Distributed event‐triggered containment control for dynamical multiagent networks

This paper studies the event‐triggered containment control problem for dynamical multiagent networks of general MIMO linear agents. An event‐triggered containment control strategy is provided, which consists of a control law based on a relative‐state feedback and a distributed triggering rule based on both the relative‐state information and a time‐dependent threshold function. Compared to the previous related works, our main contribution is that the triggering rule depends only on local information of communication networks. It is proved that under the proposed event‐based controller, the containment errors are uniformly ultimately bounded and the Zeno behavior can be excluded. Moreover, when the derivation constant in the threshold function is equal to zero, the containment control problem can be solved. Then, the results are extended to the event‐triggered observer‐based containment controller design.     

Global robust practical output regulation for nonlinear systems in output feedback form by output‐based event‐triggered control

In this paper, we study the event‐triggered global robust practical output regulation problem for a class of nonlinear systems in output feedback form with any relative degree. Our approach consists of the following three steps. First, we design an internal model and an observer to form the so‐called extended augmented system. Second, we convert the original problem into the event‐triggered global robust practical stabilization problem of the extended augmented system. Third, we design an output‐based event‐triggered control law and a Zeno‐free output‐based event‐triggered mechanism to solve the stabilization problem, which, in turn, leads to the solvability of the original problem. Finally, we apply our result to the controlled hyperchaotic Lorenz systems.     

Robust event‐triggered model predictive control for constrained linear continuous system

Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual‐mode control strategy based on event‐triggered MPC (ETMPC) and state‐feedback control for continuous linear time‐invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event‐triggered mechanism and reduce the computational load of MPC. Next, the dual‐mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter‐event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance.     

Robust event‐triggered output feedback learning algorithm for voltage source inverters with unknown load and parameter variations

We consider the output feedback event‐triggered control of an off‐grid voltage source inverter (VSI) with unknown inductance‐capacitance (L ? C) filter dynamics and connected load in the presence of an input disturbance acting at the inverter. Due to uncertain dynamics and unmodeled parameters in the L ? C filter connected to the VSI, we use an adaptive observer to reconstruct the system's states by measuring only the voltage at the output. The control mechanism is constructed based on an impulsive actor/critic framework that approximates the cost, the event‐triggered controller, and the worst case disturbance and generates the desired AC output with the least energy dissipation. We provide rigorous stability proofs and illustrate the applicability of our results through a simulation example.     

Event‐triggered practical finite‐time output feedback stabilization of a class of uncertain nonlinear systems

This paper studies the event‐triggered practical finite‐time output feedback stabilization problem for a class of uncertain nonlinear systems with unknown control gains. First, a reduced‐dimensional observer is employed to implement the reconstruction of the unavailable states. Furthermore, a novel event‐triggered output feedback control strategy is proposed based on the idea of backstepping design and sign function techniques. It is shown that the practical finite‐time stability of the closed‐loop systems is ensured by Lyapunov analysis and related stability criterion. Compared with the existing methods, the main advantage of this strategy is that the observer errors and event‐trigger errors can be processed simultaneously to achieve the practical finite‐time stability. Finally, an example is adopted to demonstrate the validity of the proposed scheme.     

Nonlinear bilateral teleoperators under event‐driven communication with constant time delays

Bilateral teleoperation systems provide a platform for human operators to remotely manipulate slave robots in engaging various tasks in remote environments. Most of the previous studies in bilateral teleoperation were developed under continuous transmission or periodic communication with fixed data exchanging rates. This paper presents control schemes for bilateral teleoperation systems using nonperiodic event‐driven communication. By using P‐like and PD‐like controllers, this study proposes triggering conditions for teleoperators to reduce network access frequency so that robots only transmit output signals when necessary. Stability and position tracking of the control system are studied, and nonzero minimum interevent time is guaranteed. The proposed event‐driven teleoperation is studied with a velocity estimator to avoid the requirement of velocity information in the controller and triggering condition. Without velocity measurements, the boundedness of tracking errors and stability are ensured for teleoperation systems under event‐driven communication. Simulations and experiments are illustrated to validate the performance of the proposed event‐driven teleoperation systems.     

Event‐triggered control design for nonlinear systems with actuator failures and uncertain disturbances

Traditional adaptive event‐triggered design methods compensated for the event‐triggered error are not direct, and the stability analysis of resulting close‐loop systems is rather complicated. To alleviate the above restrictions, we propose a direct and simple event‐triggered co‐design method to solve the tracking control problem for parameter strict‐feedback systems with actuator faults and uncertain disturbances. By introducing a compensating terms in a smooth function form of a conventional control law and certain positive integrable functions, the effects of actuator faults and event‐triggered error can be compensated completely. Such a direct design method has the following features: (i) a direct compensation of the event‐triggered error is achieved without introducing any extra design parameters; (ii) it is not necessary to know any bound information on the parameters of event‐triggered threshold, and global asymptotic tracking control of the overall closed‐loop system is achieved; and (iii) the resulting stability criteria of the proposed event‐triggered control design are much simpler and easier to fulfill by virtue of the introduced co‐design method. Simulations are then carried out to validate the proposed schemes.     

Modular field robot deployment for inspection of dilapidated buildings

Robotic inspection often relies on building custom platforms for each new deployment; this is a luxury that urban search and rescue (USAR) robots do not have when time is of critical importance. A significant factor for robots deployed in disaster areas is the varying size of voids and access ways in their path. These situations require platforms that can quickly reconfigure on location. With these challenges in mind, we present the NeWheel system: An in‐field reconfigurable robotic platform that allows mobility changes before, and during, deployment. The NeWheel platform also has the advantage of being small enough to be person‐deployable and to travel as checked luggage on a commercial flight. This field report presents the results and learnings from three field trips on Peel Island located off the coast of Brisbane, Australia. These field trips featured the deployment of the NeWheel system in multiple configurations to inspect and map inside historic dilapidated buildings. It demonstrates the potential of the NeWheel in buildings cluttered with debris and with unstable flooring whether they are historically important or in USAR contexts.     

The current state and future outlook of rescue robotics

Robotic technologies, whether they are remotely operated vehicles, autonomous agents, assistive devices, or novel control interfaces, offer many promising capabilities for deployment in real‐world environments. Postdisaster scenarios are a particularly relevant target for applying such technologies, due to the challenging conditions faced by rescue workers and the possibility to increase their efficacy while decreasing the risks they face. However, field‐deployable technologies for rescue work have requirements for robustness, speed, versatility, and ease of use that may not be matched by the state of the art in robotics research. This paper aims to survey the current state of the art in ground and aerial robots, marine and amphibious systems, and human–robot control interfaces and assess the readiness of these technologies with respect to the needs of first responders and disaster recovery efforts. We have gathered expert opinions from emergency response stakeholders and researchers who conduct field deployments with them to understand these needs, and we present this assessment as a way to guide future research toward technologies that will make an impact in real‐world disaster response and recovery.     

A coordinated dispatch method for energy storage power system considering wind power ramp event

In response to the impact of wind power ramp events on power system, a forecast and coordinated dispatch method for wind power ramp events is proposed. Firstly, the LSTM neural network is utilized to multi-step forecast the wind power, which can identify the features such as amplitude and duration of wind power ramp event in advance. Then, an ahead-delay adjustment method (ADAM) for wind power ramp events is proposed, which coordinates the thermal power generation units (referred to as units) and the energy storage system (referred to as ESS) to make two-step adjustments during the period before and after wind power ramp event. Thereby reducing the ramp rate of wind power, while reducing the requirement for the output power and capacity of ESS. Based on the above methods, a coordinated dispatch model of units and ESS considering wind power ramp events is established. The units are coordinated in advance to ensure sufficient adjustable reserves during wind power ramp events. A more economical and reliable dispatch plan can be achieved by coordinating the units and ESS according to the judging conditions. Finally, the forecast performance test and the ramp dispatch simulation are carried out in case study to verify the effectiveness of the forecast and dispatch method.