Adaptive Fault-Tolerant Formation Control of Heterogeneous Multi-Agent Systems under Directed Communication Topology.

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.

A Novel Piecewise Frequency Control Strategy Based on Fractional-Order Filter for Coordinating Vibration Isolation and Positioning of Supporting System.

A piecewise frequency control (PFC) strategy is proposed in this paper for coordinating vibration isolation and positioning of supporting systems under complex disturbance conditions, such as direct and external disturbances. This control strategy is applied in an active-passive parallel supporting system, where relative positioning feedback for positioning and absolute velocity feedback for active vibration isolation. The analysis of vibration and deformation transmissibility shows that vibration control increases low-frequency position error while positioning control amplifies high-frequency vibration amplitude. To overcome this contradiction across the whole control bandwidth, a pair of Fractional-Order Filters (FOFs) is adopted in the PFC system, which increases the flexibility in the PFC design by introducing fraction orders. The system stability analysis indicates that the FOFs can provide a better stability margin than the Integral-Order Filters (IOFs), so the control gains are increased to get a better performance on the AVI and positioning. The PFC based on FOFs can suppress the peak amplitude at the natural frequency which cannot be avoided when using the IOFs. The constrained nonlinear multivariable function is formed by the required performance and the stability of the system, then the controller parameters are optimized effectively. Lastly, the effectiveness of the proposed method is verified by experiments.

Sensor Fault and Delay Tolerant Control for Networked Control Systems Subject to External Disturbances.

In this paper, the problem of sensor fault and delay tolerant control problem for a class of networked control systems under external disturbances is investigated. More precisely, the dynamic characteristics of the external disturbance and sensor fault are described as the output of exogenous systems first. The original sensor fault and delay tolerant control problem is reformulated as an equivalence problem with designed available system output and reformed performance index. The feedforward and feedback sensor fault tolerant controller (FFSFTC) can be obtained by utilizing the solutions of Riccati matrix equation and Stein matrix equation. Based on the designed fault diagnoser, the proposed FFSFTC is further reconstructed to compensate for the sensor fault and delayed measurement effects. Finally, numerical examples are provided to illustrate the effectiveness of our proposed FFSFTC with different cases with various types of sensor faults, measurement delays and external disturbances.

Distributed adaptive robust containment control for reaction-diffusion neural networks with external disturbances under directed graphs.

In this paper, the leader-follower robust synchronization issue is mainly addressed for reaction-diffusion neural networks (RDNNs) with multiple leaders and external disturbances under directed graphs. Based on the σ modification approach, we propose a novel distributed adaptive controller by adding a term [Formula: see text] to avoid the phenomenon of parameter drift, that is, the adaptive parameters grow to infinity. Meanwhile, different from the adaptive control algorithm proposed in the undirected graph, we introduce a new function χi(t) to provide additional freedom for the design to achieve robust containment when confronted with external disturbances. Further, the robustness of tracking synchronization with one leader is guaranteed by the proposed adaptive controller when the external disturbances concerning L2 norm are bounded. Finally, relevant numerical simulation graphics are displayed separately to verify the correctness of the related theoretical results.

Fixed-time convergence attitude control for a tilt trirotor unmanned aerial vehicle based on reinforcement learning.

This paper presents a new nonlinear robust attitude control strategy for the tilt trirotor unmanned aerial vehicle (UAV). Fixed-time convergence control of the UAV's attitude tracking errors under the effects of model uncertainties and unknown external disturbances is achieved by utilizing the proposed control design. Actor-critic (AC) structure based neural networks are trained only with the information of the UAV's inputs and outputs data, to handle the UAV's modeling uncertainties with bounded estimation error. Then a sliding-mode based fixed-time controller is designed to compensate the approximation error of the neural networks and the unknown external disturbances. Based on the Lyapunov stability theory, the stability analysis of the closed-loop system is presented. The performance of the presented nonlinear robust control strategy is validated through the real-time flight experiments.

Global stabilization via output feedback for a class of uncertainty nonlinear systems with time-varying delay and zero dynamics.

This paper proposes a new control strategy to deal with three different types of uncertainties for a class of time-varying delay nonlinear systems. Quite different from related celebrated works, the considered systems allow the existence of parameter uncertainties in the output function and nonlinearities, dynamic uncertainties of the unmeasurable state, and continuous external disturbances, which invokes the motivation of the paper. The objective is to construct a continuous output feedback controller by utilizing a new restructured integral function and the double-domination method. The novelty control design is the capability of tackling zero-dynamic and unknown output function simultaneously, thereby guaranteeing the state convergence of the closed-loop system. Finally, the proposed scheme is applied to a practical example and a numerical one simultaneously to demonstrate the effectiveness and the superiority.

Disturbance observer-based fuzzy event-triggered ISMC design: Tracking performance.

This article presents a disturbance observer (DO)-based event-triggered integral sliding mode tracking control (ISMTC) for continuous-time Takagi-Sugeno (T-S) fuzzy model (TSFM) subject to external disturbances. Merging the event-triggered control (ETC) with integral sliding mode control (ISMC) approach is led to reach the better accommodate the features of ISMC. To do this, two forms of fuzzy integral sliding surfaces (FISSs) are proposed. The first is the periodic-time-based FISS that is presented to provide the robustness of the tracking performance from the initial moment. The second is the event-triggered-based FISS which is proposed to obtain the fuzzy event-triggered ISMTC law. To more decrease the chattering effects and compensate the tracking performance against the disturbances, a fuzzy disturbance observer (FDO) is proposed to estimate the mismatched disturbances. Compared with the existing works, a more practical controller is proposed based on the asynchronous premise variables. Utilizing the common quadratic Lyapunov function, appropriate conditions are derived to verify that the tracking error is robust from the initial moment. Furthermore, by adopting a decaying triggering threshold, it is guaranteed that the system is Zeno-free during the process. To verify the effectiveness of the suggested event-triggered ISMTC, a practical system including continuous stirred tank reactor (CSTR) is simulated and the results are compared.

Composite fault reconstruction and fault-tolerant control design for cyber-physical systems: An interval type-2 fuzzy approach.

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber-physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass-spring-damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy.

Distributed neurodynamic optimization for multi-energy management with time-varying external disturbances considering time-varying emission limitations and load demand in multi-microgrid.

An emerging time-varying distributed multi-energy management problem (MEMP) considering time-varying load and emission limitations for resisting time-varying external disturbances and communication time delays in the multi-microgrid (MMG) system is investigated. Each microgrid (MG) contains some smaller microgrids (SMGs), which are connected by energy routers (ERs) of the system and can monitor energy in real-time with each other. In addition, a time-varying multi-energy management optimization model (MEMOM) is proposed in this paper in order to minimize the total cost of the MEMP which considers environmental cost, renewable energy cost and fuel cost. Furthermore, time-varying distributed neurodynamic optimization algorithms are proposed for solving the above MEMP based on consensus theory and sliding mode control technique. Compared with the optimization algorithms which consist of symbolic functions proposed in traditional energy management problems, algorithms consisting of hyperbolic tangent functions proposed in this paper can effectively reduce the oscillation of the algorithms and improve the stability of algorithms. Furthermore, the algorithm can converge the optimal trajectory of optimization problems with time-varying external disturbances and communication time delays. Meanwhile, the stability and convergence of the algorithms are proved theoretically by constructing appropriate Lyapunov functions. Finally, the performance evaluation results of numerical simulations show that the proposed algorithms can efficiently handle energy trading under time-varying load and maintain excellent stability with time-varying external disturbances and communication time delays.

Neuroadaptive control of saturated nonlinear systems with disturbance compensation.

Extended state observer acting as a popular tool can estimate the system states and total disturbances simultaneously. However, for extended-state-observer-based control of high-order nonlinear systems, there are still some difficult issues to solve, such as how to simultaneously reject matched and mismatched model uncertainties with strict theoretical proof, especially in the case of output feedback, "explosion of complexity" and so on. Motivated by these reasons, different control schemes in full-state feedback and output feedback conditions respectively will be integrated via the filter-based backstepping approach for saturated nonlinear systems. For the full-state feedback condition, adaptive neural network and extended state observer will be combined for each dynamic to handle the unknown nonlinear dynamics and external disturbances, respectively. For the output feedback condition, nonlinear disturbance observer design will be incorporated into the neural-network-based extended state observer scheme to handle mismatched disturbances at the same time. In particular, an auxiliary system will be constructed to compensate for the saturation influence. Moreover, the anticipate control effects of the developed controllers have been demonstrated by contrastive results for a hydraulic servo system.

Online Gain Adaptation of Whole-Body Control for Legged Robots with Unknown Disturbances.

This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie.

Mean-square H∞ antagonistic formations of second-order multi-agent systems with multiplicative noises and external disturbances.

In physical systems, since the acceleration is always regard as the control input, it is meaningful to study the coordination problems of the second-order multi-agent system. This paper devotes to the mean-square H∞ antagonistic formation control of second-order multi-agent systems with multiplicative noises and external disturbances under directed signed topologies. To force all agents achieve antagonistic formation and attenuate the effect of communication noises and external disturbances, a novel distributed consensus control protocol with a time-invariant control gain is proposed where only the information that received from neighbors is utilized. And then, by combining the theories of graph, robust H∞ control and stochastic analysis, some matrix inequalities conditions are deduced. It is proved that under the designed control protocol, the state of each agent converge to its own desired formation in its allied groups in the sense of mean square. Furthermore, numerical simulations are given for the purpose of showing that the proposed theoretical results are effective.

Distributed estimation and control for nonlinear multi-agent systems in the presence of input delay or external disturbances.

This paper establishes some distributed algorithms for nonlinear multi-agent systems to solve tracking control (TC) problem subject to external disturbances or delay. First, a distributed controller is introduced based on a distributed observer for the nodes to estimate and follow a nonlinear target. Then, utilizing a future predictor (FP) and an external disturbance observer, the proposed controller is developed for each agent with delay or disturbances to deal with TC problem. Stability of the control laws and FP is also analyzed and sufficient conditions are proposed for the TC of the multi-agent systems (MASs). Simulation examples validate the efficiency of the presented methods.

Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External Disturbances, and Time-Varying Delay.

In this paper, we design a robust model predictive control (MPC) controller for vehicle subjected to bounded model uncertainties, norm-bounded external disturbances and bounded time-varying delay. A Lyapunov-Razumikhin function (LRF) is adopted to ensure that the vehicle system state enters in a robust positively invariant (RPI) set under the control law. A quadratic cost function is selected as the stage cost function, which yields the upper bound of the infinite horizon cost function. A Lyapunov-Krasovskii function (LKF) candidate related to time-varying delay is designed to obtain the upper bound of the infinite horizon cost function and minimize it at each step by using matrix inequalities technology. Then the robust MPC state feedback control law is obtained at each step. Simulation results show that the proposed vehicle dynamic controller can steer vehicle states into a very small region near the reference tracking signal even in the presence of external disturbances, model uncertainties and time-varying delay. The source code can be downloaded on https://github.com/wenjunliu999.

Adaptive sliding mode current control with sliding mode disturbance observer for PMSM drives.

This paper focuses on the current control of a permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and external disturbances. To improve the performance of the PMSM current loop in terms of the speed of response, tracking accuracy, and robustness, a hybrid control strategy is proposed by combining the adaptive sliding mode control and sliding mode disturbance observer (SMDO). An adaptive law is introduced in the sliding mode current controller to improve the dynamic response speed of the current loop and robustness of the PMSM drive system to the existing parameter variations. The SMDO is used as a compensator to restrain the external disturbances and reduce the sliding mode control gains. Experiments results demonstrate that the proposed control strategy can guarantee strong anti-disturbance capability of the PMSM drive system with improved current and speed-tracking performance.

Tracking control of nonholonomic mobile agents with external disturbances and input delay.

This paper investigates the tracking control problem of chained-form nonholonomic multiagent systems (MASs). In contrast to the existing works in which some algorithms have been designed for ideal conditions, the destructive factors including external disturbances and input delay are considered in the dynamics of the agents in this work. Two distributed controllers are proposed such that the states of the controlled agents can track the states of the target in the presence of external disturbances and input delay. For this purpose, a distributed controller is firstly suggested based on a switching method to solve the tracking control problem for nonholonomic MASs with external disturbances. Then, the proposed control law is extended based on a state predictor for the tracking control of agents in the presence of input delay. The stability analysis of the two distributed controllers is also provided. Simulation results show the promising performance of the proposed algorithms.

A generalized smith predictor for unstable time-delay SISO systems.

In this work, a generalization of the Smith Predictor (SP) is proposed to control linear time-invariant (LTI) time-delay single-input single-output (SISO) systems. Similarly to the SP, the combination of any stabilizing output-feedback controller for the delay-free system with the proposed predictor leads to a stabilizing controller for the delayed system. Furthermore, the tracking performance and the steady-state disturbance rejection capabilities of the equivalent delay-free loop are preserved. In order to place this contribution in context, some modifications of the SP are revisited and recast under the same structure. The features of the proposed scheme are illustrated through simulations, showing a comparison with respect to the corresponding delay-free loop, which is here considered to be the ideal scenario. In order to emphasize the feasibility of this approach, a successful experimental implementation in a laboratory platform is also reported.

Wire rope tension control of hoisting systems using a robust nonlinear adaptive backstepping control scheme.

This paper concerns wire rope tension control of a double-rope winding hoisting system (DRWHS), which consists of a hoisting system employed to realize a transportation function and an electro-hydraulic servo system utilized to adjust wire rope tensions. A dynamic model of the DRWHS is developed in which parameter uncertainties and external disturbances are considered. A comparison between simulation results using the dynamic model and experimental results using a double-rope winding hoisting experimental system is given in order to demonstrate accuracy of the dynamic model. In order to improve the wire rope tension coordination control performance of the DRWHS, a robust nonlinear adaptive backstepping controller (RNABC) combined with a nonlinear disturbance observer (NDO) is proposed. Main features of the proposed combined controller are: (1) using the RNABC to adjust wire rope tensions with consideration of parameter uncertainties, whose parameters are designed online by adaptive laws derived from Lyapunov stability theory to guarantee the control performance and stability of the closed-loop system; and (2) introducing the NDO to deal with uncertain external disturbances. In order to demonstrate feasibility and effectiveness of the proposed controller, experimental studies have been conducted on the DRWHS controlled by an xPC rapid prototyping system. Experimental results verify that the proposed controller exhibits excellent performance on wire rope tension coordination control compared with a conventional proportional-integral (PI) controller and adaptive backstepping controller.

Memoryless disturbance-observer-based adaptive tracking of uncertain pure-feedback nonlinear time-delay systems with unmatched disturbances.

This paper presents a delay-independent nonlinear disturbance observer (NDO) design methodology for adaptive tracking of uncertain pure-feedback nonlinear systems in the presence of unknown time delays and unmatched external disturbances. Compared with all existing NDO-based control results for uncertain lower-triangular nonlinear systems where unknown time delays have been not considered, the main contribution of this paper is to develop a delay-independent design strategy to construct an NDO-based adaptive tracking scheme in the presence of unknown time-delayed nonlinearities and non-affine nonlinearities unmatched in the control input. The proposed delay-independent scheme is constructed by employing the appropriate Lyapunov-Krasovskii functionals and the same function approximators for the NDO and the controller. It is shown that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to an adjustable neighborhood of the origin.