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Underactuated dual boom crane (DBC) systems exhibit complicated nonlinearity and strong coupling due to the lack of independent actuators. Moreover, plant parameters may change in different transportation tasks and are difficult to be measured accurately, which leads to inaccurate gravity (torque) compensation and further brings positioning errors. Hence, most existing controllers based on exact model knowledge cannot ensure satisfactory control performance any longer. In order to handle the above issues, this article designs an adaptive sliding mode tracking controller for DBC systems based on the original complicated nonlinear dynamics without any linearization/simplification operations, which is the first one to effectively achieve both anti-swing and trajectory tracking control in the presence of parametric uncertainties. Not only can the state variables converge to the proposed sliding surface within finite time but also payload swing angles can be completely eliminated; consequently, the working efficiency and operation safety are further guaranteed. The corresponding stability and convergence for the equilibrium point of the closed-loop system are proven by rigorous mathematical analysis based on the Lyapunov techniques and Barbalat’s lemma. Hardware experimental results demonstrate the effectiveness and robustness of the presented controller. [ABSTRACT FROM AUTHOR]

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme. [ABSTRACT FROM AUTHOR]

This article investigates the controller design problem for an electromagnetic torsional micromirror with guaranteed transient performance constraint. Specifically, the developed solution works under the output regulation framework and utilizes the internal model principle for model parameter uncertainties and general reference trajectories tracking, incorporated with barrier Lyapunov function (BLF) method to prevent the tracking constraint violation. We first formulate the micromirror model as an output feedback system with relative degree two and unknown control coefficient, and further turn it into a lower triangular system with an extension transformation. Then, using the extended internal model design, we transform the output regulation problem of the transformed system into the stabilization problem of an augmented system. Finally, based on the BLF technique, we develop a stabilization controller for the augmented system to realize the asymptotic tracking of reference trajectory with transient performance constraint, where the effects of the main design parameters on the control performances are also investigated. By such a technical treatment, the entire control architecture is independent of the angular velocity information of the micromirror, which reduces the measurement complexity in practical. This feature behind the control scheme is important since the velocity information is difficult to be available in many microelectromechanical systems (MEMS). Moreover, the enhanced transient performance is beneficial for improving the scanning quality of the packaged micromirror system. The developed control solution is verified on an experimental platform using a field programmable gate array (FPGA)-based hardware, where the scanning and imaging applications are both conducted. [ABSTRACT FROM AUTHOR]

A model-free optimal tracking controller is designed for discrete-time nonlinear systems through policy gradient adaptive critic designs (PGACDs) with experience replay (ER). By using system transformation, optimal tracking control problems are converted into optimal regulation problems. An off-policy PGACD algorithm is developed to minimize the iterative $Q$ -function and improve the tracking control performance. The proposed method is realized based on the critic network and the actor network (AN), which are applied to approximate the iterative $Q$ -function and the iterative control policy, respectively. Then, the policy gradient technique is introduced to derive a novel weight updating law of the AN explicitly by using measured system data only. The convergence of the iteration is established through theoretical analysis, and the uniform ultimate boundedness is demonstrated for the closed-loop system under the PGACD-based controller by using Lyapunov’s direct method. To guarantee the stability and increase the data usage efficiency of the learning process, an ER-based learning framework is designed to improve the realizability of the proposed method. Finally, simulation results of two examples are provided to demonstrate the performance of the off-policy PGACD algorithm. [ABSTRACT FROM AUTHOR]

This article is mainly concerned with the fixed-time tracking control problem for multiple uncertain mechanical systems with actuation dead zones. As the dead zones and control gains are time varying and completely unknown, the control impact on mechanical system becomes completely unknown, making the achievement of fixed-time tracking control nontrivial. The existing studies on fixed-time tracking control cannot effectively estimate and compensate such dead zones, resulting in the upper bound of convergence time being uncertain and even system becoming unstable. To compensate the dead zones in a fixed time, we introduce a bound estimation method to estimate the impact of dead zones and design two fixed-time adaptive laws. Two cases on system uncertainties are considered. By introducing distributed control method, adaptive control method, neural networks and some skillful treatments, two novel distributed adaptive neural fixed-time tracking control schemes are proposed. It is confirmed that, with the proposed control schemes, all followers which are uncertain mechanical systems with completely unknown dead zones track the leader with sufficient precision in a predefined time. [ABSTRACT FROM AUTHOR]

This article focuses on developing an adaptive fast terminal sliding-mode controller (AFTSMC) with power rate proportional reaching law for the position and altitude tracking of a quadrotor in the presence of parametric uncertainties and bounded external disturbance. A nonlinear fast terminal sliding surface is proposed for the fast and finite-time convergence of the tracking error despite having the system states far away from the equilibrium point. Also, a power rate proportional reaching law has been proposed that ensures fast and finite-time convergence of the sliding manifold while attenuating the chattering phenomena in the sliding phase. To avoid the problem associated with over-estimation of the unknown disturbance bound, which eventually leads to chattering, an adaptive tuning law for gain adaptation is developed based on the Lyapunov’s stability theory that completely eradicates the necessity of knowing the upper bound of the disturbance a priori. The finite-time stability of a closed-loop system is investigated using the Lyapunov theory. The effectiveness of the proposed scheme is compared with an adaptive sliding-mode controller (ASMC) using extensive simulation and validated on the DJI Matrice 100 quadrotor as a proof of concept on the hardware platform. [ABSTRACT FROM AUTHOR]

As a typical underactuated mechanical system, offshore ship-mounted cranes are widely used to carry out the tasks of transferring cargos from one ship to another in the marine environment. Different from land-fixed cranes as well as traditional harbor cranes, offshore ship-to-ship crane systems work in two noninertial (ship) frames, while the target locations of the cargos are also inevitably influenced by the movements of the target ship. Besides, various external disturbances, which are caused by persistent sea waves, sea winds, or currents, etc., bring much more challenges to the control task of offshore ship-to-ship crane systems. To properly address these practical problems, in this article, we propose an increased adaptive neural network (NN)-based anti-swing tracking control strategy for such coordinated offshore crane systems, with a ship-motion prediction algorithm suggested to generate the target trajectories for cargos, and an adaptive NN proposed to deal with complicated unknown wave-induced disturbances. Bounded tracking performance is also guaranteed through a complete Lyapunov-based stability analysis. To the best of our knowledge, without any simplification of the original nonlinear dynamics, this article provides a high-performance adaptive control approach to deal with the anti-interference tracking control problem for offshore ship-to-ship crane systems, which are subjected to trajectories uncertainties as well as unknown wave motions disturbances. Furthermore, comparative hardware experimental results are presented to demonstrate the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper addresses trajectory planning and control for a dual-arm free-floating space robot (FFSR) based on predefinedtime stability. Firstly, a class of general nonsingular terminal sliding mode control strategy is proposed to achieve global predefined-time stability by switching a control parameter in a form of function. Then, a high-precision trajectory planning method is proposed for a FFSR using the proposed predefined-time stability theorem, which can make the end-effector and the base reach the desired pose within a prespecified time, and reduce the adverse effect on planning accuracy resulted from the singularity avoidance algorithm and the obstacle avoidance one. Subsequently, a trajectory tracking control strategy is proposed for a FFSR employing the proposed general nonsingular predefined-time terminal sliding mode, and the tracking errors can converge within a predefined time to an arbitrarily small neighborhood of zero in the presence of persistent disturbances. The proposed trajectory planning method has the advantage of pose feedback, so can be combined with the trajectory tracking controller in the control process to further enhance the control performance for the FFSR. Simulation results validate the effectiveness of the proposed methodologies. [ABSTRACT FROM AUTHOR]

This article deals with the event-triggered leader-following guaranteed cost consensus control problem for second-order nonlinear multiagent systems, in which the guaranteed cost function is proposed to facilitate to enhance the consensus tracking regulation performance. To reduce the frequency of information transmission, a distributed event-triggered mechanism, which broadcasts the triggered states to its neighbours for each agent, is designed, and the triggering condition is then constructed for leader-following second-order nonlinear multiagent systems. By employing Lyapunov–Krasovskii method and Barbalat’s lemma, some sufficient conditions are derived to ensure the leader-following consensus and guaranteed cost performance for second-order nonlinear multiagent systems. It is also exhibited that the constructed triggering condition can efficaciously exclude the Zeno behavior. To testify the efficacy of the proposed theoretical methodology, a simulation example is offered. [ABSTRACT FROM AUTHOR]

This article is focused on the design of an output tracking control scheme for a class of continuous-time periodic piecewise time-varying systems (PPTVSs) with actuator saturation and nonlinear perturbations. The energy-to-peak tracking performance is studied based on an equivalent condition on the definiteness property of matrix polynomials. Considering the actuator saturation and nonlinear perturbation, matrix polynomial-based sufficient conditions are derived through the Lyapunov method using periodic matrix functions. From a perspective of subinterval segmentation aimed at PPTVSs, the proposed conditions can achieve less conservatism for tracking the output of a periodic time-varying reference system, while the controller gains can be computed using convex optimization. Moreover, a heuristic algorithm is constructed to simultaneously guarantee the closed-loop state convergence and the output tracking performance. The reduction in conservatism and the effectiveness of algorithm are demonstrated by illustrative case studies. [ABSTRACT FROM AUTHOR]

This article studies the robust adaptive asymptotic tracking control problem for uncertain nonlinear systems with event-triggered inputs. Contrary to the existing results, a novel nonaffine nonlinear control input separated design scheme is developed based on the Taylor decoupling technique instead of the previous approximation ways. Correspondingly, the augmented dimension parameter updated laws and the unknown bound estimation of the compounded disturbance with a positive continuous integrable function are skillfully introduced. Also, by introducing a suitable decreasing function of tracking error variables, an improved adaptive event-triggered mechanism is constructed to achieve the asymptotic tracking and the communication load can be effectively alleviated. Moreover, it is shown that all the signals in the closed-loop system are uniformly bounded and the tracking errors converge to zero in a preset compact set. Finally, the validity of the proposed method is illustrated by simulation on an inverted pendulum model. [ABSTRACT FROM AUTHOR]

In this article, a virtual-goal-guided rapidly exploring random tree (RRT)-based visual servoing approach is proposed for nonholonomic mobile robots to simultaneously satisfy the field-of-view (FOV) constraint and the velocity constraints during the motion toward the desired pose. The presented approach contains two parts: 1) trajectory planning in the scaled Euclidean space and 2) trajectory tracking control. For the trajectory planning part, a new virtual-goal-guided RRT algorithm is designed to guarantee the FOV constraint and the velocity constraints by iteratively exploring the scaled Euclidean space in the presence of unknown image depth. Specifically, a virtual goal directly behind the desired pose is set to guide the tree to extend laterally into the area wherein the robot is easier to satisfy the FOV constraint. In addition, the lateral extension of the tree also helps decrease the lateral error of the robot as much as possible. Following each successful extension toward the virtual goal node, a greedy extension from the newly explored node to the desired pose is attempted using a polar stabilization controller, so that the planned trajectory can accurately arrive at the desired pose. Each newly explored edge in the scaled space is projected into the image space to check for the FOV limit. For the visual tracking part, the final searched trajectory in the scaled space is first transformed into image feature trajectories, which are then tracked by an image-based visual tracking controller. Experiments validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article studies the formation tracking control problem for a group of underactuated surface vehicles with guaranteed transient properties, including connectivity maintenance, collision avoidance, and tracking performance specifications. The formation is within the leader–follower control framework, in which every follower is controlled to track its leader and maintain a desired relative distance and bearing angle with respect to its leader such that the prescribed formation geometry is achieved based on local sensing capability. The onboard sensor systems are of limited range and angle of view, thus defining a cone of detectable region for every follower. Each follower can detect its leader, if and only if the relative distance and bearing angle keep always inside the predefined detectable region such that the connectivity between the follower and its leader is maintained over time. In addition to the consideration of connectivity maintenance, no collision between the follower and its leader is also considered. A transverse function control approach is introduced to overcome the difficulties caused by the off-diagonal system matrix and underactuation. The barrier Lyapunov function and adaptive backstepping procedure are incorporated into the formation control design to achieve the boundedness of the closed-loop systems with guaranteed transient performance. Collision avoidance and connectivity maintenance between every follower and its leader are also proven mathematically. Simulation studies are performed to show the effectiveness of the proposed control design technique. [ABSTRACT FROM AUTHOR]

This article focuses on investigating the synchronization and position/force tracking performance of the multilateral teleoperation system with asymmetric time delays. First, the synchronization and tracking error signals are proposed to transform the nonlinear dynamic system into a closed-loop system governed by the Markovian jumping parameter. Because of the presence of time delays, the information regarding the system states might become unavailable. Owing to this, a feedback control based on the hidden Markov model is developed through which the information on the current state of the actual system can be accessed through a series of observations. Moreover, the forward and backward delays of the master and slave manipulators are assumed to be asymmetric and varying with time. For the stability analysis, the Lyapunov–Krasovskii technique has been adopted for which its derivatives are dealt by employing Jensens’ inequality and extended reciprocal convex matrix inequality. Finally, simulation results were provided to validate the proposed methodology guaranteeing the closed-loop system to be asymptotically stable. [ABSTRACT FROM AUTHOR]

The communication time delay and uncertain models of robotic manipulators are the major problem in the teleoperation system, which can reduce the performance and stability of the system. This article proposed a novel finite-time adaptive control scheme for position and force tracking performances of the teleoperation system. First, a combined auxiliary error system with position and force tracking errors is designed. Second, a velocity feedback filter is introduced, and a new auxiliary variable function with finite-time structure is designed for controller design. The radial basis function neural network (RBFNN) is applied to estimate the uncertain parts. Then the finite-time adaptive control scheme and adaptive laws are given. Third, based on the Lyapunov method, stability and finite-time performance are demonstrated. And finally, the simulation and experimental studies (with Phantom Ommi devices) are performed and demonstrate the effectiveness of the proposed control scheme on teleoperation position/force tracking. [ABSTRACT FROM AUTHOR]

To implement the human–robot interactions in a noisy environment, the speech improvement-based (SIB) stratified adaptive finite-time saturation control (SAFTSC) for omnidirectional service robot (OSR) is developed. From the outset, the feature vectors of nine designed speech commands are extracted from their frequency signals and then trained by multiclass support vector machine. Two background noises are on-line filtered with the characteristic: “the smaller error in power spectrum is, the larger recovery from noisy power spectrum is.” Comparisons among without or with noise, and filtering are addressed. To achieve the zero pose error of OSR in finite time, an adaptive finite-time indirect trajectory (AFTIT) is constructed. To track the AFTIT with the zero error in finite time, the adaptive finite-time saturation control (AFTSC) is also established. Both AFTIT and AFTSC possess nonlinear switching gain increasing the high-frequency motion capability to fulfill the classified speech command. Simply put, the proposed SIB stratified AFTSC includes the speech improvement for classification, the AFTIT, and the AFTSC. Besides the stability of the closed-loop system is verified by the Lyapunov stability theory, three categories of SIB experiments are compared. [ABSTRACT FROM AUTHOR]

This article addresses the problem of adaptive finite-time neural tracking control for nonstrict-feedback nonlinear systems via dynamic surface (DS) technique. First, a new quasi-fast finite-time practical stability (QFPS) criterion is proposed for a class of general nonlinear systems. Then, the presented QFPS criterion is applied to design the desired adaptive finite-time neural tracking controller for a class of nonstrict-feedback nonlinear systems. The presented design scheme for the nonstrict-feedback nonlinear system has the following two features: 1) the “explosion of complexity” issue of the backstepping design is addressed by utilizing the DS technique and 2) the structural feature of Gaussian functions is applied to solve the design difficulties caused by the nonstrict-feedback form. It is proved that the designed controller for the nonstrict-feedback nonlinear system can make the resulting closed-loop system stabilizable in a quasi-fast finite time and the tracking error converges to a sufficiently small neighborhood of the origin. Finally, the simulation results are given to show the validity and practicability of the proposed design scheme. [ABSTRACT FROM AUTHOR]

This article presents and practically validates an identifier-critic-based approximate dynamic programming (ADP) method to online address the optimal tracking control problem for nonlinear continuous-time unknown systems. The imposed assumption on precisely known system dynamics is obviated via a neural network (NN) identifier. A static control is first adopted to retain the steady-state tracking response, while an optimal control derived via the ADP method is proposed to regulate the tracking error by minimizing a cost function. A critic NN is then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation. The learning of the identifier NN and critic NN is performed online simultaneously by tailoring a novel adaptation method, which can guarantee the convergence of the estimated NN weights. Consequently, the critic NN can be used to construct the optimal control policy directly, such that the actor NN used in the previous ADP schemes is avoided. Simulations are performed to verify the suggested control, and experiments on a helicopter plant are carried out to show its feasibility and improved control response. [ABSTRACT FROM AUTHOR]

In this article, we consider controlling a class of single-input–single-output (SISO) commensurate fractional-order nonlinear systems with parametric uncertainty and external disturbance. Based on the backstepping approach, an adaptive controller is proposed with adaptive laws that are used to estimate the unknown system parameters and the bound of unknown disturbance. Instead of using discontinuous functions, such as the sign function, an auxiliary function is employed to obtain a smooth control input that is still able to achieve perfect tracking in the presence of bounded disturbances. Indeed, the global boundedness of all closed-loop signals and asymptotic perfect tracking of fractional-order system output to a given reference trajectory are proved by using fractional directed Lyapunov method. To verify the effectiveness of the proposed control method, simulation examples are presented. [ABSTRACT FROM AUTHOR]

In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach. [ABSTRACT FROM AUTHOR]

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles. [ABSTRACT FROM AUTHOR]

Implementing underwater target tracking remains difficult for a free-swimming robotic fish owing to the intrinsically reciprocating motion in fishlike propulsion. In this article, we present a novel robotic fish platform with a camera stabilizing system and achieve real-time two-dimensional target tracking assisted by reinforcement learning (RL) in continuous environments. More specifically, we first develop an active visual tracking system based on cascade control structure to obtain the relative orientation between the robotic fish and the underwater target. Then, we propose a target tracking controller dealing with continuous state and action spaces based on deep RL (DRL). The controller takes the position of the target object as input and yields the motion parameters of the bioinspired central pattern generator governed robotic fish. The robustness and adaptability of the proposed controller as well as the influence of time-delays on the control system are explored via simulated experiments under different scenarios. Finally, both static and dynamic tracking experiments on the actual robotic fish demonstrate the effectiveness of the proposed mechatronic design and control methods, providing insights to executing aquatic vision-based tracking tasks. [ABSTRACT FROM AUTHOR]

Only partial system knowledge for the generalized robotic dynamics with input saturation, i.e., the regression matrix for the specific robot, is required to design the proposed adaptive finite-time saturated tracking control (AFTSTC). It contains a nonlinear auxiliary tracking error, which can shape the system frequency response. As the operating point is in the neighborhood of the zero auxiliary tracking error, nonlinear filtering gains can be increased to accelerate its tracking ability. Moreover, the traditional skew-symmetric matrix’s condition for the time derivative of inertia matrix and the Coriolis and centrifugal force matrix is not necessarily required such that the uncertainties and the finite-time convergence are reduced. The computational complexity as compared with the fuzzy neural network adaptive control is also addressed. The stability of the closed-loop system is verified by the Lyapunov stability theory. Finally, two practical robotic examples are given to validate the effectiveness and robustness of the proposed control. [ABSTRACT FROM AUTHOR]

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim–Simulink simulation. [ABSTRACT FROM AUTHOR]

A stabilization control can be used to maintain the position and attitude of the ship, mooring manipulation, and tracking control can force ships to enter the preplanned route from any initial position and finally reach the destination along the route. In this article, both the prespecifiable fixed-time stabilization control and adaptive trajectory tracking control are considered for a kind of fully actuated ocean surface ship under output constraints. Barrier Lyapunov function (BLF) is utilized to remedy with the output limitation constraints. By combining the fixed-time control and switching techniques, and choosing the appropriate symmetric Lyapunov function, the control algorithm of fixed-time stabilization is designed guaranteeing that the closed-loop system state trajectories tend to zero in a fixed time and meanwhile not going beyond the preset operating range in the control process. The adaptive tracking control law is proposed by using the asymmetric BLF and adaptive control technologies. Under the adaptive controller, the output constraints are also achieved, and the tracking errors are adaptively converge to steady zero states, respectively, while keeping all the signals bounded in the control process. The simulations are carried out to demonstrate the effectiveness of the presented control methods. [ABSTRACT FROM AUTHOR]

In this article, adaptive tracking control of finite impulse response (FIR) systems is studied with binary-valued measurements. An adaptive control strategy is proposed based on an online identification algorithm. First, the designed control inputs are proved to be bounded and satisfy a persistent excitation (PE) condition under the assumption of periodic and PE target signals, which ensures the convergence of the identification algorithm. Second, the convergence rate of the identification algorithm is proved to be O(1/t) and it depends on the true parameter instead of a priori information of the parameter, which is more intuitive. Due to the convergence and the convergence rate of the identification algorithm, we finally prove that the adaptive tracking control is asymptotically optimal and the tracking speed is faster than the previous control algorithm. The simulations are given to validate the developed results in this article. [ABSTRACT FROM AUTHOR]

This article addresses the distributed path maneuvering of underactuated unmanned surface vehicles (USVs) with collision avoidance and connectivity preservation. The USVs are guided by the multiple virtual leaders moving along the multiple parameterized paths and only a fraction of USVs have access to the virtual leaders. An observer-based finite-time control method is proposed to achieve a containment formation. Specifically, a finite-time extended state observer is employed to recover the unmeasured linear/angular velocities and estimate the total disturbances consisting of model uncertainties as well as ocean disturbances at first. Then, observer-based finite-time guidance laws using the information of neighbors are designed based on a containment scheme. An artificial potential field is incorporated into the distributed guidance law design to avoid collision and preserve connectivity. Finally, antidisturbance kinetic control laws are devised based on the finite-time convergent observers and nonlinear tracking differentiators. It is proven that all error signals in the closed-loop system are ultimately uniformly bounded, and the distributed path maneuvering formation pattern can be achieved in a finite time when USVs outside the collision avoidance and connectivity preservation region. Simulation results are given to verify the effectiveness of the proposed output feedback control method for the distributed path maneuvering of multiple USVs with position-yaw measurements only. [ABSTRACT FROM AUTHOR]

An adaptive neural network (NN)-based finite-time tracking control method is presented for the nonstrict nonaffined nonlinear multi-input–multi-output systems. The hardship of this article is that each subsystem responses to all input variables and any other subsystems of the whole system. Moreover, the uncertainty of the input transition matrix further soars the difficulty of controller design. In this article, NNs are used to approximate these functions with uncertainty automatically. Based on the Lyapunov stability theory, the controller we designed has proven to be semiglobal finite-time stable, implying that all the tracking errors converge to a small neighborhood of the original states in finite time, and the closed-loop system is semiglobal practical finite-time stable. At last, a simulation example is applied to verify the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the cooperative tracking control problem of multiagent systems where each agent is described as a Lur’e system. We first consider the case where the leader’s control input is zero and design a distributed adaptive event-triggered protocol for the followers to track the leader. We then deal with the general case where the leader contains a bounded control input by proposing a novel event-based protocol, including a nonlinear term to restrain the effect of the leader’s input. Both of the proposed event-triggered protocols can guarantee the uniform ultimate boundedness of the tracking error and the adaptive gains without the requirement of any global information of the network. The relationship between the upper bound of the tracking error and the controller parameters is explicitly derived. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed formation control problem of multiple nonholonomic wheeled mobile robots within real environments. The formation pattern of the system adopts leader–follower structure and the communication topology among the multirobot system is modeled by a directed graph. Slippage is hard to avoid due to the possible existence of ice, sand, or muddy roads. To overcome the effect of slippage, an adaptive trajectory tracking controller for leader robot is designed, such that the leader robot can keep up with the virtual reference trajectory and estimate the real value of unknown slipping ratio accurately. In addition, it is difficult for each follower robot to obtain leader’s states in a large formation system, so distributed formation controllers are designed based on distributed observers. It is shown that the proposed controllers can realize formation objective and overcome the slippage constraints at the same time. Finally, the effectiveness of the proposed controllers is verified by simulation results. [ABSTRACT FROM AUTHOR]

Service robot navigation must take the humans into account explicitly so as to produce motion behaviors that reflect its social awareness. Generally, the navigation problems of mobile service robot can be summarized to three aspects: 1) human detection; 2) robot real-time localization; and 3) robot motion planning. The purpose of this paper is to provide a feasible strategy to integrate these three aspects to achieve a conscious, safe, accurate, robust, and efficient navigation. We first introduce the human detection system for recognition of human gesture using a weighted dynamic time warping (DTW) with kinematic constraints. Thus, by interpreting the human body language through gesture recognition, robot motion behaviors like heading to the assigned position or following people can be activated. Then, for the robot localization, a simultaneous localization and mapping (SLAM) method based on artificial and natural landmark recognition is employed to provide absolute position feedback in real time. For the motion planning, a novel quadrupole potential field (QPF) method is proposed to plan collision-free trajectories, adequately considering the nonholomic constraint of the mobile robot system. Then, a robust kinematic controller is designed for trajectory tracking to account for slip disturbances. Such a design automatically merges path finding, trajectory generation, and trajectory tracking in a closed-loop fashion, achieving simultaneous motion planning for obstacle avoidance and feedback stabilization to a desired position and orientation even in the presence of slippage. Finally, experiments prove the effectiveness and feasibility of the proposed strategy, showing a good navigation performance on mobile service robot. [ABSTRACT FROM AUTHOR]

The event-triggered model predictive control (MPC) reduces energy consumption for updating control sequences while maintaining the originality of the MPC, which copes with hard constraints on dynamical systems. In the presence of large uncertainties, however, the standard event-triggered MPC may generate too frequent event occurrences. To compensate for unknown uncertainties, this paper applies a statistical learning to event-triggered MPC. The stability and the feasibility of the proposed control system are analyzed in regard to the statistical learning influences, such as the number of training samples, model complexity, and learning parameters. Accordingly, the event-triggering policy is established to guarantee the stability. We evaluate the proposed algorithm for the tracking problem of a nonholonomic model perturbed by uncertainties. In comparison with the standard event-triggered control scheme, the simulation results of the proposed method show better tracking performance with less frequent event triggers. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid nonlinear control system, comprising of a conventional proportional-differential (PD) controller and a PD-type fuzzy logic autopilot for the trajectory tracking of a quadcopter drone. Given the inherent nature of traditional control, which is model-based, and the essence of fuzzy logic control, which is knowledge-based, the proposed hybrid controllers can provide a more robust solution in the face of uncertainties. Both controllers operate in a parallel incremental form to improve the transient performance and the robustness of the closed-loop control system. Through extensive computer simulations supported by real-time flight tests, this paper highlights the efficacy of the proposed hybrid control system in the presence of some parameter variations, nonlinear aerodynamic models, and some external disturbances (e.g., wind gusts). The Dryden and 1–cos turbulence models are employed to represent the effects of wind gusts under realistic flight environments. The stability analysis of the closed-loop control system is conducted using Lyapunov’s indirect method. [ABSTRACT FROM AUTHOR]

Convergence rate is an important performance index in consensus control. Yet, most of the existing results can only achieve asymptotic consensus, or finite-time consensus whose settling time is contingent on initial conditions. This paper studies the robust fixed-time consensus tracking control problem for high-order nonholonomic chained-form systems subject to unknown parameters and nonlinear uncertainties. By using the modified power integrator method, we propose a new robust fixed-time consensus tracking algorithm, with which the consensus tracking can be achieved in a fixed time independent of initial conditions even in the presence of uncertainties. An observer-based fixed-time consensus tracking algorithm is also developed as an alternative method to overcome the communication loop problem. Lyapunov analysis shows the fixed-time stability of the closed-loop system. An application to multiple wheeled mobile robots is presented. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural bounded control scheme is proposed for an ${n}$ -link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a tracking flight control scheme is proposed based on a disturbance observer for a quadrotor with external disturbances. To facilitate the processing of external time-varying disturbances, it is assumed to consist of some harmonic disturbances. Then, a disturbance observer is proposed to estimate the unknown disturbance. By using the output of the disturbance observer, a flight controller of the quadrotor is developed to track the given signals which are generated by the reference model. Finally, the proposed control method is applied to flight control of the quadrotor Quanser Qball 2. The experimental results are presented to demonstrate the effectiveness of the developed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a robust output voltage-tracking algorithm is proposed for dc/dc boost power conversion systems based on a variable invariant manifold. A systematic multivariable approach, considering not only nonlinearity but also the parametric uncertainties, is used for deriving the control law. The proposed method has two features. First, a variable cut-off frequency algorithm is constructed to automatically adjust the invariant manifold, improving the transient output voltage-tracking performance. Second, nonlinear disturbance observers are introduced to enable the control law to exponentially recover the desirable tracking performance without any offset errors in the variable invariant manifold. The performance of the proposed technique is experimentally confirmed with a 3-kW dc/dc boost power conversion system. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed containment maneuvering problem for uncertain nonlinear multiagent systems in multiple-input multiple-output (MIMO) strict-feedback form. The follower agents are driven to achieve a collective motion guided by multiple parameterized paths, and a dynamic behavior can be independently prescribed for the group during maneuvering. A containment maneuvering controller is developed by utilizing a modular design method enabling decoupled estimation and control. Specifically, an estimator module is constructed by utilizing an echo state network to identify the unknown nonlinearities. Next, a controller module is constructed by employing a modified dynamic surface control method where a second-order nonlinear tracking differentiator is introduced to extract the derivative information of the virtual control law. Subsequently, a path update law is derived such that the virtual leaders are synchronized, and the desired speed profile for the group can be specified independently. By using a small-gain theorem and cascade stability theory, the entire closed-loop system is proved to be input-to-state stable, and the containment maneuvering errors are uniformly ultimately bounded. An application for the formation control of marine surface vehicles is provided to show the efficacy of the proposed controller for containment maneuvering of uncertain nonlinear MIMO strict-feedback systems. [ABSTRACT FROM AUTHOR]

This paper investigates the position tracking control problem for an induction motor with completely unknown nonlinearities. A novel control scheme is presented by using the gradient descent algorithm, adaptive backstepping technique, neural networks (NNs), and extended differentiators. Differing from some existing results which only designed the adaption of weights of NNs, our proposed control strategy provides training for all the parameters of NNs, including the basis functions’ widths and centers. With the help of the gradient descent algorithm and Lyapunov stability criterion, the convergence of both the NN approximation error and the system tracking error can be guaranteed. Finally, a simulation example shows the advantages of our proposed method compared with direct adaptive NN control strategy. [ABSTRACT FROM AUTHOR]

In this paper, both a trajectory tracking controller and a position controller are investigated for leader–follower trajectory control of quadrotors. A tracking differentiator is designed to generate speed signals for the quadrotors which are composed of a leader and some followers. Based on state errors between the leader and followers, a nonlinear disturbance observer is used in the trajectory tracking controller. A saturation law with anti-trigonometric functions is adopted in the position controller for the leader and followers. Experiment results are given to show the applicability of the proposed method for quadrotors. [ABSTRACT FROM AUTHOR]

A soft-bound interval control problem is proposed for general non-Gaussian systems with the aim to control the output variable within a bounded region at a specified probability level. To find a feasible solution to this challenging task, the initial soft-bound interval control problem has been transformed into an output probability density function (PDF) tracking control problem with constrained tracking errors, thereby the controller can be designed under the established framework of stochastic distribution control. Fault tolerant control (FTC) is investigated for soft-bound interval control systems in presence of faults. Three fault detection methods are proposed based on criteria extracted from the initial soft-bound control problem and the recast PDF tracking problem. An integrated design for fault estimation and FTC is proposed based on a double proportional integral structure. This integrated FTC design is developed through linear matrix inequality. Extensive simulation studies have been conducted to examine the key design factors, the implementation issues and the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article, the $H_{\infty }$ tracking control problem for a class of discrete time-delay systems is studied using data-based adaptive dynamic programming (ADP) algorithm. First, the controlled system and the reference system are combined to form an augmented discrete time-delay system. Second, we transform it into a discrete time-delay system represented by system measured data, which allows the system state to be completely replaced by the data generated during system operation. Third, a novel data-based Bellman equation is derived according to the Bellman optimality principle. Then, an ADP-based $H_{\infty }$ tracking control method is designed by means of the measured data. The simulation example demonstrates the effectiveness of the data-based ADP control method proposed in this article. [ABSTRACT FROM AUTHOR]

In this paper, an integral reinforcement learning-based adaptive neural network (NN) tracking control is developed for the continuous-time (CT) nonlinear system with unknown control directions. The long-term performance index in the CT domain is prescribed. Critic and action NNs are designed to approximate the unavailable long-term performance index and the unknown dynamics, respectively. The reinforcement signal is explicitly embedded in the updated law of the action NN and then the estimated long-term performance index can be minimized. Rigorous theoretical analysis is provided to show that the closed-loop system is stabilized and all closed-loop signals are semiglobally uniformly ultimately bounded. Finally, to demonstrate the control performance, simulation results are provided to verify the tacking control performance of an autonomous underwater vehicle model. [ABSTRACT FROM AUTHOR]

In this paper, a reinforcement learning-based adaptive control algorithm is proposed to solve the tracking problem of a discrete-time (DT) nonlinear state and input time delayed system of the wheeled mobile robot (WMR). With the typical model of the WMR transformed into an affine nonlinear DT system, a delay matrix function and appropriate Lyapunov–Krasovskii functionals are introduced to overcome the problems caused by the state and input time delays, respectively. Furthermore, with the approximation of the radial basis function neural networks (NNs), the adaptive controller, the critic NN, and action NN adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the WMR system, and the tracking errors convergence to a small compact set to zero. Two examples of simulation are given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel distributed ${{H}_{ \infty }}$ optimal tracking control scheme is designed for a class of physically interconnected large-scale nonlinear systems in the presence of strict-feedback form, external disturbance and saturating actuators. First, by designing feedforward control, the distributed ${{H}_{\infty }}$ optimal tracking control problem of a physically interconnected large-scale system is transformed into equivalent control of a decoupled multiagent system. Subsequently, a feedback control algorithm is designed to learn the optimal control input and the worst-case disturbance policy. The algorithm guarantees that the function approximation error and the distributed tracking error are uniformly ultimately bounded while the cost function converges to the bounded $ {L}_{{2}}$ -gain optimal value. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results of distributed control for the mobile multirobot system. [ABSTRACT FROM AUTHOR]

This paper investigates the output feedback adaptive finite-time quantized tracking problem for a class of uncertain nonstrict-feedback nonlinear systems with asymmetric actuator saturation, where signals including the measurable output and the control input are quantized by the sector-bounded quantizers. In order to overcome the technical difficulties arising from asymmetric actuator saturation and sector-bounded quantization errors, the combination of some novel techniques, the Gaussian error function-based smooth model, and fuzzy logic system approximation-based method are utilized. Moreover, an observer involving the quantized output signal and the saturated input signal is designed to reconstruct the unknown states. Then an adaptive output feedback tracking control strategy is developed for this class of systems, so that the system output follows satisfactorily a desired reference signal in finite time while all signals in the closed-loop systems are semi-global practical finite-time stable. Finally, two illustrative simulation examples are provided to validate the effectiveness and applicability of the proposed design. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust adaptive fault-tolerant tracking control for a class of linear systems with parameter uncertainty, external disturbance, and actuator faults including loss of effectiveness, outage, and stuck. According to the online estimation information provided by adaptive mechanism, a fault-tolerant compensation controller is constructed for robust tracking of closed-loop reference model systems. Compared with the existing results, by introducing the closed loop reference model which provides the additional degree-of-freedom, the transient performance of the systems can be improved. It is shown that the signals of the resulting adaptive closed-loop systems are bounded and the tracking error converges to zero asymptotically. A simulation example is provided to verify the effectiveness of the proposed fault-tolerant design method. [ABSTRACT FROM AUTHOR]

This paper provides an approach to design robust smooth controllers that allow a plant belonging to a broad class of nonlinear uncertain systems, with possible real actuators and subject to bounded or rate-bounded disturbances, to track a sufficiently smooth reference signal with an error norm smaller than a prescribed value. The proposed control laws are based on the concept of majorant systems and allow one to establish asymptotic bounds for the tracking error and its first and second derivatives. The proposed controller design is based on two parameters: the first is related to the minimum eigenvalue of an appropriate matrix, which the practical stability depends on, and the second is determined by the desired maximum norm of the tracking error and its convergence velocity. If the trajectories to be tracked are not sufficiently smooth, suitable filtering laws are proposed to facilitate implementation of the control laws and reduce the control magnitude, especially during the transient phase. The obtained theoretical results are validated in two case studies. The first one presents a tracking control design for an industrial robot, both in the joint space and workspace, with and without real actuators or velocity measurement noise. The second one deals with tracking control design for a complex uncertain nonlinear system. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude and position tracking control problem of the Lead–Wing close formation system with unknown multiplicative actuator faults. In close formation flight, the movement of the Wing unmanned aerial vehicle (UAV) is influenced by the vortex effects of the neighboring Lead UAV. This situation requires a modeling of the aerodynamic coupling vortex effects and linearization on the basis of optimal close formation geometry. Adaptive actuator fault parameters are identified by merging the improved unknown input observers, and adaptive laws with projection functions are presented for actuator fault tracking. The sufficient condition corresponding to the existence of unknown input observers is given. Then, an adaptive fault-tolerant H-infinity output feedback control scheme is developed to guarantee the asymptotic stability, H-infinity closed-loop system performance, and attitude and position tracking properties while the Lead UAV is being maneuvered. Simulation results of the Lead–Wing close formation flight validate the efficiency of the proposed fault-tolerant control algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a generalized nonlinear extended state observer is applied to an integral sliding mode (ISM) tracking control method for a pneumatic cylinder servo system with varying loads. The observer is established to estimate the total disturbance and its derivative for the system. An ISM controller is designed to ensure control performances of the system. Both finite-time convergence of the observer and finite-time stabilization of the closed-loop system are realized using Lyapunov’s stability theory. Lastly, experimental results demonstrate that control precision and response rapidity are improved using the proposed method. [ABSTRACT FROM AUTHOR]