly fewer events than their static counterparts, while guaranteeing the same control performance. The benefits of these new event-generators are demonstrated via two numerical examples.\u3c/p\u3

Riccati-based design of event-triggered controllers for linear systems with delays

In event-triggered control (ETC) systems, the measured state or output of the plant is sent to the controller at so-called event times. In many ETC systems, these event times are generated based on a static function of the current state or output measurement of the system and its sampled-and-held version that is available to the controller. Hence, the event-generator does not include any dynamics of its own. In contrast, dynamic event-generators trigger events based on additional dynamic variables, whose dynamics depend on the state or output of the system. In this paper, we propose new static and dynamic continuous event-generators (which require continuous measuring of the plant output) and periodic eventgenerators (which only require periodic sampling of the plant output) for linear control systems with communication delays. All event-generators we propose lead to closed-loop systems which are globally exponentially stable with a guaranteed decay rate, L2 -stable with a guaranteed L2 - gain, and have a guaranteed positive minimum inter-event time. By using new Riccati-based analysis tools tailored to linear systems, the conservatism in our decay rate and L2 -gain estimates is small. The dynamic event-generators even further reduce this conservatism, and as a result typically generate significantly fewer events than their static counterparts, while guaranteeing the same control performance. The benefits of these new event-generators are demonstrated via two numerical examples

Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input --Abstract, page iv

Distributed scheduling algorithms for LoRa-based wide area cyber-physical systems

Low Power Wide Area Networks (LPWAN) are a class of wireless communication protocols that work over long distances, consume low power and support low datarates. LPWANs have been designed for monitoring applications, with sparse communication from nodes to servers and sparser from servers to nodes. Inspite of their initial design, LPWANs have the potential to target applications with higher and stricter requirements like those of Cyber-Physical Systems (CPS). Due to their long-range capabilities, LPWANs can specifically target CPS applications distributed over a wide-area, which is referred to as Wide-Area CPS (WA-CPS). Augmenting WA-CPSs with wireless communication would allow for more flexible, low-cost and easily maintainable deployment. However, wireless communications come with problems like reduced reliability and unpredictable latencies, making them harder to use for CPSs. With this intention, this thesis explores the use of LPWANs, specifically LoRa, to meet the communication and control requirements of WA-CPSs. The thesis focuses on using LoRa due to its high resilience to noise, several communication parameters to choose from and a freely modifiable communication stack and servers making it ideal for research and deployment. However, LoRaWAN suffers from low reliability due to its ALOHA channel access method. The thesis posits that "Distributed algorithms would increase the protocol's reliability allowing it to meet the requirements of WA-CPSs". Three different application scenarios are explored in this thesis that leverage unexplored aspects of LoRa to meet their requirements. The application scenarios are delay-tolerant vehicular networks, multi-stakeholder WA-CPS deployments and water distribution networks. The systems use novel algorithms to facilitate communication between the nodes and gateways to ensure a highly reliable system. The results outperform state-of-art techniques to prove that LoRa is currently under-utilised and can be used for CPS applications.Open Acces

Event-triggered Consensus Frameworks for Multi-agent Systems

Recently, distributed multi-agent systems (MAS) have been widely studied for a variety of engineering applications, including cooperative vehicular systems, sensor networks, and electrical power grids. To solve the allocated tasks in MASs, each agent autonomously determines the appropriate actions using information available locally and received from its neighbours. Many cooperative behaviours in MAS are based on a consensus algorithm. Consensus, by definition, is to distributively agree on a parameter of interest between the agents. Depending on the application, consensus has different configurations such as leader-following, formation, synchronization in robotic arms, and state estimation in sensor networks. Consensus in MASs requires local measurements and information exchanges between the neighbouring agents. Due to the energy restriction, hardware limitation, and bandwidth constraint, strategies that reduce the amount of measurements and information exchanges between the agents are of paramount interest. Event-triggering transmission schemes are among the most recent strategies that efficiently reduce the number of transmissions. This dissertation proposes a number of event-triggered consensus (ETC) implementations which are applicable to MASs. Different performance objectives and physical constraints, such as a desired convergence rate, robustness to uncertainty in control realization, information quantization, sampled-data processing, and resilience to denial of service (DoS) attacks are included in realization of the proposed algorithms. A novel convex optimization is proposed which simultaneously designs the control and event-triggering parameters in a unified framework. The optimization governs the trade-off between the consensus convergence rate and intensity of transmissions. This co-design optimization is extended to an advanced class of event-triggered schemes, known as the dynamic event-triggering (DET), which is able to substantially reduce the amount of transmissions. In the presence of DoS attacks, the co-design optimization simultaneously computes the control and DET parameters so that the number of transmissions is reduced and a desired level of resilience to DoS is guaranteed. In addition to consensus, a formation-containment implementation is proposed, where the amount of transmissions are reduced using the DET schemes. The performance of the proposed implementations are evaluated through simulation over several MASs. The experimental results demonstrate the effectiveness of the proposed implementations and verify their design flexibility