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Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case. [ABSTRACT FROM AUTHOR]

In this article, we consider adaptive output feedback tracking for a class of nonlinear systems with uncertain parameters, multiplicative and additive sensor uncertainties, and external disturbances. A state observer and an adaptive controller are constructed based on gain scaling technique by which a dynamic gain is designed to compensate for the effects of the sensor uncertainties. The adaptive controller is linear-like and, therefore, is simple and easy to implement. It is shown that with this proposed scheme, all states of the closed-loop system are bounded and the real tracking error can converge to an arbitrarily small residual set. Simulation and experiment are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

If one cannot obtain the accurate prior information of the controlled object in advance, it will take much effort to get good dynamic performance. To this end, this article proposes an online estimation method to deal with disturbances and uncertainties for a class of systems with uncertain control gain. Based on the extended-state observer scheme, the estimation problem of disturbance and uncertain control gain is transformed to the convex optimization problem and solved by the sign-projected gradient flow, then the estimation can be applied to compensate disturbance and design controller. Moreover, the exponential and ultimate boundedness is obtained for the derived closed-loop system, together with rigorous theoretical analysis and proof. Finally, both the numerical simulations and the experiment on a brushless dc motor demonstrate that the proposed design has a better tracking performance and stronger robustness to deal with disturbances and uncertainties compared with the existing methods. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

An adaptive robot control approach that can achieve an accurate approximation of the complex robot dynamics without knowledge unlearning in the case of multiple sequential tasks is proposed in this article. A locally weighted learning (LWL) model with automatic structure growth according to the size of the learning domain is introduced to approximate unknown robot dynamics, and a composite learning technique with regressor extension is applied to improve parameter convergence. The LWL ensures that learning in one area of the learning domain does not cause unlearning in another area, and the composite learning theoretically guarantees the identification performance of the LWL model. By the exploitation of both stored data in memory and instantaneous data, parameter convergence in the LWL model is achieved under a more achievable interval-excitation condition than the stringent persistent-excitation condition. This further enhances the trajectory tracking performance for multiple sequential tasks, which is generally not achievable by existing approximation-based adaptive robot control approaches. Experimental studies have verified the superiority of the proposed approach over prevalent approaches. [ABSTRACT FROM AUTHOR]

This article addresses a novel adaptive compensation strategy for a flexible manipulator in the presence of unmodeled system dynamics and an infinite number of time-varying actuator faults. Differing from the existing results, the unmodeled system dynamics are represented by a nonlinear time-varying delay function, which is tackled with the aid of neural network technique and Lyapunov–Krasovskii function. By introducing the projection technique and a piecewise Lyapunov function, a fault-tolerant control method is proposed, the uniformly ultimate boundedness of the closed-loop system is ensured, and the number of actuator faults is allowed to be infinite. Simulation and physical experiment results show that the manipulator can track the desired angle and the vibration can be suppressed effectively under the proposed scheme for the system with uncertainties and actuator faults. [ABSTRACT FROM AUTHOR]

In this article, the geometric adaptive robust hierarchical control strategy is proposed for underactuated quadrotor, in which the negative effects of nonlinearities, uncertainties, and coupling are thoroughly considered and effectively attenuated. For handling nonlinearities, geometric control is adopted, i.e., the attitude error is directly defined on tangent space of rotation group, thus better attitude tracking performance can be achieved. To deal with uncertainties, a comprehensive dynamic model is established with compound damping terms representing the main aerodynamic effects and all inertia parameters including mass, inertia tensor, and center of gravity. Then, integrated direct/indirect adaptive robust control is developed with adaptive compensation of aerodynamic damping and complete inertia, which is beneficial to improve tracking performance. As for the coupling, hierarchical control architecture is utilized to relax the strong coupling between attitude and translational subsystems. Moreover, the stability of the overall closed-loop hierarchical system, together with transient performance and final tracking accuracy, has been rigorously analyzed without time-scale separation assumption. Finally, comparative experiments demonstrate the better performance of the proposed method in terms of tracking accuracy and robustness. [ABSTRACT FROM AUTHOR]

This article proposes a proportional-integral (PI) event-triggered control strategy for networked nonlinear systems subject to unmatched uncertainties. The influences of the unmatched uncertainties and external disturbances on the output channel are attenuated by an extended-state-observer-based output feedback controller through a state transformation. The outputs of the controller are transmitted in an event-triggered fashion by comparing the difference between the current sampling error and its integration with respect to time. We prove the stability of the closed-loop event-triggered control system by regulating the trajectories of the observation and tracking errors into a compact set. Further, the relationships between the control performance and designed parameters are investigated quantitatively. In addition, Zeno-freeness is guaranteed by proving the existence of a nonzero minimum inter-event time. Finally, the effectiveness of the proposed PI-type event-triggered control strategy in reducing signal transmission rate is validated through numerical examples and various experiments. [ABSTRACT FROM AUTHOR]

The independent metering system decouples the meter-in and meter-out flows of the chamber, giving extra control flexibility and can reducing energy consumption while keeping motion control accuracy. The control performance of the independent metering system is significantly determined by the valve characteristics.Valve nonlinearities, such as deadband, nonlinear flow gain, and leakage, may restrict the control accuracy. To address the issue and improve the system control precision, the nonlinearities of the valve need to be further modeled. In this article, a nonlinear valve flow model is proposed to accurately depict the relationships between the flow rate, pressure drop, and input voltage. The valve deadband is compensated by the static inverse function, while a certain polynomial projection function is used to mimic the nonlinear flow curve outside the deadband. To deal with uncertainties and nonlinearities in the independent metering system, an adaptive robust control approach is synthesized. The least-squares estimation law is adopted to online adapt the valve parameter and other system parameters. Comparative experiments were carried out to illustrate the effectiveness of the proposed approach. The results show that the valve flow nonlinearities are further captured by the proposed model, and the independent metering system performed a higher level control precision. [ABSTRACT FROM AUTHOR]

In this article, a pneumatic artificial muscle (PAM) based on a metal hydride (MH) is considered for a compact compliant actuator. It is suitable for broad applications of the human–robot interaction (HRI). To address the problem of the HRI represented by a varying environment, a compliant control is introduced. In fact, the bottlenecks of improving the performance in the compliant control of the PAM actuator are: an inherent nonlinear dynamics of a PAM, the parametric and nonlinear uncertainties influenced by a varying environment, and an additional high dimension introduced by an MH employed as a driving force for the PAM. We propose a learning-based adaptive robust control (LARC) framework to tackle these challenges. A Bayesian learning technique deals with the parameter adaptation for the adaptive control. The effectiveness of the LARC has been examined in extensive experiments of tracking control. [ABSTRACT FROM AUTHOR]

Cable-driven parallel mechanism has been widely studied due to its advantages of fast response and large workspace. The structural uncertainties in the system often introduce internal disturbance and unavoidably influence its motion precision. Generally, to reduce the influence, robust control is adopted and the boundary of the disturbance is regarded as a fixed value given by rough estimation. This method often causes large vibration of the motion component, which certainly compromises the dynamic performance of the system. Therefore, to solve this problem, a variable boundary analytical scheme based on the decoupling of control law and internal disturbance is proposed in this article. Specifically, sufficient condition for the solvability of disturbance boundary is proved by the boundedness of the structural matrix error. Then, the disturbance boundary is modeled analytically, so that its particular value can be determined with respect to the system status. Following that, a robust adaptive control algorithm based on the variable boundary of disturbance is developed. It is verified that the tracking error is globally uniformly bounded. The experimental results show that the proposed control method can effectively reduce the tracking errors and attenuate the tension chattering compared with the conventional robust adaptive control scheme with a roughly estimated boundary. [ABSTRACT FROM AUTHOR]

For three-phase ac–dc converters, load disturbances, and parameter uncertainties cause large fluctuations of the dc-link voltage, even resulting in system instability. In order to cope with this problem, the disturbance observer-based finite-time control (DOFTC) is put forward in this article, where the finite-time disturbance observer is designed to fast and accurately estimate disturbances. Subsequently, the stability of the ac–dc converter closed-loop system is analyzed and theoretically proved. Finally, experimental results verify the effectiveness of the DOFTC strategy, where good dynamic and steady-state performance is achieved under load disturbance and parameter uncertainties. Compared with the enhanced state observer-based sliding mode control and adaptive control method, the proposed DOFTC method not only reduced the fluctuations of the dc-link voltage by at least 55% but also improved the transient response. [ABSTRACT FROM AUTHOR]

The control problem of active suspension systems (ASSs) with uncertainties is considered in this article by formulating control goals as a series of equality constraint (i.e., soft constraint) and inequality constraint (i.e., hard constraint). Uncertainties are (possibly fast) time varying, bounded, and include large mismatched portions. However, bounds are unknown. The objective is to design an adaptive robust control driving the system to converge to soft constraints provided by the sky-hook model, and simultaneously, ensuring the system to meet hard constraints at every instant. The adaptive robust control design is implemented in four steps. First, we investigate a transformation technique for incorporating hard constraints into soft constraints and the model. Second, a nominal control is presented without considering uncertainty. Third, an orthogonal decomposition technique is proposed for dividing uncertainty into matched and mismatched portions, which creatively allocates the mismatched portion for later control design. Fourth, a continuous adaptive law is constructed to emulate a constant design parameter vector associated with uncertainty bounds. It is proved that the proposed control guarantees uniform boundedness and uniform ultimate boundedness of ASSs in the presence of uncertainties and hard constrains. Furthermore, experimental and simulation results on the 2-DOF ASS are presented to validate the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

To achieve the precision contouring performance, this article presents a contouring compensation approach for the biaxial motion system even under extreme contour tasks. Two permanent magnet linear synchronous motors are installed in the biaxial motion platform. First, the dynamics of the biaxial motion servo system with the uncertainties such as model parameter variations, additive disturbance, and nonlinear friction are introduced. Since Newton algorithm is provided to realize dynamic contouring error estimation, accurate estimation of the contouring error can be achieved, even for the high velocity and complex contour with sharp corners and large curves. Then, iterative learning control (ILC) is used to control the contouring error for contouring performance improvement. An adaptive PD-type learning law based on an asymmetric Gaussian-like function is applied to improve the performance of the system. Moreover, adaptive jerk control (AJC) integrated with adaptive feedback gain is designed to address the negative effect of the uncertainties and enhance the robustness. The adaptive feedback gain is dominated and tuned by a novel adaptation law without the need for prior bound knowledge of the uncertainties. Meanwhile, parameter estimation is employed to handle the model parameter variations. Finally, the experimental results are presented to validate the efficiency and the achievable contouring performance of the proposed Newton-ILC and AJC approach. [ABSTRACT FROM AUTHOR]

Normally, achieving asymptotic convergence in the presence of unknown arbitrarily bounded external disturbance requires noncontinuous control signals. In this article, an adaptive backstepping controller for a class of incommensurate fractional-order nonlinear systems with parametric uncertainties and external time-varying disturbance is proposed to realize asymptotic perfect output tracking. Appropriate adaptive laws are designed to deal with the uncertainties and unknown bounded disturbance. In the designed adaptive backstepping control scheme, an auxiliary function is adopted to obtain a smooth control signal. It is theoretically shown that asymptotic output tracking to a given reference signal is achieved while ensuring global system stability in the sense that all the closed-loop signals are bounded. Simulation and experimental studies demonstrate the effectiveness of the proposed backstepping control scheme. [ABSTRACT FROM AUTHOR]

This article studies an adaptive fault-tolerant approach with a fixed-time sliding mode for trajectory tracking of uncertain robot manipulators with actuator effectiveness faults. An equivalent system with an adaptive technique is developed to remove the effects of uncertainties on tracking performance, whereas an appropriate system transformation is accomplished to eliminate the effects of the actuator effectiveness faults. A significant feature of this study is that the fixed-time stability is guaranteed theoretically and practically in the presence of uncertainties and actuator effectiveness faults. Moreover, the appearing advantages are that the proposed approach yields an improved tracking performance without utilizing the overestimated control gain and prior knowledge of a robot and, then, overcomes the singularity and algebraic loop problems completely. The effectiveness of the proposed approach for tracking systems is accomplished on simulation and experimental results from a qualitative and quantitative analysis. [ABSTRACT FROM AUTHOR]

In dual-motor servo systems, several factors seriously affect the tracking performance especially in high-speed and high-accuracy situations, which include machinery flexibilities, torque disturbance, unmodeled dynamics, and motor parameter differences. Given these factors, this article investigates the command filtered backstepping synchronization control (CFBSC) method for a servo system driven by two motors synchronously. Command filters are used to deal with the virtual control signals in backstepping design process to avoid the computational burden causing by repeated derivatives, and a compensation system is applied to reduce the tracking error. Adaptive control is used to compensate the torque disturbance and unmodeled dynamics. In addition, the speed and torque synchronization control signals are designed to guarantee high synchronization performance. The stability of the dual-motor servo system is proved. And the effectiveness of the proposed controller is validated through experiments. [ABSTRACT FROM AUTHOR]

As one type of unmanned aerial vehicles, the quadrotor typically suffers from payload variations, system uncertainties, and environmental wind disturbances, which significantly deteriorate its attitude control performance. To provide high-speed, accurate, and robust attitude tracking performance for the quadrotor, an adaptive fast nonsingular terminal sliding mode (AFNTSM) controller is proposed in this article. The proposed AFNTSM controller combines the advantages of fast nonsingular terminal sliding mode (FNTSM), integral sliding mode, and adaptive estimation techniques, which are effective to achieve the desired tracking performance and suppress control signal chattering. Furthermore, unlike conventional methods, the adaptive estimation removes the requirements for the upper bound information of the disturbances. It is proved that the proposed AFNTSM can guarantee finite-time convergence and zero tracking error for the quadrotor attitude control. Finally, comparative study with the FNTSM control only and conventional sliding mode control is conducted through experiments and the results demonstrate that the proposed AFNTSM can achieve faster convergence and stronger robustness in line with theoretical analysis. [ABSTRACT FROM AUTHOR]

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications. [ABSTRACT FROM AUTHOR]

In this article, we propose a solution to the problem of path following for a quadcopter aircraft with unknown vehicle parameters (mass and moment of inertia) and external disturbances. By employing the backstepping technique, the proposed adaptive control strategy guarantees the following: the quadcopter is globally steered toward, and kept within, an arbitrarily small neighborhood of a desired smooth path, achieving global uniformly ultimately boundedness; compared to trajectory tracking, a smoother convergence is obtained as the control actuation signals (thrust force and torque) are bounded with respect to the position error, and the designed timing law ensures that the desired path starts to move only when the vehicle gets close to the desired path; and a single adaptive control law can be used for accurate motion control of aerial vehicles with a wide range of inertial properties, without the need for retuning control gains or other parameters. Moreover, the controller is also made robust to external constant and slowly time-varying disturbances through the design of disturbance estimators. To demonstrate the effectiveness and performance of the proposed control strategies, simulation and experimental results are presented and analyzed. [ABSTRACT FROM AUTHOR]

In this article, we proposed a scheme of modeling an air heater system with a long airflow duct, predominantly available in many industrial plants to maintain the desired temperature by simultaneously befitting the transport delay, nonlinearities, model uncertainties, and nonuniform disturbances. At first, the nonlinear delayed air heater system is formulated by combining linear fuzzy system models utilizing fuzzy parallel distributed compensation (PDC) logic. Then, a newly developed L1 adaptive control strategy is modified by replacing its conventional state feedback controller with fuzzy PDC control logic to maintain the desired temperature by nullifying nonlinearities, transport delays, uncertainties, and disturbances. The novelty of the proposed work lies in the fact that the proposed fuzzy PDC law augmented L1 adaptive scheme explicitly deals with the delays and nonlinearities by means of fuzzy PDC logic as well as eliminates time-varying uncertainties and disturbances utilizing L1 adaptive control strategy. The results obtained from real-life laboratory-scale experiments suitably demonstrate the usefulness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article, we introduce an adaptive strictly negative-imaginary (SNI) autopilot for a low-cost quadrotor aerial vehicle, specifically designed to achieve high precision hovering and perform accurate trajectory tracking under time-varying dynamic load (i.e., displacement, velocity, and acceleration). Leveraging the learning ability of an artificial neural network, our adaptive SNI controller is robustly designed to overcome uncertainties in flight environments such as variations in the centre-of-gravity, modeling errors, and unpredictable wind gusts. The efficacy of the proposed adaptive control system is investigated under extensive flight tests in addition to numerous computer simulations and rigorous comparison with other control techniques, namely, fixed-gain SNI, fuzzy-SNI, and conventional PID controllers. We also conduct a stability analysis of the proposed control system using the SNI theorem. [ABSTRACT FROM AUTHOR]

In this article, a state-constraints adaptive backstepping control (ABC) strategy is proposed and presented to accomplish precise rotor displacement control for a kind of nonlinear active magnetic bearing (AMB) system considering the presence of parameters nonstationarities and uncertainties. Since the control properties of the AMB system is strong nonlinear, uncertain, and nonstationary owing to external disturbance and the variation of geometry and material dependent on operation condition, a nonlinear AMB system model with switched parameters is established to describe the AMB dynamics. Subsequently, a nonlinear ABC algorithm is designed for the rotor position control of radial magnetic bearing. For the purpose of making the rotor displacement confined to a prescribed range in consideration of physical limitations and security concerns, barrier Lyapunov function (BLF) is founded to construct the adaptive backstepping controller. Thus, the proposed BLF-based adaptive backstepping controller is able to restrict the position states without exceeding the prescriptive constraint spaces and then to improve the system performance. Additionally, the extended development for n-order controller and the stability proof is given in this article as well. Simulation and experiment results demonstrate the effectiveness and the potential application of the proposed control approach. [ABSTRACT FROM AUTHOR]

Cable-driven parallel robots (CDPRs) are a new type of parallel robots with a mobile platform driven by several cables. The flexibility of the structure not only helps them perform better than traditional parallel robots, but also brings about the issues of model uncertainty and multicable coordination. To handle these problems, a novel adaptive synchronization control (ASC) scheme is proposed to suppress the uncertainties of kinematics and dynamics simultaneously, regulate the coordination motion of multiple cables, and finish high-precision tracking tasks. In the proposed scheme, apart from tracking error, a new synchronization error is introduced to describe the coordination motion relationship between adjacent cables. According to these defined errors, the kinematic and dynamic adaptation laws are designed through the linear expressions of kinematic and dynamic models, respectively. Finally, the ASC scheme is presented as well as a tension distribution algorithm. The tracking experiments are implemented on a redundantly actuated 3-degree-of-freedom CDPR with four cables. The proposed ASC scheme can effectively complete the correction of the model parameters by using the feedback of errors and enhance the tracking accuracy of each cable and the synchronization accuracy between all the cables significantly, thereby ultimately improving the control performance of the mobile platform. [ABSTRACT FROM AUTHOR]

Robustness in the face of uncertainties is an integral part of designing a real-time control system. Based on negative imaginary (NI) systems theory, we design robust and adaptive control systems for accurate trajectory tracking of a quadcopter aerial vehicle. Considering the challenging dynamics of unmanned aerial vehicles, we employ knowledge-based fuzzy inference systems (FIS) to facilitate automatic tuning in our SNI controllers, leading to the development of adaptive SNI control systems. Unlike fixed-gain controllers that have no ability to adapt to the variations in environmental conditions or changes in the dynamics of the plant, our adaptive SNI controllers are able to perform self-tuning to constantly update their parameters. The concept of adaptive autopilots will enhance the ability of the closed-loop control systems to accommodate large uncertainties. To demonstrate their efficacy, we design and implement our adaptive SNI controllers in the three-position control loops of the AR.Drone quadcopter after conducting extensive computer simulations. We also perform a rigorous comparative study with respect to the performance of fixed-gain SNI controllers, fixed-gain NI systems, in addition to model-predictive-control systems, and proportional integral derivative (PID) control systems as our benchmarks. To complete the study, we conduct a stability analysis based on Kharitonov's Theorem. [ABSTRACT FROM AUTHOR]

In the context of disturbance-estimation (DE)-based control, it is reported that including desired dynamics into the nominal model yields a simple and compact control law that is easier to implement. The resulting control structure unifies the nominal feedback controller and feed-forward disturbance compensator to formulate an overall feed-forward disturbance compensation problem. Based on this, two popular DE-based techniques, namely active disturbance rejection control (ADRC) and uncertainty and disturbance estimator (UDE)-based control, are restructured into a unified form. By reformulating the controllers in the frequency domain, it is shown that the two structures are equivalent provided the UDE filter is selected based on the observer designed for ADRC. For an easier understanding of practitioners who are familiar with the conventional control structures, the designed controllers are reinterpreted in the framework of two-degree-of-freedom (2DoF) internal model control and 2DoF proportional-integral-derivative (PID) controller to show the equivalence among them. Tuning rules for PID controllers in terms of unified ADRC parameters are also presented to aid practical implementation in industry. Performance of the unified control scheme is evaluated on a non-minimum phase dc–dc boost converter that is operating under line and load disturbances using simulation and experimental study. [ABSTRACT FROM AUTHOR]

For high control performance of cable-driven manipulators, we design a new adaptive time-delay control (ATDC) using enhanced nonsingular fast terminal sliding mode (NFTSM). The proposed ATDC uses time-delay estimation (TDE) to acquire the lumped dynamics in a simple way and founds a practical model-free structure. Then, a new enhanced NFTSM surface is developed to ensure fast convergence and high control accuracy. To acquire good comprehensive performance under lumped uncertainties, in this article we propose a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately. Benefitting from the TDE and the proposed enhanced NFTSM surface and adaptive control gain, our proposed ATDC is model-free, fast response, and accurate. Theoretical analysis concerning system stability, and control precision and convergence speed are given based on Lyapunov theory. Finally, the advantages of our ATDC over existing methods are verified with comparative experiments. [ABSTRACT FROM AUTHOR]

To simultaneously achieve accurate tracking error prediction, rigorous motion accuracy, and certain robustness to parameter variations and unknown disturbances, this article proposes a data-based learning adaptive robust control (LARC) strategy based on gated recurrent unit (GRU) neural network. Firstly, parameter adaptive control and robust control are utilized to guarantee the robustness against parametric uncertainties and unknown disturbances. A GRU neural network is then constructed and capable of precisely predicting the tracking error after training with data collected from a linear-motor-driven stage. Essentially, the GRU network can be viewed as a data-based model, which captures the tracking error dynamic characteristics and provides a prediction even before implementing the real trajectory. Consequently, a reference modification and a feedforward compensation part can be formed, which is the significant part of the whole LARC control structure. Comparative experimental investigation not only validates the effectiveness of the tracking error prediction ability, but also demonstrates the practically satisfactory transient/steady-state tracking performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Linearization is often used for control design of nonlinear systems but what degree of a linearization is sufficient for the controller design is always a question. Furthermore, most of the existing linearization methods aim to develop a completely linear model without retaining any nonlinearity and thus the unmodeled dynamics unavoidably exists due to omitted higher order terms. In this article, a local compact form dynamic linearization (local-CFDL) is developed at first to transform the original nonlinear nonaffine system into an affine structure consisting of both an unknown residual nonlinear time-varying term and a linearly parametric term affine to the control input. A discrete-time extended state observer (DESO) is introduced to estimate the unknown residual nonlinear time-varying term as a new extended state. Then, a local-CFDL-based DESO-model-free adaptive control (MFAC) is proposed where the estimation of DESO is incorporated to compensate for the disturbances and uncertainties. Furthermore, a local partial-form dynamic linearization (local-PFDL) is also presented using multi-lag inputs and partial derivatives. And, a corresponding local-PFDL-based DESO-MFAC is proposed utilizing additional control information to improve control performance. The two proposed methods are both data-driven and do not require any explicit model information. Theoretical analysis shows the robust convergence of the proposed methods in the presence of disturbances. Simulations verify the effectiveness of the proposed method and show that the local-PFDL-based DESO-MFAC outperforms the local-CFDL-based one owing to the use of additional control information. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive time-delay control method to handle the balance of a class of biped robots. Considering model uncertainties and unknown external disturbances, a high-dimensional integral Lyapunov function has been developed, then time-delay estimation technique (TDE) is utilized, and in order to compensate for the errors produced by TDE, an adaptive control law is designed. Based on the evolution of the error trajectories, the adaptive control law can automatically regulate the control gains without any threshold value. By the Lyapunov synthesis, the semiglobally uniformly ultimately boundness (UUB) of the closed-loop control system has been theoretically proved. We have conducted the experimental studies on biped robot to demonstrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an advanced nonlinear controller for hydraulic system to achieve asymptotic tracking with various disturbances. To accomplish this study, a multilayer neural-networks (NNs) estimator is first developed to improve the compensation accuracy of model-based feedforward control terms, which can greatly reduce the uncompensated disturbance, then a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the multilayer NNs estimator to deal with the residual mismatched disturbance, in which the RISE feedback gain is adapted online to further decrease the high-gain feedback. At last, by considering the inwardness of matched disturbance in hydraulic systems, it is estimated by another multilayer NNs fully with the residual functional reconstruction inaccuracies handled by a novel adaptive term. As a result, theoretical analysis reveals that the proposed controller guarantees a semiglobal asymptotic stability. Extensively comparative experimental results verify the priority of the proposed control strategy, and a 0.2% dynamic tracking accuracy is achieved. [ABSTRACT FROM AUTHOR]

Redundancy resolution is of great importance in the control of manipulators. Among the existing results for handling this issue, the quadratic program approaches, which are capable of optimizing performance indices subject to physical constraints, are widely used. However, the existing quadratic program approaches require exactly knowing all the physical parameters of manipulators, the condition of which may not hold in some practical applications. This fact motivates us to consider the application of adaptive control techniques for simultaneous parameter identification and neural control. However, the inherent nonlinearity and nonsmoothness of the neural model prohibits direct applications of adaptive control to this model and there has been no existing result on adaptive control of robotic arms using projection neural network (PNN) approaches with parameter convergence. Different from conventional treatments in joint angle space, we investigate the problem from the joint speed space and decouple the nonlinear part of the Jacobian matrix from the structural parameters that need to be learnt. Based on the new representation, we establish the first adaptive PNN with online learning for the redundancy resolution of manipulators with unknown physical parameters, which tackles the dilemmas in existing methods. The proposed method is capable of simultaneously optimizing performance indices subject to physical constraints and handling parameter uncertainty. Theoretical results are presented to guarantee the performance of the proposed neural network. Besides, simulations based on a PUMA 560 manipulator with unknown physical parameters together with the comparison with an existing PNN substantiate the efficacy and superiority of the proposed neural network, and verify the theoretical results. [ABSTRACT FROM PUBLISHER]

Most existing control methods for wheeled mobile robot (WMR) are based on the primary assumption that the WMR's center of mass (CM) is known and fixed. This paper presents an adaptive tracking control scheme for an asymmetrically actuated WMR with slipping/skidding dynamics and uncertain/unknown mass center. First, we establish the WMR dynamic model with consideration of the fact that its CM is normally unknown or even shifting due to dynamic loading and/or load shifting. The resultant model also takes into account the impact of slipping/skidding uncertainties. Second, a structurally simple and computationally inexpensive controller is developed to deal with time-varying unknown control gain and parametric/nonparametric uncertainties of WMR, where the asymmetric and nonsmooth input saturation with no a prior knowledge of bounds of input saturation, together with output constraints and actuation/propulsion failures, is addressed. [ABSTRACT FROM PUBLISHER]

In this paper, a saturated adaptive control of an electrohydraulic actuator is proposed to drive the joint motion of a two-degree-of-freedom robotic arm. Due to the largely unknown load disturbance from the driven force/torque of the robotic arm and the existing control saturation of a servo valve, a Nussbaum function is designed to compromise between the antiwindup control effect and the dynamic response performance of an electrohydraulic system. Then, an adaptive parametric estimation law is designed to estimate the hydraulic parametric uncertainties. The effectiveness of the proposed controller has been demonstrated by comparative experimental study, which indicates that this controller can quickly recover the unsaturated dynamics with satisfactory tracking accuracy under largely unknown load disturbance and parametric uncertainty. [ABSTRACT FROM PUBLISHER]

This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

This paper proposes a new adaptive-robust control (ARC) strategy for a tracking control problem of a class of uncertain Euler–Lagrange systems. The proposed adaptive-robust time-delay control (ARTDC) amalgamates the ARC strategy with the time-delay control (TDC). It comprises three parts: a time-delay estimation part, a desired dynamics injection part, and an adaptive-robust part. The main feature of the proposed ARTDC is that it does not involve any threshold value in its adaptive law; thus, it allows the switching gain to increase or decrease whenever the error trajectories move away or close to the switching surface, respectively. Thus, compared with the existing ARC schemes, ARTDC is able to alleviate the over- and underestimation problems of the switching gain. Moreover, the stability analysis of ARTDC provides an upper bound for the selection of sampling interval and its relation with controller gains. The proposed ARTDC shows improved tracking performance compared with the TDC and the existing adaptive sliding-mode control in simulations as well as in experiments with a multiple-degree-of-freedom system. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive interval type-2 fuzzy logic control scheme is proposed for high-performance permanent magnet synchronous machine drives. This strategy combines the power of type-2 fuzzy logic systems with the adaptive control theory to achieve accurate tracking and robustness to higher uncertainties. Unlike other controllers, the proposed strategy does not require electrical transducers and hence, no explicit currents loop regulation is needed, which yields a simplified control scheme. But, this limits the machine's operation range since it results in a higher energy consumption. Therefore, a modified reference frame is also proposed in this paper to decrease the machine's consumption. To better assess the performance of the new reference frame, comparison against its original counterpart is carried-out under the same conditions. Moreover, the stability of the closed-loop control scheme is guaranteed by a Lyapunov theorem. Simulation and experimental results for numerous situations highlight the effectiveness of the proposed controller in standstill, transient, and steady-state conditions. [ABSTRACT FROM PUBLISHER]

This paper presents a six-degree-of-freedom relative motion control method for autonomous spacecraft rendezvous and proximity operations subject to input saturation, full-state constraint, kinematic coupling, parametric uncertainty, and matched and mismatched disturbances. Relative rotational and relative translational controllers are developed separately based on a unified adaptive backstepping technique. Both element-wise and norm-wise adaptive estimation techniques are used for handling parametric uncertainties, kinematic couplings, and matched and mismatched disturbances, where the bounds of disturbances are unknown. Two auxiliary design systems are employed to deal with input saturation in the relative rotational and relative translational control designs, and the stability of the saturated control solution is verified. Full-state constraint of the relative pose motion is handled by using barrier Lyapunov functions while achieving a satisfactory control performance. All signals in the closed-loop system are guaranteed to be uniformly ultimately bounded, and the relative motion states are all restricted within the known constraints. Compared with the previous control designs of spacecraft rendezvous and proximity operations, the proposed control strategy in this paper can simultaneously deal with input saturation, full-state constraint, kinematic coupling, parametric uncertainty, and matched and mismatched disturbances. Experimental simulation results validate the performance and robustness improvement of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

This paper investigates the adaptive tracking-control problem for a class of nonlinear systems with uncertain parameters, disturbance, and unmodeled dynamics. A novel model reference adaptive control (MRAC) with external filter is proposed to not only stabilize the error system but also guarantee its transient performance. The proposed control strategy can mathematically ensure designable L\infty norm bounds of the difference between the original model’s and the reference model’s state and their control inputs as well. On the other hand, the reference model, which is deemed as a linear time-invariant (LTI) system, can be designed to run in a desired performance level by selecting a suitable filter in the control structure. Another improvement emphasizes that the proposed approach can attain the above performance without using a high-static adaptation gain, which avoids the possible negative effects in the context of robustness. Finally, simulation on a high-accuracy motion-system model driven by a direct-current (dc) motor is given to verify the proposed approach, and the effectiveness is shown based on the simulation curves and analysis. [ABSTRACT FROM PUBLISHER]

This paper presents a robust adaptive iterative learning control (ILC) algorithm for 3-DOF permanent magnet (PM) spherical actuators to improve their trajectory tracking performance. The dynamic model of a PM spherical actuator is a multivariable nonlinear system with interaxis coupling terms. Uncertainties such as modeling errors, loads, and external disturbances exist in the model unavoidably, which will affect the performance, including the precision of the control system. Hence, to compensate for these uncertainties, a new hybrid control scheme that consists of a proportional–derivative (PD) feedback control with varying gains, a PD-type ILC with adjustable gains, and a robust term is developed. The new control law combines the advantages of simplicity and easy design of the PD control, the effectiveness of the ILC to handle model uncertainties and repetitive disturbances, and the robustness of the robust term to random disturbances. In addition, to expedite the convergence rate, the gains in the PD feedback control and the PD-type ILC are adaptively adjusted according to the iteration times. It is shown that the system tracking error approaches zero as the number of iterations increases. To demonstrate the proposed algorithm and verify the established theoretical results, both simulations and experiments are conducted. The results have shown that the proposed control algorithm can effectively compensate for various uncertainties and can thus improve the trajectory tracking performance of spherical actuators. [ABSTRACT FROM AUTHOR]

This paper concerns high-accuracy tracking control for hydraulic actuators with nonlinear friction compensation. Typically, LuGre model-based friction compensation has been widely employed in sundry industrial servomechanisms. However, due to the piecewise continuous property, it is difficult to be integrated with backstepping design, which needs the time derivation of the employed friction model. Hence, nonlinear model-based hydraulic control rarely sets foot in friction compensation with nondifferentiable friction models, such as LuGre model, Stribeck effects, although they can give excellent friction description and prediction. In this paper, a novel continuously differentiable nonlinear friction model is first derived by modifying the traditional piecewise continuous LuGre model, then an adaptive backstepping controller is proposed for precise tracking control of hydraulic systems to handle parametric uncertainties along with nonlinear friction compensation. In the formulated nonlinear hydraulic system model, friction parameters, servovalve null shift, and orifice-type internal leakage are all uniformly considered in the proposed controller. The controller theoretically guarantees asymptotic tracking performance in the presence of parametric uncertainties, and the robustness against unconsidered dynamics, as well as external disturbances, is also ensured via Lyapunov analysis. The effectiveness of the proposed controller is demonstrated via comparative experimental results. [ABSTRACT FROM PUBLISHER]

This paper studies the distributed consensus problem of multiagent systems (MASs) in the presence of nonidentical unknown nonlinear dynamics and undetectable actuation failures. Of particular interest is the development of a robust adaptive fault-tolerant consensus protocol capable of compensating uncertain dynamics/disturbances and time-varying yet unpredictable actuation failures simultaneously. By introducing the virtual parameter estimation error into the artfully chosen Lyapunov function, the consensus problem is solved with a robust adaptive fault-tolerant control scheme based upon local (neighboring) agent state information. It is shown that the proposed method is user friendly in that there is no need for detail dynamic information of the agent or costly detection/diagnosis of the actuation faults in control design and implementation, resulting in a structurally simple and computationally inexpensive solution for the leaderless consensus problem of MAS. Simulation results illustrate and verify the benefits and effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

An adaptive robust position control for real-time high-performance applications of induction motors is developed in this work. The proposed sliding-mode controller provides a global asymptotic position tracking in the presence of model uncertainties and load torque variations. The proposed control scheme incorporates an adaptation law for the switching gain, so that the controller can calculate the switching gain value that is necessary to overcome the existing system uncertainties. The design also incorporates a sliding-mode-based load torque and rotor flux observers in order to improve the control performance without using sensors that increase cost and reduce reliability. The proposed design does not present a high computational cost and, therefore, can be easily implemented in real-time applications. Simulated and experimental results show that this scheme provide high-performance dynamic characteristics, and that it is robust with respect to plant parameter variations and external load disturbances. [ABSTRACT FROM PUBLISHER]

In this paper, we have developed a novel large measurement-range atomic force microscopy (AFM) system performing the tapping mode operation. This system consists of a compact/low-cost scanning probe-type sensing system (z-scanner) and a hybrid xy-scanner. To achieve precision measurement through image scan of given samples, a thorough mathematical modeling is established first, and an advanced robust adaptive controller is then proposed, which can deal with unknown parameters, cross-talk effects, external disturbances, and unknown hysteresis phenomena. The salient properties of the resulting closed-loop AFM system includes long traveling range, high precision, and fast response after integrating two kinds of actuations. To demonstrate and qualify the scanning capability of the proposed system, systematic experiments have been conducted. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive fuzzy control scheme is introduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability-based fuzzy speed controller that capitalizes on the machine's inverse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and parametric uncertainties is achieved. Moreover, no explicit currents loop regulation is needed, which simplifies the control structure and unlike other control strategies, no a priori offline training, weights initialization, parameters knowledge, voltage, or current transducer is required. The system's convergence and stability are proved by Lyapunov stability theory, which yields an improved performance. Simulation results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions. Furthermore, the adaptive fuzzy systems inherent parallelism makes them a good candidate for implementation in real-time PMSM drive systems. [ABSTRACT FROM PUBLISHER]

This paper presents a global task coordinate frame (TCF) (GTCF)-based integrated direct/indirect adaptive robust contouring controller for linear-motor-driven biaxial systems that achieves both stringent contouring performance and accurate parameter estimations. In contrast to the past research works those use various locally defined TCFs “attached” to the desired contour to approximately calculate the contouring error for feedback controller designs, this paper first formulates the contouring control problem using a recently developed GTCF, in which calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical-model-based indirect-type parameter-estimation algorithm is then synthesized to obtain accurate online estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust controller with dynamic-compensation-type fast adaptation is also constructed to preserve the excellent transient and steady-state performance of the direct adaptive robust control (ARC) designs. Comparative experimental results show that the proposed GTCF-based integrated direct/indirect ARC algorithm not only achieves the best contouring performance but also possesses rather accurate estimations of physical parameters. [ABSTRACT FROM PUBLISHER]

The problem of attitude stabilization for a spacecraft system which is nonlinear in dynamics with inertia uncertainty and external disturbance is investigated in this paper. An adaptive law is applied to estimate the disturbances, where a sliding mode controller is designed to force the state variables of the closed-loop system to converge to the origin. Then, the spacecraft system subjected to control constraints is further considered, and another adaptive sliding mode control law is designed to achieve the attitude stabilization. No prior knowledge of inertia moment is required for both of the proposed adaptive control laws, which implies that the designed control schemes can be applied in spacecraft systems with a large parametric uncertainty existing in inertial matrix or even in unknown inertial matrix. Also, simulation results are presented to illustrate the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]

In the face of large-scale parametric uncertainties, the single-model (SM)-based sliding mode control (SMC) approach demands high gains for the observer, controller, and adaptation to achieve satisfactory tracking performance. The main practical problem of having high-gain-based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics, causing poor tracking performance. In this paper, a multiple model/control-based SMC technique is proposed to reduce the level of parametric uncertainty to reduce observer-controller gains. To this end, we split uniformly the compact set of unknown parameters into a finite number of smaller compact subsets. Then, we design a candidate SMC corresponding to each of these smaller subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that approximates closely the plant at each instant of time. The key idea is to allow the parameter estimate of conventional adaptive sliding mode control design to be reset into a model that best estimates the plant among a finite set of candidate models. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development. [ABSTRACT FROM AUTHOR]

The problem of attitude control for a spacecraft model that is nonlinear in dynamics with inertia uncertainty and external disturbance has been investigated. Adaptive law and extended state observer are applied to estimate the disturbance, by which sliding-mode controllers are designed to combine the two approaches in order to force the state variables of the closed-loop system to converge to the reference attitude states. Also, simulation results are presented to illustrate the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]