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This article focuses on the fast finite-time tracking consensus for uncertain nonlinear multiagent systems. By gracefully combining the hierarchical decomposition, adding a barrier power integrator and disturbance compensation techniques, a new adaptive fast finite-time controller is proposed. Compared with the existing works, the proposed method has several distinguishing features: 1) It can achieve fast finite-time convergence with full state constraints; 2) it can deal with some fully unknown nonlinearities and state-dependent disturbance. Moreover, the nonlinearities can be related with the states of all the followers; 3) the input of the leader is not required to be zero, meaning a broad class of references signals can be generated; and 4) the controller is computationally simply and ready to be implemented. No fuzzy logic/neural networks are needed. Applications of the proposed method on multiple servo motors are also studied. [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]

In this article, a butterfly-like Prandtl–Ishlinskii (PI) hysteresis model and a novel neural network based adaptive implicit inverse control scheme to describe and control the butterfly-like hysteresis are proposed. The main contributions are: 1) a butterfly-like PI model is developed for the purpose of predicting the hysteresis effects and the model is feasible for controller design; 2) an implicit inverse control scheme especially for mitigating the butterfly-like hysteresis is implemented, which avoids the construction of the direct inverse of the butterfly-like PI model; 3) an adaptive implicit inverse control approach, which integrates the neural network and the implicit inverse technique into the output-feedback control is developed for eliminating the butterfly-like hysteresis and an arbitrarily small $L_{\infty }$ norm of tracking error is achieved. The proposed modeling and control methods are validated experimentally via the dielectric elastomer actuator based motion control platform. [ABSTRACT FROM AUTHOR]

This article presents a saturated adaptive-law-based backstepping control method for a class of uncertain nonlinear systems with external dynamic disturbances. In this approach, a novel adaptive backstepping control method with a new command filter is primarily proposed to design the feedback controllers. In order to address the system unknown nonlinearities caused by external dynamic disturbances, a method named “saturated adaptive law approach” is used to estimate the system unknown nonlinearities online. Meanwhile, a filter called “dual command filter” is applied to estimate the derivative of certain signal such that the saturated adaptive law approach can be well implemented. With the help of Lyapunov stability criteria, it is proved that the target signal can be tracked by the output of the system with small error. Finally, our control method is applied to a quadrotor hover system, and two sets of comparison experiments are given to show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultaneously achieve the obstacle avoidance and tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties. [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]

This article presents a novel control strategy for a dual active bridge series resonant converter. The strategy seeks to ensure the stability of the converter over its entire dynamic range while enhances the transient response. Both properties allow the use of the converter in new applications, where fast dynamics are required, surpassing the performance of existing feedback loops. Starting from the generalized averaged model of the converter, we propose a nonlinear control strategy by means of Lyapunov's stability theory. After that, we derive a series of modifications in order to implement the strategy in a microcontroller or a DSP, including a sensorless method to tackle the lack of measurements of certain variables and an adaptive control law to deal with uncertain parameters in the model. The strategy is evaluated in simulations and experiments, employing a commercial converter, and comparing the results with other control policies. [ABSTRACT FROM AUTHOR]

This article proposes a novel optimal tracking control scheme for permanent magnet synchronous motors (PMSMs) with partially unknown dynamics, saturation voltages, and disturbances in both speed and current dynamics. The strict-feedback nonlinear system is employed to present the PMSM model. Augmented feedforward control inputs are proposed to transform a speed and current tracking control problem of conventional cascade structures to an optimal control problem of a new structure. Consequently, the saturated adaptive optimal control law is designed for the problem. The optimal solution of Hamilton–Jacobi–Issac equation, which provides the value to the control law, is approximated by a simple online approximator. An integral reinforcement learning technique is used to tune the approximator without observing unknown dynamics. It is proven that the optimal value function, the control law, and the worst disturbance law converge to the near-optimal values. The simulation and experiment on a PMSM prototype model of a load drive application with a digital signal processing board TMS320F28379D of Texas Instrument are conducted to justify the effectiveness of the proposed scheme. [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]

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]

This article proposes an equal-distance surrounding control method for second-order nonlinear multiagent systems (MASs) to encircle multiple moving targets with guaranteed collision avoidance. First, a distributed estimator is developed to approximate the center of moving targets. Then, an adaptive distributed control law is designed for the MASs to accomplish equal-distance surrounding collaboratively. In particular, conditions for assuring asymptotical stability for the closed-loop MAS are derived. Finally, experimental results with unmanned surface vessels are reported to substantiate the effectiveness of the proposed coordinated surrounding control method. [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]

In this article, the problem of the stochastically finite time stabilization for an uncertain single-input and single-output stochastic system in presence of input quantization is studied. The broad learning system (BLS) is first applied to identify the uncertain system with unknown dynamics. The problem of unmeasured states can be solved by establishing a novel BLS-based state observer. Combining the stochastically finite time theorem with Itô formula, a new finite time design method is proposed, which can reduce the difficulty in designing controllers by traditional methods. A stochastically finite time quantized control method is presented by utilizing a new finite time design Lemma and quantized input decomposition technique. The developed control approach can guarantee that the closed-loop system is semi-global finite-time stable in probability, and the convergence performances are well in presence of actuator quantization. The simulation on a chemical reactor is utilized to verify the proposed scheme, which demonstrates the advantage of BLS, as well as the validity of our control method. [ABSTRACT FROM AUTHOR]

Pump control hydraulic systems can achieve high efficiency by the advantages of no throttling loss and high power-to-volume ratio. However, low tracking accuracy and slow frequency response are main drawbacks for the applications of pump control hydraulic systems, because of the existing high-order dynamics, uncertainties, and highly nonlinear dynamics. Recently, the advent of servomotor-pump direct-drive electrohydraulic systems shows a good prospect for this issue, and the design of the control algorithm is the key to achieve high motion accuracy. In this article, to achieve precision motion control, an adaptive robust control with a backstepping design is proposed for an electrohydraulic system, where the cylinder actuator is direct-driven by a servomotor pump. Considering the high-order dynamics and nonlinearities of hydraulic systems, the controller is processed in two steps: position tracking step and pressure step. Besides, the pump flow deviation under low speed is another important limitation for good control performance. Thus, a nonlinear pump flow rate mapping is proposed by practical fitting and used into the controller design by the proper nonlinearity compensation of the desired pump flow. Comparative experiment results show that the proposed control strategy achieves high motion control performances in spite of the nonlinearities and uncertainties. [ABSTRACT FROM AUTHOR]

Focusing on the nonlinear time-delay systems actuated by smart-material-based actuators, an adaptive dynamic surface estimated inverse motion control scheme is proposed in this article. The asymmetric hysteresis nonlinearity is counteracted by constructing its evaluated inverse compensator. A state estimator is constructed to estimate the unmeasurable states and at the same time to cope with external disturbances. The assumptions on time-delay functions are removed, and the unknown time-delay function is online evaluated by combining the fuzzy logic system with a finite covering lemma. Also, the prespecified tracking performance is achieved by utilizing an error-transformed function. Experimental results of the proposed controller are performed on the piezoelectric positioning stage to verify the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

In this article, we study the control problem of the vehicle active suspension systems (ASSs) subject to actuator failure. An adaptive control scheme is presented to stabilize the vertical displacement of the car-body. Meanwhile, the ride comfort, road holding, and suspension space limitation can be guaranteed. In order to overcome the uncertainty, the neural network is developed to approximate the continuous function with the unknown car-body mass. Furthermore, to improve the transient regulation performance of ASSs when the actuator failure occurs, we propose a novel control scheme with the prescribed performance function to characterize the tracking error convergence rate and maximum overshoot in ASSs. Then, the stability of the proposed control algorithm can be proven based on the Lyapunov theorem. Finally, the comparative simulation results of two actuator failure types (i.e., the float fault and the loss of effectiveness fault) are given to demonstrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

In this paper, we study the adaptive fuzzy formation control of multiple autonomous agents with prioritized missions. For a platoon of autonomous agents in an unknown environment containing multiple obstacles, formation control is investigated, where each agent is modeled by a second-order nonlinear system under unknown external disturbance in the Brunovsky form. We introduce the systematic procedure of null-space-based projection to convert the prioritized multimission control problem into a behavioral control problem. Then, we further develop a class of nonlinear-fast-terminal-sliding-mode-based adaptive control strategies that combine the fuzzy logic systems by jointly considering both kinematic and dynamic levels of the agents. The proposed controllers can guarantee each individual agent to achieve the predesigned desired pattern and drive the entire systems to achieve the prescribed missions. A simulation example with five agents demonstrates the effectiveness of the algorithm. Finally, the strategies are experimentally validated using a platoon of Pioneer 3AT and 3DX mobile robots. [ABSTRACT FROM AUTHOR]

This paper aims at the fault-tolerant adaptive control for the nonlinearly parameterized systems with mismatched disturbances. A novel adaptive control scheme is developed in this paper by adding a power integrator technique. The mismatched disturbances are estimated by a designed disturbance observer. A new fault-tolerant control method is proposed to deal with the uncertain actuator faults including actuator stuck. The proposed adaptive controller ensures that the closed-loop system is input-to-state stable. Simulation results for a robotic arm system are given to present the effectiveness of the developed control technique. [ABSTRACT FROM AUTHOR]

An adaptive controller is proposed for unknown nonlinear discrete-time systems when the control direction is unknown and unfixed. Two fuzzy rules emulated networks are utilized to establish a system dynamic model which provides the control-gain and the control direction. The control law is developed according to the control direction including the following advantages: first, the relation between input and output of controlled plant cannot be generally Lipschitz with the zero-change of control effort; and second, the control direction is unfixed for both positive and negative values. Theoretical expressions are manifested to guarantee the convergence of model and tracking performance. The practical-validation is conducted by two experimental systems: first, dc motor current control for the case of fixed control direction and Lipschitz condition, and second, robotic system with opened architecture for the case of unfixed control direction and non-Lipschitz condition at constant input. [ABSTRACT FROM PUBLISHER]

A brief overview on the model-based control and data-driven control methods is presented. The data-driven equivalent dynamic linearization, as a foundational analysis tool of data-driven control methods for discrete-time nonlinear systems, is introduced in detail with motivations and distinct features. The prototype model-free adaptive control schemes by using the dynamic linearization to an unknown nonlinear plant model, as well as the alternative model-free adaptive control methods by using the dynamic linearization to an unknown ideal nonlinear controller, are discussed. Furthermore, the extensions of the dynamic linearization to unknown nonlinear repetitive systems and the corresponding model-free adaptive iterative learning control methods are also overviewed and summarized. This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers. Finally, some perspectives on data-driven control methods in information-rich age are given. [ABSTRACT FROM AUTHOR]

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]

In this paper, a multiple adaptive observer-based strategy is proposed for the control of multi-input multi-output nonlinear processes using input/output (I/O) data. In the strategy, the pseudopartial-derivative parameter matrix of compact form dynamic linearization is estimated by a multiple adaptive observer, which is used to dynamically linearize a nonlinear system. Then, the proposed data-driven model-free-adaptive-control algorithm is only based on the online identified multiobserver models derived from the I/O data of the controlled plants, and Lyapunov-based stability analysis is used to ensure that all signals of the close-loop control system are bounded. A numerical example and a Wood/Berry distillation column example are provided to show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations. [ABSTRACT FROM PUBLISHER]

This paper describes a novel development of a lower limber exoskeleton for physical assistance and rehabilitation. The developed exoskeleton is a motorized leg device having a total of 4 DOF with hip, knee, and ankle actuated in the sagittal plane. The exoskeleton applies forces and learns the impedance parameters of both robot and human. An adaptive control scheme by incorporating learning control approaches into the exoskeleton system is developed to help the leg movement on a desired periodic trajectory and handle periodic uncertainties with known periods. The proposed control approach does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model and an exoskeleton model in the presence of bounded nonlinear disturbances (e.g., spasticity and fatigue). The performance of the controller is demonstrated through closed-loop experiments on human subjects. The experiments illustrate the ability of the exoskeleton to enable the leg shank to track single and multiple period trajectories with different periods and ranges of motion. [ABSTRACT FROM PUBLISHER]

This paper proposes an adaptive control for a class of nonlinear mechanisms with guaranteed transient and steady-state performance. A performance function characterizing the convergence rate, maximum overshoot, and steady-state error is used for the output error transformation, such that stabilizing the transformed system is sufficient to achieve the tracking control of the original system with a priori prescribed performance. A continuously differentiable friction model is adopted to account for the friction nonlinearities, for which primary model parameters are online updated. A novel high-order neural network with only a scalar weight is developed to approximate unknown nonlinearities and to dramatically diminish the computational costs. Comparative experiments on a turntable servo system are included to verify the reliability and effectiveness. [ABSTRACT FROM PUBLISHER]

In this paper, the recently developed integrated direct/indirect adaptive robust control (DIARC) for a class of nonlinear systems with unknown input dead-zones is combined with the desired compensation strategy to synthesize practical high-performance motion controllers for precision electrical drive systems having unknown dead-zone effects. The effect of measurement noise is alleviated by replacing noisy state feedback signals with the desired state needed for perfect output tracking. Theoretically, certain guaranteed robust transient performance and steady-state tracking accuracy are achieved even when the overall system may be subjected to parametric uncertainties, time-varying disturbances, and other uncertain nonlinearities. Furthermore, zero steady-state output tracking error is achieved when the system is subjected to unknown parameters and unknown dead-zone nonlinearity only. The proposed algorithm is also experimentally tested on a linear motor drive system preceded by a simulated unknown nonsymmetric dead-zone. The comparative experimental results obtained validate the necessity of compensating for unknown dead-zone effects and the high-performance nature of the proposed approach. [ABSTRACT FROM AUTHOR]

We consider the sliding-mode output feedback controller (SMOFC) design problem for a class of uncertain multi-variable systems. We first design a stabilizing SMOFC for matched uncertain systems. Using linear matrix inequalities (LMI5), we derive a necessary and sufficient condition for the existence of a linear sliding surface depending on outputs and compensator states. Using the solution of the LMI existence condition, we characterize the gain matrices. We give the nonlinear switching feedback gain guaranteeing the reachability condition. Second, we give an LMI-based design method to combine various useful performance criteria which can be used to guarantee a desired robust performance in spite of mismatched uncertainties. The performance criteria include α-stability, LQ performance, H2/H∞ performance, and peak-to-peak gain bound. In particular, we show that by including H∞ performance constraints, we can easily solve the SMOFC design problem for challenging system models to which the previous methods are not easily applicable. Finally, we give a numerical design example showing that our method can be successfully applied to the problem of designing reduced-order SMOFCs for uncertain time-delay systems or mismatched uncertain systems. [ABSTRACT FROM AUTHOR]

This paper investigates the control of an uninterruptible power supply (UPS) using a combined measurement of capacitor and load currents in the same current sensor arrangement. The purpose of this combined measurement is, on one hand, to reach a similar performance as that obtained in the inductor current controller with load current feedforward and, on the other hand, to easily obtain an estimate of the inductor current for overcurrent protection capability. Based on this combined current measurement, a voltage controller based on resonant harmonic filters is investigated in order to compensate for unbalance and harmonic distortion on the load. Adaptation is included to cope with uncertainties in the system parameters. It is shown that after transformations the proposed controller gets a simple and practical form that includes a bank of resonant filters, which is in agreement with the internal model principle and corresponds to similar approaches proposed recently. The controller is based on a frequency-domain description of the periodic disturbances, which include both symmetric components, namely, the negative and positive sequence. Experimental results on the output stage of a three-phase three-wire UPS are presented to assess the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Presents a decentralized indirect adaptive control for a class of two-time-scale nonlinear systems. Stability analysis for the full-order adaptively controlled closed-loop system; Application to a two-link flexible-joint manipulator; Stability and robustness of the corrected reduced-order slow subsystems with decentralized adaptive controller.