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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.

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.

Multiagent cooperation is one of the most attractive research fields in multiagent systems. There are many attempts made by researchers in this field to promote cooperation behavior. However, several issues still exist, such as complex interactions among different groups of agents, redundant communication contents of irrelevant agents, which prevents the learning and convergence of agent cooperation behaviors. To address the limitations above, a novel method called multiagent hierarchical cognition difference policy (MA-HCDP) is proposed in this paper. It includes a hierarchical group network (HGN), a cognition difference network (CDN), and a soft communication network (SCN). HGN is designed to distinguish different underlying information of diverse groups’ observations (including friendly group, enemy group, and object group) and extract different high-dimensional state representations of different groups. CDN is designed based on a variational auto-encoder 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 a soft attention mechanism. The results of simulations demonstrate the superior effectiveness of our method compared with existing methods.

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.

Multi-agent reinforcement learning (MARL) has made significant advances in multi-agent systems. However, it is hard to learn a stable policy in complicated and changeable environment. To address these issues, a two-level attention network is proposed, which is composed of across-group observation attention network (AGONet) and intentional communication network (ICN). AGONet is designed to distinguish the different semantic meanings of observations (including friend group, foe group, and object/entity group) and extract different underlying information of different groups with across-group attention. Based AGONet, the proposed network framework is invariant to the number of agents existing in the system, which can be applied in large-scale multi-agent systems. Furthermore, to enhance the cooperation of the agents in the same group, ICN is used to aggregate the intentions of neighbors in the same group, which are extracted by AGONet. It obtains the understanding and intentions of their neighbors in the same group and enlarges the receptive filed of the agent. The simulation results demonstrate that the agents can learn complicated cooperative and competitive strategies and our method is superiority to existing methods.

Multi-agent cooperation is one of the attractive aspects in multi-agent systems. However, during the process of cooperation, communication among agents is limited by the distance or the bandwidth. Besides, the agents move around and their neighbors appear or vanish, which makes the agents hard to capture temporal dependences and to learn a stable policy. To address these issues, a Spatial-Temporal Graph Attentional Long Short-Term Memory (LSTM) Scheme (STGA-LSTM), which is composed of spatial capture network and spatiotemporal LSTM network, is proposed. The spatial capture network is designed based on graph attention network to enlarge the agents’ communication range and capture the spatial structure of the multi-agent system. Based on the standard LSTM, a spatiotemporal LSTM network, which is in combination with graph convolutional network and attention mechanism, is designed to capture the temporal evolutionary patterns while keeping the spatial structure learned by spatial capture network. The results of simulations including mixed cooperative and competitive tasks indicate that the agents can learn stable and complicated strategies with STGA-LSTM.

In this paper, a novel adaptive finite-time control of air-breathing hypersonic vehicles is proposed. Based on the immersion and invariance theory, an adaptive finite-time control method for general second-order systems is first derived, using nonsingular terminal sliding mode scheme. Then the method is applied to the control system design of a flexible air-breathing vehicle model, whose dynamics can be decoupled into first-order and second-order subsystems by time-scale separation principle. The main features of this hypersonic vehicle control system lie in the design flexibility of the parameter adaptive laws and the rapid convergence to the equilibrium point. Finally, simulations are conducted, which demonstrate that the control system has the features of fast and accurate tracking to command trajectories and strong robustness to parametric and non-parametric uncertainties.

This study presents an observer-based robust adaptive type-2 (T2) fuzzy tracking control scheme for the longitudinal dynamics of flexible air-breathing hypersonic vehicles. The control scheme is composed of three parts, namely an uncertain dynamics approximator, a reduced-order adaptive state observer, and command filter-based backstepping altitude and velocity controllers. First, the approximator is implemented by utilising interval T2 fuzzy logic systems for approximating unknown vehicle dynamics in the control scheme. Then, the reduced-order adaptive state observer is designed by the combination of the approximator and output state feedback techniques, which guarantee the estimation of unmeasurable states as well as uncertain dynamics identification. By using the linear matrix inequalities technique, a new condition for obtaining the gains of the state observer is presented. Next, on the basis of the designed approximator and state observer, the altitude and velocity tracking controllers are constructed by command-filtered backstepping control approach which avoids the explosion of complexity in conventional backstepping design. Thorough stability analysis of the closed-loop systems is conducted. Nominal and comparison simulation studies considering parametric uncertainty, incomplete state measurement, and measurement noises are carried out, illustrating the effectiveness of the proposed control scheme.

A novel adaptive control with σ-modification for uncertain nonlinear systems is proposed in the paper. The application of conventional adaptive control is severely limited by the problems of construction of Lyapunov function and parameter drift because of non-parametric uncertainties. The proposed adaptive control that is on the basis of the immersion and invariance theory and σ-modification can be used to deal with these problems to some extent. It turns out to be a structured design method without requiring a Lyapunov function in the design level and robust to non-parametric uncertainties. Moreover, constrained command filter backstepping is adopted to meet the amplitude and rate constraints on the states and actuators. The uniformly ultimately bounded stability of the closed-loop system has been analyzed by Lyapunov theory with parametric and non-parametric uncertainties of the controlled model. To demonstrate the design flexibility, the method is applied to the position tracking control system design of a mass-damper-spring system and the flight control system design of a scramjet-powered air-breathing hypersonic vehicle. Finally, the effectiveness of the proposed adaptive control method is illustrated by numerical simulations.

An indirect adaptive interval type-2 fuzzy sliding mode controller (AIT2-FSMC) for flexible air-breathing hypersonic vehicle (FAHV) longitudinal model is presented in this paper. The proposed controller is designed by combining an adaptive Mamdani linguistic based interval type-2 fuzzy logic system (IT2-FLS) with sliding mode control technique. For the sake of the FAHV longitudinal model stably controlled under parametric and structural uncertainties which mainly come from varying aerodynamic interferences, flexible modes in airframe mutual couplings and fuel consumptions in practical conditions, we decouple the model through feedback linearization and design the main controller by sliding mode control technique to achieve the system convergence. Moreover, three adaptive interval type-2 fuzzy logic systems, which uses difference combinations of velocity and altitude tracking errors, pitch angle and slip slide angle as the inputs of antecedent sets, are designed to estimate nonlinear time varying functions and inverse matrix with bounded uncertainties. The adaptive law of the IT2-FSMC is derived through Lyapunov synthesis approach to guarantee the system asymptotic stability. Several comparisons under different levels of complex parametric and structural uncertainties have been done. The simulation results validate the performance of the proposed controller with robustness and effectiveness.

Navigation in a complex dynamic environment is one of the most attractive tasks. Although most of such algorithms can achieve navigation tasks effectively, they ignore the necessity of the mission planning in the process of navigation. Given the situation, a novel end-to-end two-stage deep reinforcement learning architecture for a time-sequence navigation and action-decision in a dynamic environment with randomly rapidly moving obstacles is proposed in this paper. During the first-stage training, a network with spatial and temporal information is designed to process the navigation task while a conventional recurrent full-connected network is adopted to resolve the action-decision task. During the second-stage training, the two networks are integrated and trained online with dynamic entropy to obtain a stable policy for dynamic missions. Simulations demonstrate that the navigation and the action-decision in different environments can be completed effectively under our architecture.

Beyond-visual-range (BVR) air-to-air unmanned aerial vehicles (UAVs) combat has complex and varied characteristics. It always performs in a multiple UAVs cooperative way. This paper proposes a BVR air-to-air combat tactical strategy for multiple UAVs to achieve effective confrontations. The strategy is implemented by incorporating several one-versus-one air-to-air combat cases. For each one-versus-one case, a decision action space and a situation assessment function are developed. Then a minmax method based decision algorithm is designed to obtain the optimal tactic. Afterwards, to realize the optimal tactic, flight controllers for each UAV are designed. The superiority of the proposed BVR air-to-air combat tactical action decision strategy is validated by simulations in three representative scenarios.

This paper presents a novel quadruped robot, “Biodog II”, featuring low-inertia legs and coaxial transmission. Each leg has two degrees of freedoms (DOFs)-a knee and hip joint, which are actuated by proximal mounted DC motors, and a compliant ankle joint. Motor power can be transited to active joints efficiently and accurately by means of coaxial transmission. In particular, we employ a tendon-bone co-location structure on the leg to reduce stress and enhance strength. A Hopf oscillator-based Central Pattern Generator (CPG) is responsible for generating locomotion trajectories of different gaits. An embedded controller based on an ARM board is designed to command motors. Locomotion experiments including walk and trot gait test demonstrate the stability and mobility of Biodog II.

This paper investigates the time-varying formation tracking control problem for multi-agent systems with consideration of model uncertainties. For each dimension of an agent, a radial basis function neural network (RBFNN) is first adopted to approximate the model uncertainties online. Taking the square of the norm of the neural network weight vector as a newly developed adaptive parameter, a novel RBFNN-based adaptive control law with minimal learning parameter (MLP) approach is then constructed to tackle the time-varying formation tracking problem. The uniformly ultimately boundedness (UUB) of formation tracking errors is guaranteed through Lyapunov analysis. Compared with other traditional RBFNN-based formation tracking control laws for multi-agent systems, very few parameters need to be updated online in our proposed one, which can greatly lessen the computational burden. Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed adaptive control law.

The formation control of multi-robot systems in complex environments is a challenging problem, which needs to handle velocity estimation and obstacle avoidance problems. However, few researches considered these problems simultaneously in the literature. In this paper, a leader-follower hybrid formation control design for multi-robot system is proposed, which simultaneously solves leader-follower formation control problem, velocity estimation problem, and obstacle avoidance problem. First, based on the leader-follower architecture, formation control law is designed. Second, considering the difficulty of obtaining the whole accurate velocity information, a velocity estimator of the leader is designed, and the Lyapunov function is used to analyze the stability of the multi-robot system. In addition, the artificial potential field (APF) method is adopted to avoid obstacles in complex environments. Finally, an experimental platform of the multi-robot system is constructed, and simulation and experimental results verify the effectiveness performance of the leader-follower hybrid formation control design for the multi-robot system.

Generating collision free, time efficient paths for followers is a challenging problem in formation control with collision avoidance. Specifically, the followers have to consider both formation maintenance and collision avoidance at the same time. Recent works have shown the potentialities of deep reinforcement learning (DRL) to learn collision avoidance policies. However, only the collision factor was considered in the previous works. In this paper, we extend the learning-based policy to the area of formation control by learning a comprehensive task. In particular, a two-stage training scheme is adopted including imitation learning and reinforcement learning. A fusion reward function is proposed to lead the training. Besides, a formation-oriented network architecture is presented for environment perception and long short-term memory (LSTM) is applied to perceive the information of an arbitrary number of obstacles. Various simulations are carried out and the results show the proposed algorithm is able to anticipate the dynamic information of the environment and outperforms traditional methods.

Aiming at achieving smooth gait transition for quadruped robots, we present a Hopf oscillator-based Central Pattern Generator (CPG) with controllable phase relationships in this paper. The proposed CPG controller can facilitate changing parameters to generate different locomotion gaits. Particularly, required phase relationships between every two oscillators can be obtained by modulating the duty factor and relative phases of legs. Therefore, smooth rhythmic signals for gait transition are generated. Moreover, the onset of the gait transition can be designed accurately. Finally, we verify the effectiveness of our controller on a simulated quadruped robot. Smooth and stable transition among walk, trot and pace gait has been achieved. Both transition from walk to trot and trot to pace are finished in a second.

Collision avoidance in formation control is an essential and challenging problem in multiagent filed. Specifically, the agents have to consider both formation maintenance and collision avoidance. However, this problem is not fully considered in existing works. This paper presents a hybrid collision avoidance method for formation control. The formation control is designed based on consensus theory while the collision avoidance is achieved by utilizing optimal reciprocal collision avoidance (ORCA). Furthermore, the stability of the multiagent systems is proved. Finally, a simulation demonstrates the effectiveness of the proposed method.

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

In wireless sensor networks (WSNs), it is critical to design an advisable transmission power allocation strategy for balancing the latency and energy efficiency, and prolonging the lifetime of WSNs. However, some measured key parameters, e.g., data latency, energy consumption and communication radius, are with high levels of uncertainties, which deteriorate the transmission power allocation performance greatly. How to employ an advanced method to deal with the uncertainties and to further improve the network performance is a pressing issue. Type-2 fuzzy logic system (T2FLS) as a powerful tool for handling the uncertainties provides an effective way for designing such advisable allocation strategies. Therefore, this paper adopts the interval T2FLS (IT2FLS) to design the transmission power allocation (TPA) strategy for lifetime maximization of WSNs. Firstly, the problem of lifetime enhancement in WSNs is formulated in detail, and then it is converted into a TPA problem. Secondly, the IT2FLS method is applied to the transmission power decision making process for maximizing the lifetime of WSNs. In the designed IT2FLS-based TPA strategy, expected latency, residual energy and distance between nodes are taken as input variables, while the transmission power and communication radius are considered as the output variables. Finally, both simulation and experiment results are given. The results indicate that the proposed TPA strategy using IT2FLS can effectively realize the tradeoff between the latency and energy efficiency, and can prolong the network lifetime of the WSNs. Moreover, compared with other TPA strategies, including the minimum total energy algorithm, the flow augmentation algorithm and the type-1 fuzzy logic method, the proposed IT2FLS-based TPA strategy has obvious advantages in terms of network lifetime, average latency and energy consumption.

Flexible air-breathing hypersonic vehicle (FAHV) inevitably suffers unknown aerodynamic disturbances as well as measurement noises in real flight. Although fixed-gain state estimator proposed in our previous work can effectively restrain the measurement noises, it may not perform well when large model disturbances and uncertainties exist. Focusing on this problem, we first study how the state estimator gains affect the transient process of FAHV. Then, the contradiction between accurate and fast state reconstruction versus high-frequency measurement noise suppression is analyzed. Based on the knowledge in choosing the state estimator gains, we design two fuzzy logic systems (FLSs), for the velocity channel and altitude channel respectively, to adapt the gains online. Finally, a novel knowledge-based fuzzy adaptive state estimator (FASE) is developed, which can realize accurate and fast state reconstruction in the transient process as well as high-frequency noise suppression in the steady-state process. Comparative simulation results verify the improvements of our proposed FASE on FAHV tracking performances.

A composite adaptive control design based on the immersion and invariance (I& I) theory is presented for the air-breathing hypersonic vehicles. The interest here is to achieve a robust trajectory tracking control of the reference commands in the presence of model parametric uncertainties. The main feature of the proposed control scheme lies in the design of the composite I& I adaptive estimators, which consist of tracking-error based adaptation law and prediction-error based adaptation law. The tracking-error based adaptation law is first constructed using I& I theory, then the prediction-error based adaptation law is added to it, thus makes a composite adaptive control law. Stability analysis is presented using Lyapunov theory and asymptotical convergence of the tracking errors to zero is accomplished. Representative simulations are performed, which illustrate the effectiveness and robustness of the composite control scheme.

This paper investigates the time-varying formation control problem for multiple unmanned aerial vehicle (multi-DAV) systems with time-varying delays and jointly connected topologies. Firstly, a consensus based formation control law is proposed to realize and maintain the desired time-varying formation in presence of time-varying delays and jointly connected topologies. Then, a sufficient condition in terms of linear matrix inequalities (LMI) is derived for formation control and the stability of the close-loop system is analyzed by employing Lyapunov-Krasovskii function. Finally, two task-oriented formation transformation cases are simulated to verify the effectiveness of the proposed control law, where the first is to shape varying regular hexagon, and the second is to avoid multiple obstacles.

In practice, the measurement noise arising from actual state measurement deteriorates the system performance significantly. To alleviate the noise effect on the hypersonic vehicle’s pitch angle and pitch rate channels (i.e. attitude subsystem), this paper designs the fuzzy approximation based adaptive controller. Based on the desired compensation technique, a novel adaptive update law is proposed in the fuzzy approximator, in which the desired values are used to replace the actual states. Hence, the corresponding fuzzy approximator is able to estimate the unknown nonlinearity in the system even when measurement noises exist. In addition, the matching error of the fuzzy approximation and residual uncertainties are handled through an additional robust control term in the control law. Stability analysis is conducted by employing the Lyapunov method. Finally, nominal and comparative simulation results show that the control strategies proposed in this paper can guarantee the bounded tracking performance with consideration of the measurement noise and uncertainty.

Central Pattern Generator (CPG) is able to produce rhythmic signals without sensory feedback or any rhythmic inputs, and is becoming popular for locomotion of quadruped robots. However, due to the lack of foot-ground contact force information, the swing phase and the stance phase of control signals generated by CPG are not consistent with the actual phase of the quadruped limb, so that the dynamic performance of quadruped robots controlled by CPG is not satisfying. Therefore, in this paper, force sensor is attached on the quadruped limb to detect the real-time contact force of foot with the substrate, and the actual contact-lift state of the limb. In addition, we analyze the characteristic of ground reaction force (GRF), and the relationship between GRF and the detected foot-ground contact force. Moreover, we discuss the effect of different CPG parameters on the foot-ground contact force, so as to lay basis for improving the dynamic performance of quadruped robots.

This paper investigates the time-varying formation control problem for multiple unmanned aerial vehicle (multi- UAV) systems with unknown uncertainties and varying time delays. Firstly, a radial basis function neural network (RBFNN) is adopted to estimate the lumped model uncertainties online for compensation. Then, a novel RBFNN-based fully distributed adaptive control scheme consisting of control law to stabilize the system and adaptive law to adjust RBFNN weights is developed to tackle the time-varying formation tracking problem in the presence of varying time delay. The uniformly ultimately boundedness (UUB) of the formation tracking errors is theoretically analyzed through Lyapunov approach. Comparative simulation results demonstrate the effectiveness of the control and adaptive laws proposed in this paper.

In this paper, a novel robust adaptive control scheme for a class of uncertain nonlinear systems based on interval type-2 fuzzy logic system (IT2-FLS) and small gain approach is proposed. An interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2-TSK-FLS) is employed to approximate the unknown dynamics of such a system. Based on the small gain theorem, a composite feedback form of the system is then established and a novel robust adaptive control law is developed, which can ensure all the signals in the close-loop system are uniformly ultimately bounded (UUB). Throughout the whole control scheme, only one parameter needs to be adapted online, which is different from most of existing IT2-FLSs. Numerical simulations demonstrate the robustness of our proposed scheme against uncertainties as well as the superiorities of IT2-TSK-FLS.