This paper presents a prescribed time learning-based near optimal enclosing controller to ensure that unmanned aerial vehicles (UAVs) encircle around the specified target with prescribed performance constraints and minimum cost efforts. First, a basic enclosing controller is established to achieve the enclosing error stabilization and stable circumnavigation around a given target. Second, a new prescribed time behavior envelope that eliminates the availability on initial error is proposed. To render the satisfaction of performance constraints and optimal enclosing actions, a transformed enclosing error is obtained by enforcing state conversion on original error. Then, aiming at stabilizing the enclosing error to a prescribed accuracy within a prescribed time, a prescribed time learning-based near optimal enclosing controller under a critic-only adaptive dynamic programming (ADP) is explored, approximating the solution of a novel Hamilton-Jacobi-Bellman (HJB) equation via pursuing the minimum cost associated with transformed errors. Especially, a novel prescribed time learning rule driven by weight errors is elaborated by revisiting real-time and historical information, such that the convergence of weights is only determined by a user-defined time constant. The prominent merit is that the optimal enclosing with performance guarantees can be achieved by a prescribed time ADP. Lyapunov stability demonstrates that involved error variables are ultimately limited and resultant controller satisfies optimality. Finally, simulations verify the values and superiority of the proposed methodology.

This article solves an anti-collision near optimal enclosing control for Uncrewed Aerial Vehicles (UAVs) under a fixed-time learning based adaptive dynamic programming (ADP) context, containing an adaptive feedforward circling item and a collision-free optimal stabilization policy. First, an adaptive feedforward circling item is derived to steer UAVs to approximate and evolve along a desired circle under wind interferences. Specifically, a neural predictor is incorporated that enables a smooth and accurate uncertainty estimate without imposing transient chattering. Next, under a critic-only ADP, a collision-free optimal stabilization policy is developed to tackle static obstacle avoidance and preserve control optimality with the minimum energy cost. To achieve collision elimination, a novel barrier function that describes the hitting risk degree is augmented in the value function. Especially, based on integration of historical data, an innovative fixed-time learning rule driven by weight errors is elaborated, retaining a prescribed convergence free from initial weight selections. The unique innovation includes two aspects, one is that obstacle avoidance and enclosing maintenance are simultaneously guaranteed even with different weight initializations in a unified ADP learning paradigm, another is that energy consumption can be reduced by approximately 18.9% compared with artificial potential field. Involved errors are theoretically demonstrated to be convergent. The usefulness and merits of presented algorithm are accessed by abundant comparisons.

This article presents a concurrent learning-based adaptive critic formation for multirobots under safety constraints, which comprises of an initial formation consensus item and a collision-free adaptive critic policy. First, based on directed graph communication, an initial formation consensus item is designed to maintain the velocity agreement under a leader-follower setting. Particularly, a collision-free adaptive critic policy is developed that enables robots to preserve formation configuration with the minimum cost while excluding collisions caused by inter-robots and static/moving obstacles, wherein safety constraints encoded by an elegantly devised penalty function are enforced by converting constrained optimal control into unconstrained optimal control issue. Furthermore, by revisiting real-time and historical information, a concurrent weight learning rule is elaborated under a critic-only adaptive dynamic programming, improving the weight convergence without demanding the persistence excitation conditions. The remarkable benefits outperforming existing outcomes are safety-critical coordination with energy-saving performances is assured under a computationally efficient optimal learning paradigm. Involved errors are theoretically proved to be convergent. Finally, the values and superiorities are verified through extensive simulations on 2-D and 3-D multirobots.

This paper presents a safety-certified optimal for-mation control scheme for nonlinear multi-agents to realize de-sired formation configuration under safety constraints, guaran-teeing a compromise between safety-critical and energy-saving performances. Firstly, a self-learning optimal formation policy enables agents to achieve optimal formation configuration, wherein optimal performance is guaranteed via a computational-ly-efficient adaptive dynamic programming (ADP) framework. Furthermore, by revisiting real-time and historical information, a novel weight updating rule with fixed-time convergence is elabo-rated, such that rapid weight regulation is realized without de-pending on the initial choices. Secondly, a minimally-invasive safe control policy with high-order control barrier function con-straints is constructed in obstacles-clustered environments, wherein collision risk is excluded by ensuring the forward invari-ance of the safety set. It is strictly proved that closed-loop errors are uniformly ultimately bounded. Finally, extensive simulations are verified the values and superiorities of proposed method.

During the recovery of fixed-wing unmanned aerial vehicles (UAVs) by a SideArm, the motion of the desired docking point (DDP) on the arresting cable located at the SideArm’s tip is heavily influenced by the ship’s movements. The movements increase the complexity and risk of the docking procedure. As a means of stabilizing the spatial position of the DDP, this paper presents a novel control framework that integrates kinematic solutions and dynamic control strategies. The proposed framework first compensates for the ship’s six-degrees-of-freedom motion, then generates the basic joint signals of the SideArm using a geometry-based inverse kinematic (IK) method. The twin delayed deep deterministic policy gradient (TD3) algorithm is applied to compensate for these signals, forming the IK-TD3 approach that enhances the accuracy of the DDP’s spatial position. An appointed-time prescribed performance control method is designed to ensure that the system’s performance remains within predefined constraints and converges within a preassigned time. A fixed-time disturbance observer is introduced to estimate the lumped disturbances and uncertainty dynamics of the SideArm. A comprehensive set of simulations and experiment considering different sea states demonstrates the reliability of the proposed approach.

This paper investigates the adaptive sliding mode security control (ASMSC) problem of rotary inverted pendulum against randomly occurring false data injection attacks (ROFDIAs). To accomplish the control objectives for both swing-up and stabilization stages, this study proposes an adaptive backstepping nonlinear control method and an ASMSC method, respectively. These approaches distinctly differ from conventional strategies such as energy-based swing-up control methods, linear quadratic regulators, or adaptive sliding mode control techniques. Besides, note that the existing control methods are proposed in the normal network environment, once the network is malicious attacks, these methods will be invalid. Based on this, a novel ASMSC method is proposed of rotating inverted pendulum against ROFDIAs. Meanwhile, the validity of the proposed method is proved by two comparative experiments on the hardware-in-the-loop simulation platform.

This paper addresses an optimal elliptical enclosing problem of Unmanned Aerial Vehicles (UAVs) under prescribed constraints, whose objective is to steer UAVs to fulfill accurate target encirclement while complying with arriving time restrictions and minimum efforts. A novel learning-based approximate optimal control policy including two-stage designs is presented. At the first stage, a steady-state robust control protocol is developed to steer UAVs to precisely travel along a predefined elliptical path based on concise filtering. At the second stage, to address specified-time constraints and gain the online optimization ability, a single-critic based enhanced learning rule is explored to generate an approximate optimal regulator that stabilizes error dynamics and minimizes value functions, wherein specified-time constraints can be handled by encoding inequality conditions as skilled barrier functions, and by making full use of historical data and current information, a finite-time learning mechanism driven by weight errors rather than Bellman errors is proposed to approximate the solution of Hamilton-Jacobi-Bellman (HJB) equation with faster decaying. The distinct merit is that an improved reinforcement learning (RL) paradigm is formulated to prescribe an elliptical circumnavigation with assured time requirements and optimization behaviors, which can greatly outperform non-RL alternatives in maintaining the optimal performance index while exhibiting restriction handling ability via online learning. Lyapunov stability demonstrates that involved error variables are ultimately limited and resultant controller obeys optimality. The feasibility and values of presented algorithm are accessed by comparisons and simulations.

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 article, a neural-network-based constrained output-feedback control is considered for microelectromechanical system (MEMS) gyroscopes subject to scarce transmission bandwidth and lumped disturbances resulting from model uncertainties, dynamic coupling, and environmental disturbances. First, a hybrid quantizer capable of achieving an adjustable communication rate and quantization density is proposed to convert continuous control signals into discrete values, allowing for reduced chattering behavior even when control actions vary within large regions and enhanced tracking accuracy can be ensured. Subsequently, by applying two types of nonlinear mapping, all state variables of MEMS gyroscopes are restrained within the predefined time-varying asymmetric functions without imposing stringent feasibility conditions on virtual control laws. Furthermore, an echo-state network-based minimal learning parameter neural observer is developed to simultaneously recover the unmeasurable velocity-state variables, matched as well as unmatched disturbances in constrained MEMS gyroscopes dynamics, enabling an output-feedback control solution with a decreased online learning complexity. It is shown via the Lyapunov stability and nonsmooth analysis that all signals in the closed-loop system remain ultimately uniformly bounded even with discontinuous control actions. Comparison simulations are produced to certify the effectiveness of the presented controller.

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.

In this article, we investigate the neural adaptive quantized control problem for microelectromechanical system (MEMS) gyroscope with full-state constraints and lumped disturbances. With two different kinds of one-to-one nonlinear mappings, the traditional gyroscope model with matched disturbances is transformed into an unconstrained one with both unmatched and matched disturbances, thus the predefined time-varying state constraints imposed on MEMS gyroscope can be achieved. To compensate for the lumped disturbances, a state estimator-based minimal learning parameter neural network is proposed to obtain fast and smooth disturbance estimates for both position and velocity control loops, which not only can eliminate the poor transient behaviors that widely appear in the available neural adaptive control with a large adaptive gain, but also greatly reduce the number of leaning parameters updated online. Furthermore, by employing a hysteresis logarithmic quantizer, the neglected difficulty, named as constrained data bandwidth of actuator can be overcome with less chattering in control signal, which is more convenient to implement. Finally, the neural adaptive control for MEMS gyroscope is developed such that satisfactory tracking performance is achieved despite of large disturbances, full-state constraints as well as quantized input. The effectiveness and advantages of the proposed control method are demonstrated through extensive simulations.

In this paper, the receiver aircraft of the AAR is modeled, and the disturbances are presented. The longitudinal and the lateral model of the receiver are correspondingly transformed into relevant three second-order systems and a third-order system. It makes the motion model more convenient for controller design, and the scale separation is avoided at the mean time. Then, the ADRC is firstly applied to the precise docking controller design to adequately reject the different complex disturbances during the docking of AAR. Comparative simulation results show that the ADRC achieves better performance in AAR docking control under complex flow interference.