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This article investigates the adaptive fuzzy tracking control design for uncertain nonstrict-feedback nonlinear systems with time-varying constraints and asymmetric input saturations. It is known that time-varying output constraint, time-varying error constraints, and input saturations are commonly-seen issues in many practical engineering systems due to inherent physical limitations and performance requirements. To deal with these problems and improve the convergence rate of the control system, a novel fixed-time convergent adaptive fuzzy control scheme is proposed for the considered uncertain nonlinear systems via the time-varying barrier Lyapunov function (BLF) technique. Firstly, to deal with the unknown nonlinearities and uncertainties in the system dynamic model, fuzzy logic systems are utilized to approximate the unknown functions of the considered model. Then, the problem of asymmetric input amplitude and rate saturations is handled by constructing a unified smooth characterizing function and an innovative auxiliary design signal system. Subsequently, considering physical limitations and performance requirements, a novel time-varying BLF-based adaptive fuzzy backstepping control scheme is designed for the uncertain nonstrict-feedback nonlinear systems to realize superior tracking performances and keep the states staying in predefined time-varying compact regions during operations. Further, rigorous theoretical analyses have been conducted to show that the proposed control scheme achieves fixed-time convergence of all signals in the closed-loop control system. Finally, representative simulation results verify the effectiveness of the proposed control scheme.

In this article, a novel method, called attention enhanced reinforcement learning (AERL), is proposed to address issues including complex interaction, limited communication range, and time-varying communication topology for multi agent cooperation. AERL includes a communication enhanced network (CEN), a graph spatiotemporal long short-term memory network (GST-LSTM), and parameters sharing multi-pseudo critic proximal policy optimization (PS-MPC-PPO). Specifically, CEN based on graph attention mechanism is designed to enlarge the agents' communication range and to deal with complex interaction among the agents. GST-LSTM, which replaces the standard fully connected (FC) operator in LSTM with graph attention operator, is designed to capture the temporal dependence while maintaining the spatial structure learned by CEN. PS-MPC-PPO, which extends proximal policy optimization (PPO) in multi agent systems with parameters' sharing to scale to environments with a large number of agents in training, is designed with multi-pseudo critics to mitigate the bias problem in training and accelerate the convergence process. Simulation results for three groups of representative scenarios including formation control, group containment, and predator-prey games demonstrate the effectiveness and robustness of AERL.

It is commonly believed that observer-based compensation is an effective way for disturbance rejection. A less talked about fact is that such disturbance rejection control technique may also degrade control performance. In this article, we present a typical cross-coupling system to reveal this problem and, more importantly, propose a new design principle of conditional disturbance negation (CDN) to eliminate its potential drawbacks of disturbance observer-based compensation. Qualitative analysis is first given for a general form of such cross-coupling systems, indicating the necessity of CDN. The analysis and control design principle of CDN is then exemplified through two applications. A numerical linear application produces abundant quantitative results through the powerful transfer function and frequency domain tools. A more complex nonlinear flexible air-breathing hypersonic vehicle application shows how conventional compensation deteriorates the couplings between rigid and flexible modes, and validates the effectiveness of CDN through comprehensive model analysis and simulation results. The proposed CDN design principle also arouses awareness of the importance of: 1) understanding the characteristics of the plant to be controlled and 2) recognizing the critical role the information plays in engineering practice.

In this paper, a novel time-varying formation tracking control scheme in fixed-time framework under directed topologies is proposed for multi-agent systems (MASs), with consideration of uncertainties and the absence of the leader’s velocity measurements (LVMs). First, a novel cascaded fixed-time state observer (CFTSO) under directed topologies without LVMs is developed for each follower to acquire the estimates of the leader’s states (LSs) in a fixed time. Then, minimal learning parameter (MLP) methods combining with radial basis function neural networks (RBFNNs) are utilized to cope with the uncertainties. Finally, on the basis of the designed CFTSO and MLP, a new formation control scheme in fixed-time framework under directed topologies is established to solve the time-varying formation tracking control problem.

This paper presents a novel robust adaptive tracking control method for a hypersonic vehicle in a cruise flight stage based on interval type-2 fuzzy-logic system (IT2-FLS) and small-gain approach. After the input-output linearization, the vehicle model can be decomposed into two uncertain subsystems by considering matching disturbances and parametric uncertainties. For each subsystem, an interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2-TSK-FLS) is then employed to approximate the unavailable model information. Following the idea of a small-gain approach, a composite feedback form for each subsystem is constructed, based on which the final robust adaptive tracking control law is developed. Rigorous stability analysis shows that all signals in the derived closed-loop system are kept uniformly ultimately bounded (UUB). The main contribution of this paper is that the proposed control law for the hypersonic vehicle is with only two adaptive parameters in total which can greatly alleviate the computation and storage burden in practice; meanwhile its superiority over the conventional minimal-learning-parameter (MLP)-based one is specifically illustrated. Comparative numerical simulations of three cases demonstrate the effectiveness of our proposed control method with respect to complicated uncertainties.

This paper presents a novel fixed-time sliding mode disturbance observer (SMDO)-based robust backstepping cruise tracking control scheme with closed-loop finite-time convergence for flexible air-breathing hypersonic vehicles (FAHVs). In order to enhance the control system’s robustness, a fixed-time SMDO is designed to compensate for the flexibility effects, model uncertainties, and external disturbances in FAHVs. In consequence, fixed convergence time of disturbance observation is achieved independently of initial estimation errors. Furthermore, velocity and altitude continuous finite-time tracking controllers are constructed by incorporating the SMDO and nonsmooth backstepping technique. To solve the problem of “explosion of complexity” in the conventional backstepping approach, nonsmooth filters are specifically constructed to generate the derivatives of virtual control laws. A Lyapunov-based stability analysis is conducted to show the finite-time convergence of the closed-loop FAHV control system. Finally, several representative numerical simulations are given to illustrate the effectiveness and superiority of the proposed control strategy.

It is commonly believed that uncertainties are obstacles to the tracking performances of flexible air-breathing hypersonic vehicles (FAHVs). In addition, the estimation of unmeasured states in an FAHV makes this issue much more complicated. To deal with these difficulties, this article explores a novel adaptive fuzzy nonsmooth backstepping output-feedback control scheme for the FAHV with closed-loop finite-time convergence. First, to approximate the unknown dynamics of the FAHV, some function approximators are constructed by utilizing an interval type-2 (IT2) fuzzy logic system. On this basis, a fixed-time convergent adaptive IT2 fuzzy observer is designed to estimate the unmeasured flight path angle and the unmeasured angle of attack of the FAHV accurately and rapidly. Consequently, the estimation errors of the designed observer are convergent in a fixed time independent of their initial estimation errors. Furthermore, based on the estimated states, velocity and altitude tracking controllers are designed by using a finite-time adaptive IT2 fuzzy nonsmooth backstepping control technique, which avoids the problem of “explosion of complexity” in traditional backstepping methods. Subsequently, rigorous Lyapunov stability analysis is conducted to show the finite-time convergence of the closed-loop signals of the FAHV control system. Finally, the robustness and superiority of the proposed control scheme is validated by several simulations in representative scenarios.

This study proposes a novel control scheme to investigate the time-varying formation tracking control problem for multi-agent systems with model uncertainties and the absence of leader's velocity measurements. For each agent, a novel fixed-time cascaded leader state observer (CLSO) without velocity measurements is first designed to reconstruct the states of the leader. Radial basis function neural networks (RBFNNs) are adopted to deal with the model uncertainties online. Taking the square of the norm of the NN weight vector as a newly developed adaptive parameter, a novel RBFNN-based adaptive control scheme with minimal learning-parameter approach and fixed-time CLSO is then constructed to tackle the time-varying formation tracking problem. The uniform ultimate boundedness property of the formation tracking error is guaranteed through Lyapunov stability analysis. Finally, two simulation scenario results demonstrate the effectiveness of the proposed formation tracking control scheme.

Uncertainty and measurement noise are main obstacles that limit the tracking control performances of flexible air-breathing hypersonic vehicles (FAHVs). In this article, we propose a novel fixed-time convergent nonsmooth backstepping control scheme for FAHV via augmented sliding mode observers (ASMOs) to overcome these obstacles. The ASMOs are first designed for the FAHV dynamics by employing the measured states corrupted by noises as inputs. On one hand, the ASMOs can simultaneously estimate the uncertainties and filter out the measurement noises. On the other hand, the observation error of each ASMO can be convergent within a fixed time independent of its initial observation error. Then, based on the estimation results, the altitude and velocity tracking controllers are developed by using fixed-time nonsmooth backstepping technique. Afterwards, a Lyapunov-based stability analysis is given to illustrate the fixed-time convergence of the closed-loop signals of the FAHV control system. Finally, comparative simulations are conducted to illustrate the superiority of the proposed control scheme.

The design of an immersion and invariance (I&I) based composite adaptive control for a class of uncertain nonlinear systems is presented in this paper. The key feature of this control scheme lies in the construction of the novel adaptive laws, aiming to address both parametric and non-parametric uncertainties simultaneously. Composite adaptive laws, which are driven by both the information of tracking error and prediction error, are first proposed using I&I theory for the estimations of parametric uncertainties. Then the technique of σ-modification is used to guarantee the stability in the presence of non-parametric uncertainties. Stability analysis is presented using the Lyapunov theory. Improved performance of the proposed control scheme is observed via numerical simulations.

The interval data from different surveyed persons for one linguistic word can reflect the intra- and inter-uncertainties of the word. This study shows how to construct shadowed set models for linguistic words based on the surveyed interval data. Firstly, corresponding to the popularly used fuzzy sets for linguistic words, four kinds of shadowed sets are introduced according to their shapes and named as the normal, the left-shoulder, the right-shoulder, and the non-cored shadowed sets. A data-driven approach that utilizes different statistics to determine the shapes and parameters of the shadowed set models is then proposed. The proposed data-driven approach includes two methods; the first is the tolerance limit method depending on the mean and standard deviation of the remaining interval data after pre-processing, whilst the other is the percentile method relying on the percentiles of the remaining interval data. Additionally, to evaluate the modelling performance, three novel indices are presented to measure the uncertainty-capture capability and accuracy of the constructed shadowed set models. Finally, the proposed approach is applied to two real-world problems . One is the modelling of 32 words in a codebook, and the other is the modelling of the thermal feeling words. The proposed methods are compared with other interval data driven methods, e.g. the enhanced interval approach and the fuzzy statistic method. Our results show that the proposed percentile method performs better in both applications. The proposed approach can also be applied to some other linguistic word modelling applications when it is reasonable to adopt shadowed sets as the words’ models.

Finding collision-free and efficient paths in an uncertain dynamic environment is a challenge for robot navigation tasks, especially when there are external autonomous agents that also have decision-making abilities in the same environment. This paper develops a novel method based on DRL with graph attention network (GAT) to solve the problem of robot navigation among external autonomous agents (other agents). Specifically, GAT is adopted to describe the robot and other agents as a specific graph, and extract the spatial structural influence features of other agents on the robot from the graph. Multi-head attention mechanism is utilized to calculate the weights of interactions between the robot and other agents. This GAT uses observations of an arbitrary number of other agents in dynamic environments. Furthermore, the proposed method is combined with optimal reciprocal collision avoidance to improve its safety in new environments. Various simulations demonstrate that our method has good performance and robustness in different environments.

Multi-agent formation control with obstacles avoidance (MAFC-OA) is one of the attractive tasks of multi-agent cooperation. Although a number of algorithms can achieve formation control effectively, they ignore the nature structure feature of the graph formed by agents. Given this problem, a model, MAFC-OA, which is composed of observation attention network, action attention network and Multi-long short-term memory (Multi-LSTM) is proposed. With MAFC-OA, the agents can be trained to form the desired formation and avoid dynamic obstacles in the environments with restricted communication. Specifically, the above two attention networks not only incorporate the influence of the nearby agents’ observation and actions, but also enlarge the agents’ receptive field (communication range) through the chain propagation characteristics to promote cooperation among agents. Moreover, the Multi-LSTM allows the agents to take obstacles into consideration in the order of distance and to avoid the obstacles effectively. Simulations demonstrate that the agents can form the desired formation and avoid dynamic obstacles effectively.

Multi-agent cooperation is one of the most attractive research fields in multi-agent systems. There are many attempts made by researchers in this field to promote the cooperation behavior. However, in partially-observable environments, a large number of agents and complex interactions among the agents cause huge difficulty for policy learning. Moreover, redundant communication contents caused by many agents make effective features hard to be extracted, which prevents the policy from converging. To address the limitations above, a novel method called multi-agent cognition difference reinforcement learning (MACD-RL) is proposed in this paper. The key feature of MACD-RL lies in cognition difference network (CDN) and a soft communication network (SCN). CDN is designed to allow each agent to choose its neighbors (communication targets) adaptively with its environment cognition difference. SCN is designed to handle the complex interactions among the agents with soft attention mechanism. The results of simulations including mixed cooperative and competitive tasks demonstrate that the effectiveness and robustness of the proposed model.

In this paper, a novel state-estimator-integrated robust adaptive tracking control law is proposed for flexible air-breathing hypersonic vehicle (FAHV), with consideration of noisy measurements, parametric uncertainties, and unknown flexible dynamics. First, to reconstruct the states contaminated by the measurement noises, a continuous-model-based state estimator is designed, which can avoid severe phase lag problem brought by low-pass filter and greatly improve the estimation accuracy in the transition process. Then, based on noise-free measurements, an ideal state feedback robust adaptive tracking controller is formulated to deal with the parametric uncertainties as well as unknown flexible dynamics, where interval type-2 fuzzy logic systems are employed to approximate the unknown dynamics of FAHV online. Lyapunov theorem is utilized to analyze the stability properties of the state estimator and the ideal state feedback tracking controller. By synthesizing the above two parts, the whole state-estimator-integrated robust adaptive tracking control law is finally developed. Comparative numerical simulations of four scenarios demonstrate the effectiveness and superiority of the proposed integrated control law.

This paper presents a conditional disturbance negation (CDN) based active disturbance rejection control (ADRC) scheme for the velocity and altitude tracking control of flexible air-breathing hypersonic vehicles (FAHVs) in the presence of various uncertainties and disturbances. A pioneering CDN module is proposed to selectively compensate the total disturbances according to the characteristics of the total disturbances and the FAHV dynamics, which is aimed at canceling some detrimental disturbances while profiting from other beneficial disturbances. Firstly, model-based extended state observers are applied to estimate the total disturbances in FAHVs in an efficient way. Based on the Lyapunov theory and the estimated disturbances, a novel disturbance characterization indicator (DCI) is then designed to show whether the total disturbance harms or benefits the tracking performance in engineering practice. Then, DCI is utilized to construct the CDN module. In consequence, the CDN based FAHV tracking control is implemented in an ADRC framework, yielding the CDN based ADRC scheme. The stability analysis is conducted to show the convergence of the closed-loop system. Finally, the effectiveness and superiority of the proposed control scheme are validated by some representative simulations in various flight conditions.

This study is concerned with the augmented fixed-time observer based adaptive super-twisting cruise control for flexible air-breathing hypersonic vehicles (FAHVs). First, the input-output linearisation technique is employed to derive a control oriented FAHV dynamic model. Then, a super-twisting controller is designed to track the reference velocity and altitude commands when faced with various uncertainties and disturbances. In addition, a novel gain adaptation law is constructed as a supplement of the super-twisting controller to avoid overestimating the values of controller gains. The adaptation law keeps the controller gains adapting dynamically to guarantee the finite-time convergence of sliding variables in the presence of disturbances with unknown boundaries. Moreover, augmented fixed-time observers are designed to estimate the true values of measured states and filter out high-frequency sensor noises. Thus, the fixed convergence time of these observers is achieved independently of initial estimation errors. Finally, the augmented fixed-time observers and adaptive super-twisting controller are combined together to ensure the superior tracking performances of FAHVs under the effects of uncertainties, disturbances, and measurement noises.

In lots of data based prediction or modeling applications, uncertainties and&#x002F or noises in the observed data cannot be avoided. In such cases, it is more preferable and reasonable to provide linguistic &#x0028 fuzzy &#x0029 predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers. Linguistic dynamic system &#x0028 LDS&#x0029 provides a powerful tool for yielding linguistic &#x0028 fuzzy &#x0029 results. However, it is still difficult to construct LDS models from observed data. To solve this issue, this paper first presents a simplified LDS whose input-output mapping can be determined by closed-form formulas. Then, a hybrid learning method is proposed to construct the data-driven LDS model. The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method, then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules, and finally adopts multi-objective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets. The proposed approach is successfully applied to three real-world prediction applications which are: prediction of energy consumption of a building, forecasting of the traffic flow, and prediction of the wind speed. Simulation results show that the uncertainties in the data can be effectively captured by the linguistic &#x0028 fuzzy&#x0029 estimates. It can also be extended to some other prediction or modeling problems, in which observed data have high levels of uncertainties.

This paper considers a multi-target coverage problem where a robot team aims to efficiently cover multi-targets while maintaining connectivity in a distributed manner. A novel knowledge-incorporated policy framework is proposed to derive a distributed, efficient, and connectivity guaranteed coverage policy. In particular, a knowledge-guided policy network (KGPnet) is designed, which consists of observation attention representation, interaction attention representation, and knowledge-guided policy learning. Giving credit to the KGPnet, the connectivity guaranteed coverage policy can be applied to different number targets. Moreover, based on the knowledge of the algebraic connectivity and coverage rate, a comprehensive reward is designed to guide the training of the behavior of multi-target coverage with connectivity maintenance. Furthermore, since the policy learned through deep reinforcement learning (DRL) can not guarantee the connectivity of the robot team, a knowledge-nested policy filtering is designed to filter dis-connectivity policies to satisfy the connectivity constraint based on the knowledge model of connectivity maintenance. Various simulations are conducted to verify the effectiveness of the proposed method. Besides, numerous real-world experiments with three-wheel omnidirectional cars and a motion capture system are presented to demonstrate the practicability of the proposed method.

In this paper, we propose a novel method of generating and executing active impact motion on a quadruped robot by mimicking active impact motion of quadruped animals. In order to generate active impact motion postures, we divide the whole active impact process into three phases and present a Center of Gravity (COG) trajectory planning method for these phases. A controller based on virtual model control (VMC) is used for active impact motion execution and a pitch attitude adjustment controller is utilized to stabilize the robot. Besides, a Hopf oscillator-based Central Pattern Generator (CPG) network with controllable locomotion stride is responsible for generating control signals of quadruped gaits during active impact motion. In order to verify effectiveness of the proposed control strategy, we show experimental results that a quadruped robot pushes the target object weighing 15 kg away for 50 cm by executing active impact motion.

The multi-agent reinforcement learning (MARL) suffers from several issues when it is applied to large-scale environments. Specifically, the communication among the agents is limited by the communication distance or bandwidth. Besides, the interactions among the agents are complex in large-scale environments, which makes each agent hard to take different influences of the other agents into consideration and to learn a stable policy. To address these issues, a soft graph attention reinforcement learning (SGA-RL) is proposed. By taking the advantage of the chain propagation characteristics of graph neural networks, stacked graph convolution layers can overcome the limitation of the communication and enlarge the agents’ receptive field to promote the cooperation behavior among the agents. Moreover, unlike traditional multi-head attention mechanism which takes all the heads into consideration equally, a soft attention mechanism is designed to learn each attention head’s importance, which means that each agent can learn how to treat the other agents’ influence more effectively during large-scale environments. The results of the simulations indicate that the agents can learn stable and complicated cooperative strategies with SGA-RL in large-scale environments.

Collective behavior of multi-agent systems brings some new problems in control theory and application. Especially, flocking problem of multi-agent systems with uncertain nonlinear dynamics and unknown external disturbances is a challenging problem. Some existing works assume that the intrinsic nonlinear dynamics of virtual leader is the same as those of the agents, which is unreasonable and impractical. To solve this issue, we consider an adaptive flocking problem of multi-agent systems with uncertain nonlinear dynamics and unknown external disturbances in this paper, where the intrinsic nonlinear dynamics of virtual leader is allowed to be different from the agents. Firstly, to approximate the uncertain nonlinear dynamics of each agent, an adaptive neural network is used, whose weights are updated online. Furthermore, an adaptive robust signal is designed to counteract the unknown external disturbances and neural network approximation errors, which is independent with the upper bound of the unknown external disturbances and neural network approximation errors. Moreover, an adaptive flocking control law is designed, which is proved that the flocking can be realized and the velocity errors converge to a small neighbor of the origin based on Lyapunov stability theory. Finally, the robustness and superiority of the proposed robust adaptive flocking control law are validated by two representative simulations.

Generating collision-free, time-efficient paths in an uncertain dynamic environment poses huge challenges for the formation control with collision avoidance (FCCA) problem in a leader–follower structure. In particular, the followers have to take both formation maintenance and collision avoidance into account simultaneously. Unfortunately, most of the existing works are simple combinations of methods dealing with the two problems separately. In this article, a new method based on deep reinforcement learning (RL) is proposed to solve the problem of FCCA. Especially, the learning-based policy is extended to the field of formation control, which involves a two-stage training framework: an imitation learning (IL) and later an RL. In the IL stage, a model-guided method consisting of a consensus theory-based formation controller and an optimal reciprocal collision avoidance strategy is designed to speed up training and increase efficiency. In the RL stage, a compound reward function is presented to guide the training. In addition, we design a formation-oriented network structure to perceive the environment. Long short-term memory is adopted to enable the network structure to perceive the information of obstacles of an uncertain number, and a transfer training approach is adopted to improve the generalization of the network in different scenarios. Numerous representative simulations are conducted, and our method is further deployed to an experimental platform based on a multiomnidirectional-wheeled car system. The effectiveness and practicability of our proposed method are validated through both the simulation and experiment results.

Making efficient motion decisions for a multi-robot system is a challenging problem in target encirclement with collision avoidance. Specifically, each robot with local communication has to consider cooperative target encirclement and collision avoidance simultaneously. In this paper, a distributed transferable policy network framework based on deep reinforcement learning is proposed to solve the problem of multi-robot cooperative target encirclement with collision avoidance. The proposed policy network framework is able to process the information of uncertain number of robots and obstacles, which is a desirable property for multi-robot systems. In particular, graph attention communication mechanism is adopted to model multi-robot interactions as a graph and extract cooperative information from the graph. Long short-term memory is used to accept the states of uncertain number of obstacles. In addition, a compound reward is designed to lead the training of the behavior of target encirclement with collision avoidance. Curriculum learning is implemented to speed up the process of this training. Simulation results validate the effectiveness of the proposed algorithm. Moreover, we further show that the learned policy can directly transfer to different scenarios along with good generalization.