This paper studies the problem of scheduling control data transmissions from embedded processors to physical systems. For this problem, we propose novel state feedback and output feedback control architectures that are predicated on energy function-based and norm-free event-triggering conditions, where the embedded processor broadcasts a sampled data of its control signal value through a zero-order-hold operator to the physical system when the left side of the event-triggering condition equals to its right side. In this context, the energy function-based feature means that the right sides of the proposed event-triggering conditions involve an energy function as well as its time-derivative to make the selection of these right sides user-adjustable. Furthermore, the norm-free feature means that the left sides of the proposed event-triggering conditions do not depend on signal norms to allow for better control data transmission reduction. System-theoretical analyses of our event-triggered state feedback and output feedback control architectures are shown using the same energy functions and their time-derivatives used in the proposed event-triggering conditions, where illustrative numerical examples are also presented to demonstrate the efficacy of our contributions. To the best of our knowledge, the results given in this paper explore how event-triggering conditions are linked not only to energy functions but also to their time-derivatives for the first time.

This paper studies the analysis and synthesis of distributed adaptive control architectures for uncertain multiagent systems with coupled dynamics in a leader-follower setting, where overall closed-loop system stability is established, and sufficient stability conditions are defined. Specifically, we first analyse a standard distributed adaptive control law and reveal local stability conditions that guarantee the boundedness of the closed-loop system trajectories. We next synthesise fixed and adaptive robustifying terms to show relaxed local stability conditions. We also show that the fixed robustifying term can yield high-gain parameters in the resulting control signals, where the adaptive robustifying term removes this high-gain requirement. Finally, an illustrative numerical example is further presented to compare the proposed methods and demonstrate our theoretical contributions.

This paper considers the cooperative output regulation problem of heterogeneous linear time-invariant multi-agent systems over switching graphs. A sufficient condition for an internal model-based distributed dynamic state feedback control law to solve this problem is the asymptotic stability of the origin of the unforced switched closed-loop system under graph-theoretic and algebraic assumptions. Notably, the asymptotic stability depends on the properties of the switching signal. To this end, our contribution is to provide scalable stability analysis and control gain synthesis methods regarding two classes of switching signals, namely arbitrary switching signals and slow switching signals subject to an average dwell time constraint.

This paper focuses on reducing wireless network utilization between an operator (e.g., a ground station) and a controlled dynamical system (e.g., a vehicle equipped with a feedback control algorithm), where the goal of the operator is to provide guidance commands (e.g., trajectory information) to the controlled dynamical system for command following. To this end, we propose a new event-triggering approach for scheduling networked guidance data transmissions between the operator and the controlled dynamical system. The proposed approach utilizes a reference model available to the operator and we explore three event-triggering rules based on an error signal, which is a function of the state of this reference model and the guidance commands. Specifically, the first rule is a standard one in the literature and it is predicated on exchanging sampled guidance commands with the controlled dynamical system. Unlike existing results in the literature, the second and the third rules are respectively predicated on exchanging approximated curve-fitted guidance command functions, which are estimated using the current guidance command value and future waypoints, and exact guidance command functions, which are valid over a time-interval. We further generalize our results to address the presence of system uncertainties using adaptive control theory. In addition to providing detailed stability analysis, a numerical example is also given to show that the second and the third rules achieve less guidance data transmissions (i.e., events) between the operator and the controlled dynamical system.

The authors have recently proposed a norm-free and adaptive event-triggering rule in [1] for distributed control of multiagent systems. For demonstrating the feasibility and efficacy of this rule, the contribution of this paper is to provide an experimental study on an aerial multiagent system with the aim of reducing agent-to-agent information exchange.

We focus on reducing agent-to-agent information exchange in distributed control of multiagent systems. Specifically, our contribution is a norm-free and adaptive event-triggering rule for each agent, where it is decentralised and predicated on the solution-predictor curve method. The decentralised feature means that the proposed event-triggering rule depends on the own error signals of an agent without requiring any neighbouring or global information. The norm-free feature means that the left-hand side of the proposed event-triggering rule inequality does not depend on distances such as absolute values of error signals to allow for better agent-to-agent information exchange reduction. To achieve both decentralised and norm-free features together, an adaptive term is utilised in the event-triggering rule for each agent to estimate unknown variable unavailable to an agent. Here, the presented system-theoretical analysis of the proposed event-triggering rule holds for both the sampled data exchange case and the data exchange case predicated on the solution-predictor curve method. In contrast to standard sampled data exchange, the solution-predictor curve method has the ability to further reduce agent-to-agent information exchange, where each agent stores this curve and exchanges its parameters when an event occurs in a distributed manner for approximating the solution trajectory of each agent.

The contribution of this article is a regional eigenvalue assignment method for cooperative output regulation of heterogeneous linear multiagent systems with an internal model-based distributed dynamic state feedback control law. The proposed method offers an agent-wise local approach to synthesize distributed control gains while assigning, for example, the minimal decay rate and the minimal damping ratio of the overall closed-loop system as desired. Numerical examples demonstrate the efficacy of the proposed method by assigning the eigenvalues of a large-scale system to different regions, specifically, disks, shifted half-planes, and conic sectors.

For reducing information exchange in distributed control of multiagent systems, we propose a norm-free and adaptive event-triggering rule, which is decentralized and predicated on the solution-predictor curve method. Here, the decentralized feature implies that the proposed event-triggering rule depends on own error signals of an agent. Moreover, the norm-free feature implies that the left side of the proposed event-triggering rule inequality does not depend on distances such as absolute values of error signals to yield better agent-to-agent information exchange reduction. To achieve decentralized and norm-free features at the same time, an adaptive term is also utilized in the event-triggering rule for each agent in order to estimate unknown variable unavailable to an agent. The proposed event-triggering rule works both for the sampled data exchange case as well as for the data exchange case predicated on the solution-predictor curve method. In contrast to standard sampled data exchange, the solution-predictor curve method has the ability to further reduce agent-to-agent information exchange, where each agent stores this curve and exchanges its parameters when an event occurs in a distributed manner for approximating the solution trajectory of each agent.

View Video Presentation: https://doi.org/10.2514/6.2022-1381.vid This paper presents experimental results related to the trajectory tracking performance of a recently developed model reference adaptive control (MRAC) of a quadrotor unmanned aerial vehicle carrying a suspended load under wind turbulance. The suspended load as an unmodeled dynamics and the wind as a constant disturbance significantly degrade the tracking performance of the nominal controller. We compare the performance of the men�tioned MRAC with the performances of the nominal controller and the standard MRAC through a series of experiments to elucidate its efficacy in reducing the effects of unmodeled dynamics.

For distributed control of linear multiagent systems, we propose an event-triggering rule having a user-defined exponentially decaying dynamic threshold for reducing local information exchange between agents. The key novelty of the proposed approach is that information exchange between agents are predicated on local solution-predictor curves when an event occurs. In contrast to standard event-triggering rules relying on sampled data exchange, these curves allow for a significant reduction in network utilization. A system-theoretical stability analysis is further provided for the proposed approach and an illustrative numerical example is included to show its efficacy.

In this paper, we propose a new event-triggering approach to schedule control data transmissions in state feedback control of linear time-invariant dynamical systems. Specifically, an energy function-based and norm-free event-triggering condition is presented, where the embedded processor broadcasts a sampled data of its control signal value through a zero-order-hold operator to the dynamical system when the left side of the event-triggering condition equals to its right side. Here, the energy function-based feature implies that the right side of this event-triggering condition involves an energy function as well as its time-derivative for making the selection of its right side user-adjustable. Moreover, the norm-free feature implies that the left side of this event-triggering condition does not depend on signal norms to yield better control data transmission reduction. We also present illustrative numerical examples in order to demonstrate the efficacy of the proposed event-triggering approach in scheduling state feedback control data transmissions.

This paper presents a distributed event-triggered control algorithm for linear time-invariant multiagent systems to schedule local information exchange. The proposed distributed event-triggered control involves a dynamic threshold, which is a function of the error between a dynamical system and its reference model and, in addition, contains an exponentially decaying term to minimize local information exchange during the transient response. This dynamic threshold significantly decreases network utilization (i.e., number of events). Moreover, in contrast to the sampled data exchange approach, which is widely used in the event-triggered control literature, we use a solution-predictor curve exchange method. This method predicts the time trajectories of agents and has the ability to significantly decrease network utilization compared to sampled data exchange. Using graph theory and Lyapunov stability tools, we provide rigorous system-theoretic analysis and show the efficacy of the proposed approach through a numerical example.

In the previous work, a distributed adaptive control method was considered for uncertain multiagent systems with unmeasurable coupled dynamics, where overall closed-loop stability was established when local stability conditions for each agent were held. However, it was shown that these conditions could yield high-gain parameters in the resulting control signals, where this is not desired in practice. The contribution of this paper is a new distributed adaptive control architecture predicated on adaptive robustifying terms to remove the aforementioned high-gain requirement. An illustrative numerical example is included to demonstrate our theoretical contribution.

As it is well-known, system uncertainties and unmodeled dynamics can deteriorate stability properties of model reference adaptive control systems. Motivated by this standpoint, we first analyse stability conditions of model reference adaptive control architectures in the presence of unstructured system uncertainties and unmodeled dynamics. We then synthesise adaptive robustifying terms to relax the aforementioned stability condition, which presents our main contribution. Specifically, these terms in the feedback loop guarantee overall system stability even in the presence of significant system uncertainties when unmodeled dynamics satisfy a condition. We further demonstrate our theoretical findings in an experiment involving an inverted pendulum on a cart (modeled dynamics) coupled with another cart through a spring (unmodeled dynamics).

Recently, a new event-triggering approach for networked guidance data scheduling between an operator such as a ground station and a controlled dynamical system such as a controlled vehicle has been proposed. In particular, in this recent work, three different event-triggering rules are analyzed, where all three event-triggering rules are predicated on an error signal constructed from a state of a reference model available only to the operator. The first rule exchanges sampled data points from the guidance command over the network (being a standard approach in the literature). Contrary to the existing results in the literature, second and third rule, exchange approximated curve-fitted guidance command functions and the exact guidance command functions, respectively, over the network. Motivated by this new event-triggering approach, the contribution of this paper is to present experimental results to validate its efficacy for networked data scheduling between an operator (in this case a Windows PC running Python codes) and a controlled dynamical system (in this case a nano-quadcopter). Our experimental findings show that the second and third event-triggering rules achieve satisfactory level of command following with less data exchange (number of events) between the operator and the controlled dynamical system.

In the distributed adaptive control of uncertain linear time-invariant multiagent systems, it is well-known that the presence of heterogeneous coupled and/or actuator dynamics can yield to unstable controlled system trajectories. In this paper, we focus on the analysis and synthesis of a distributed adaptive architecture for control of uncertain multiagent system with both heterogeneous coupled and actuator dynamics. Specifically, a distributed adaptive control architecture for this class of uncertain multiagent systems is analyzed and two stability limits are developed. While the first one depends on the matrices resulting from the coupled dynamics, the second one depends on the first one that captures the tradeoff between heterogeneous agent actuation capabilities and unknown parameters in the agent dynamics. An illustrative numerical example is also provided to demonstrate the efficacy of the proposed architecture.

In the design and implementation of networked multiagent systems, it is essential not only to guarantee closed-loop system stability but also to schedule interagent information exchange in order to prevent potential network overload and decrease wireless communication costs. For stably scheduling information exchange in networked multiagent systems, the contribution of this article is threefold. We first present a new event-triggered distributed control architecture predicated on a dynamic threshold, which involves an error signal between the state of an agent and the state of its reference model as well as an exponentially decaying term, to schedule interagent information exchange. Building upon the first contribution and in contrast to the standard sampled data exchange viewpoint when an event occurs, second, we propose a method entitled solution-predictor curve. In particular, this method approximates the solution trajectory related to information exchange, where every agent stores this curve and distributively exchanges its parameters when an event occurs. Its key feature is that each agent utilizes the resulting solution trajectory over the time interval until the next event occurs, where it has the capability to further reduce interagent information exchange compared with the sampled data case. A system-theoretical stability analysis of the proposed event-triggered distributed control architecture is given, which captures both sampled data and solution-predictor curve cases, and practical guidelines on the selection of parameter tuning parameters are also stated. As the third contribution, we demonstrate the efficacy of our theoretical results in laboratory-level experiments.

We study how to schedule actuator data transmission in discrete-time networked model reference adaptive control systems for reducing wireless network utilization. We propose an event-triggered hedging approach introduced to the reference model, which is constructed by taking the difference between the sampled control signal sent to an uncertain dynamical system and the actual discrete-time control signal computed by the adaptive control algorithm. The advantages of the proposed approach include i) asymptotic convergence of the difference between an uncertain dynamical system state and the reference model state predicated on a logarithmic Lyapunov function-based stability analysis, ii) possibility of convergence of the estimated parameters to the unknown parameters when the closed-loop dynamical system is persistently excited, and iii) direct execution of the proposed approach in an embedded code. Note that i) and ii) are not trivial to achieve without introducing the proposed hedging approach to the reference model and iii) results from our discrete-time framework since existing continuous-time approaches require discretization that may result in loss of stability properties. Finally, an illustrative numerical example is provided for demonstrating the efficacy of the proposed approach.

This paper presents a fault-tolerant control method for a quadrotor UAV using solely on-board sensors. A simultaneous localization and mapping (SLAM) system is developed utilizing a laser rangefinder and an open source SLAM algorithm called GMapping. This system allows for mapping of the surrounding environment as well as localizing the position of the quadrotor, enabling real-time position control. However, the SLAM system using the laser rangefinder may fail in certain degenerate environment like featureless tunnels or straight hallways. In order to compensate for possible faults in the SLAM measurements, a fault detection and fault-tolerant control method is developed. An observer is designed to estimate the translational velocity of the quadrotor using SLAM position measurements. The fault detection residual is defined as the deviation between this SLAM-based velocity estimate and another velocity estimate generated by an optical flow algorithm utilizing measurements provided by a downward facing camera. Real-time experimental results have shown the effectiveness of the fault-tolerant control algorithm.