Target enclosing is a great potential avenue for unmanned aerial vehicles (UAVs) to execute search and rescue, escorting, and geography mapping missions. However, existing methods struggle to fulfill the requirements of prescribed enclosing performance and collision avoidance in a constrained environment. To address aforementioned issues, this paper aims to propose a performance-designated reinforcement learning-based enclosing control (PDRLEC) scheme for UAVs to achieve target circle approximation with specified manners while ensuring collision avoidance. Particularly, an adaptive variable performance function is designed to effectively address the singularity problem existing in the prescribed performance control (PPC). Additionally, by converting radial deviation and distance between UAVs and obstacles into two skilled barrier functions, the reinforcement learning (RL) module is endowed with the ability to handle performance constraints and achieve obstacle bypassing. The proposed PDRLEC, which consists of a robust enclosing item and a RL-related module, can achieve optimal enclosing with enhanced sample efficiency and elevated reward values while striking a balance between collision avoidance and target enclosure. Finally, abundant simulations are provided to corroborate the feasibility and superiority of developed PDRLEC.

How to efficiently collect multi-dimensional information of traffic accidents with high dangers has triggered intensive concerns in intelligent transportation community. This paper provides a viable and continuous observation solution using low-budgeted mobile robots. At the kinematic level, a relative position estimator utilizing available distance data is constructed with an exponential error convergence, removing the dependency of using global position. To proceed, resorting to estimation results, an elliptical encircling guidance rule with a time-varying radius is established to preserve circumnavigation concerning targets. At the kinetic level, a new fuel-saving uncertainty mitigation scheme, i.e., event-triggered unknown system dynamics estimators (ETUSDEs) with the feature of a reduced transmission load and a concise structure are respectively developed in velocity and angular rate subsystems to reconstruct uncertainties with a prescribed decaying rate, where event-triggering conditions are enforced to schedule updating frequency of actuators and measurements in an aperiodic manner instead of a fixed time interval. Then, an event-triggered robust kinetic control protocol is synthesized to achieve an accurate command tracking without incurring Zeno behaviors. Finally, the convergence of entire system is illustrated through input-to-state stable (ISS) criterion. Simulation results are delivered to testify the effectiveness of proposed method.

This article addresses a fuzzy-quantized elliptical target encircling control of quadrotors with arbitrary-time convergence, consisting of translational and rotational designs. At the translational level, an arbitrary-time elliptical guidance rule is designed to empower quadrotors to move along the predefined elliptical path within a prescribed settling time free from initial conditions. At the rotational level, a fuzzy-quantized attitude regulation protocol is developed to stabilize the attitude deviation, where a quantized fuzzy logic is artfully constructed to online recover uncertainties via updating weights with finite states scheduled by a hysteresis quantizer, greatly reducing signal transmission burden. Finally, the overall system stability is demonstrated via input-to-state stable principle, while not only simulations but also experiments are given to verify the efficacy of suggested approach.

This article investigates a finite-time composite learning-based elliptical enclosing control for nonholonomic robots under a global positioning system (GPS)-denied environment. At the kinematic level, following a prediction and innovation architecture, a novel bearing measurement-based relative position observer formulated in a local coordinate is proposed to assure an exponential decaying of estimation errors without the aid of GPS. Utilizing the observation outcomes, an elliptical guidance law with a time-varying enclosing radius and nonorthogonal tangential and axial vectors is established to yield the reference velocity and angular rate to be tracked. At the kinetic level, by constructing filtering operations and auxiliary variables to extract weight errors, a special finite-time composite neural learning driven by weight and tracking errors is devised to reinforce parameter convergences, then an anti-disturbance kinetic control rule is designed to achieve online precise disturbance compensation and finite-time error convergence. The distinct merit is that an elliptical surrounding concerning an unknown target can be fulfilled with the finite-time neural learning capability while eliminating the deployment of GPS, which is nontrivial and challenging than reported circumnavigation alternatives either relying on the accessibility of GPS or neglecting kinetic uncertainties. An input-to-state stable criterion is applied to demonstrate the boundedness of a closed-loop system. Simulations are provided to confirm the utility of the considered strategy.

This article investigates a robust path-following control problem of multiple autonomous vehicles along an implicit elliptical curve. At the kinematic level, by formulating a novel coordinated error in terms of projective arc length instead of relative distance, a new distributed guidance law is developed for multiple vehicles evolving along a geometric path, achieving an equal arc separation and a uniform forward speed. Based on simple filtering operations upon available states and invariant manifold, unknown system dynamics estimators (USDEs)-based robust kinetic controllers with a concise structure are derived to enable a satisfied nominal tracking of velocity and angular rate subject to uncertainties while eliminating the computational complexity encountered in the available function approximators. The remarkable merit of the explored solution lies in that robust cooperative behaviors over an implicit elliptical curve can be attained by specifying successive projective arc length for nonholonomic vehicles, avoiding the time-consuming path variable synchronization calculation inherent in parameterized reference-guided paradigms, eliminating temporal limitations of time-related function in trajectory tracking strategies. It is proven that all signals of a closed-loop system are convergent by using the input-to-state stable (ISS) principle. Simulation and experimental outcomes are both delivered to substantiate the efficacy and superiority of the presented method.

In this paper, we present a distributed cooperative surrounding control for mobile robots with uncertainties and aperiodic sampling. At the kinematic level, a cooperative circumnavigation guidance law with the capability of spatial-temporal decoupling, employing a line-of-sight (LOS) principle to accomplish closed orbit following and a path parameter synchronization to assign the desired speed, is devised to generate reference velocity and angular rate. At the kinetic level, to reduce the transmission burden in sensor-to-controller channel and compensate for the total uncertainties existing in velocity channels, aperiodic sampling based extended state observers (AS-ESOs), are developed to provide precise disturbance estimates with guaranteed convergence, while a nonnegative threshold-based event-triggering condition with a straightforward tuning procedure is designed to schedule the communication frequency without inducing Zeno behaviors. Then a robust anti-disturbance kinetic control protocol is synthesized that renders an equiangular distribution along the common circle. The salient merit is that a symmetric formation pattern over a closed curve defined by parameterized path, instead of time-related functions, can be obtained in a distributed manner with decreased sampling cost and disturbances. Moreover, all error variables in the closed-loop system are demonstrated to be bounded. Finally, the effectiveness of algorithm is verified by simulations.

This paper presents an elliptical target encircling control policy of quadrotors subject to uncertainties and aperiodic signals updating based on pure bearing measurements. At the translational level, by resorting to bearing-only data, rather than prior position and velocity information of target, a position estimator is constructed for locating the unknown target. Utilizing the localization result from position estimator, compared to the existing circular surrounding alternatives, a planar elliptical guidance law capable of adapting more sophisticated operational environment, and a longitudinal control law are synchronously established to generate the velocity reference. At the rotational level, an unknown system dynamics estimator (USDE) is introduced to online neutralize total adverse effect induced by exogenous disturbances and internal uncertainties, where high precision estimation and low computational complexity can be guaranteed with only one tuning argument, then an event-triggered robust attitude controller carrying a sampling deviation compensation item is synthesized accomplishing elliptical encircling for a dynamic target without involving Zeno behavior. Finally, stability of closed-loop system is analyzed via input-to-state stable principle, while simulations are given to verify the efficacy of suggested approach.

This article presents an input-and-measurement event-triggered output-feedback chattering reduction control for microelectromechanical system (MEMS) gyroscopes. To realize online estimation with decreased communication burden along sensor-to-control channel, a switching threshold-based sampler is embedded to achieve an intermittent measurement-based extended state observer (IMESO) capable of synchronously observing unavailable velocity states and disturbances, meanwhile, a mathematical presentation reflecting the interaction between design parameters and upper boundary of estimation errors is deduced to make argument tuning easy. Next, an event-triggered output-feedback control rule is developed in the controller-to-actuator channel to obtain a discrete control signal with less occupation on communication resources without inducing Zeno phenomena. Besides, to enforce system profiles evolve within the predefined performance boundaries with reduced chattering, a tracking differentiator (TD)-based prescribed performance control (TDPPC) is proposed, where the time differentiation of the preselected envelopes can be managed with smooth transient, and a balance between system performance and sampling cost can be ensured. Finally, a sigmoid function-based TD (STD), rather than dynamic surface control, is utilized to overcome the complexity explosion. Comparison simulations are performed to show the superiorities and effectiveness of the established controller.

The purpose of this paper is to present a new hybrid Elastic Net (EN) algorithm, by integrating the ideas of the Self Organization Map (SOM) and the strategy of the gradient ascent into the EN algorithm. The new hybrid algorithm has two phases: an EN phase based on SOM and a gradient ascent phase. We acquired the EN phase based on SOM by analyzing the weight between a city and its converging and non-converging nodes at the limit when the EN algorithm produces a tour. Once the EN phase based on SOM stuck in local minima, the gradient ascent algorithm attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. These two phases are repeated until the EN gets out of local minima and produces the short or better tour through cities. We test the algorithm on a set of TSP. For all instances, the algorithm is showed to be capable of escaping from the EN local minima and producing more meaningful tour than the EN.