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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]

The remaining useful life (RUL) prediction of servo motors plays a greatly indispensable role in preventing abnormal and inefficient operation of servo drive system and even major safety accident. In this article, a novel variant of recurrent neural networks, called gated adaptive hierarchical attention unit (GAHAU), is constructed to efficiently predict the RULs of servo motors, which can not only pay attention to the outputs of reset and update gates but also adaptively divide the hierarchy of the sequence information in attention gate. The updating formulas of GAHAU are derived. With the raw vibration signals of servo motors, a quadratic function-based deep convolutional autoencoder is used for automatically generating the health indicator vector. Based on the constructed HI vector, the RULs of servo motors are accurately predicted by GAHAU. The experiments on servo motors demonstrate that the presented GAHAU has remarkable prediction performance especially for the long-term prediction. Compared to the existing typical RUL prediction methods, the proposed methodology has stronger predictive ability according to the comparative results. [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]

State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this article, which uses the proposed adaptive send-on-delta (SoD) nonuniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD nonuniform signal sampling approach reduces the traffic over the wireless communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets algorithm. Finally, an Internet of things (IoT) experimental platform based on LoRaWAN and IEEE 802.11 (WiFi) protocols are developed to analyze the performance of the state estimator for the IEEE 5 Bus case study microgrid. [ABSTRACT FROM AUTHOR]

In microgrids, intermittency of renewable energy sources (RES) and uncertain state-of-charge (SoC) of energy storage systems (ESS) can cause power deficiency to some distributed generation units (DGs). In this case, DGs with power deficiency may not meet the power demand, resulting in voltage collapse or frequency divergence. Unfortunately, this is seldom considered in inverter control design in existing literature. Thus, in-depth investigation into the microgrid performance under renewable energy resource fluctuations and appropriate control methods are urgently needed. In this article, an adaptive droop and adaptive virtual impedance control strategy is proposed. Unlike conventional droop control where the droop coefficients are fixed by assuming the DGs can always meet the load demand, the droop coefficients here are adjusted according to actual solar PV power output. In this way, proper power sharing among DGs can be achieved under renewable energy variation. Furthermore, the impact of varying DG capacities on system stability is mathematically investigated. An adaptive virtual impedance is then incorporated into the adaptive droop method to deal with the system instability caused by renewable energy variations. The proposed strategy is analyzed theoretically and validated in MATLAB/Simulink simulation and laboratory experiments. The results demonstrate the advantages of the proposed method over conventional approaches under various scenarios. [ABSTRACT FROM AUTHOR]

In this article, an adaptive repetitive learning control (ARLC) scheme is proposed for permanent magnet synchronous motor (PMSM) servo systems with bounded nonparametric uncertainties, which are divided into two separated parts. The periodically nonparametric part is involved in an unknown desired control input, and a fully saturated repetitive learning law with a continuous switching function is developed to ensure that the estimate of the unknown desired control input is continuous and confined with a prespecified region. The nonperiodically nonparametric part is transformed into the parametric form and compensated by designing the adaptive updating laws, such that a prior knowledge on the bounds of uncertainties is not required in the controller design. With the proposed ARLC scheme, a high steady-state tracking accuracy is guaranteed, and comparative experiments are provided to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The wastewater treatment is an important avenue of resources cyclic utilization when coping with the modern urban diseases. However, there always exist obvious nonlinearities and uncertainties within wastewater treatment systems, such that it is difficult to accomplish proper optimization objectives toward these complex unknown platforms. In this article, a data-driven iterative adaptive critic (IAC) strategy is developed to address the nonlinear optimal control problem. The iterative algorithm is constructed with a general framework, followed by convergence analysis and neural network implementation. Remarkably, the derived IAC control policy with an additional steady control input is also applied to a typical wastewater treatment plant, rendering that the dissolved oxygen concentration and the nitrate level are maintained at desired setting points. When compared with the incremental proportional–integral–derivative method, it is found that faster response and less oscillation can be obtained during the IAC control process. [ABSTRACT FROM AUTHOR]

In this article, an adaptive neural network control scheme is presented for an uncertain wheeled mobile robot (WMR) with velocity constraints and nonholonomic constraints. In practice, dynamic parameters of the system, which may change in some conditions, are hard to obtain precisely, and the velocity of the WMR should be constrained for safety. To deal with the uncertainty of the robot, adaptive neural networks are used to approximate unknown robotic dynamics, and the barrier Lyapunov function is used to guarantee the constraint on velocity. The tracking error of the closed-loop system is proven to converge to a small neighborhood of zero. Both simulation studies and practical experiments are provided to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article investigates the fully distributed observer-based adaptive fault-tolerant synchronization problem (SP) of multiagent systems with event-triggered control mechanisms. First, a nonlinear and discontinuous adaptive observer-based event-triggered fault-tolerant controller is proposed for each agent to overcome the occurrence of unknown faults and unmeasurable full states of the controlled system. Besides, an adaptive triggering function consisting of state-dependent and time-dependent threshold is developed to adjust the parameter of triggering threshold online. Then, a modified nonlinear and continuous observer-based controller with adaptive ETC strategy is developed to overcome the chattering phenomenon from the discontinuous controller. It is proved that under two controllers, the SP not only can be achieved in a fully distributed way without continuous communication for both the controller updates and the triggering condition detecting but also exclusion of “Zeno behavior” can be realized. Finally, the effective algorithms can be verified by giving numerical simulations related to mobile robots. [ABSTRACT FROM AUTHOR]

For improving the convergence rate and time-varying disturbance rejection ability, an adaptive nonsingular terminal sliding mode controller based on disturbance compensation technique is proposed to control the position of magnetic levitation system in this article. First, the dynamical model of the magnetic levitation system is deduced, then a nonsingular terminal sliding mode control (NTSMC) is constructed. For reducing the chattering phenomenon, the time-varying disturbance estimation and compensation is cooperated into NTSMC controller using generalized proportional integral observer, the switching gain value could be chosen smaller, it could improve the system disturbance rejection, but the dynamic response is sacrificed to some extent. Thus, the adaptive NTSMC method is proposed for ensuring the dynamic and steady-state performance, the switching gain value could be tuned adaptively depending on the system states. When the system states are far away from the sliding mode surface, the gain value is large, else if the system states convergence to sliding mode surface, the value is small. Simulation and experimental results confirm the closed-loop performance both in dynamic response and disturbance attenuation, corresponding quantitative comparison could also show the advantages of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article proposes an efficient intelligent control structure for uncertain nonlinear systems. This controller is a new self-organizing fuzzy cerebellar model articulation controller (CMAC), which has a framework that includes a CMAC and which uses sliding mode control. A new mixed Gaussian membership function (GMF) is created using a prior GMF and a present GMF for each layer of the CMAC, which reuses relevant data in the prior GMF to more accurately detect tracking errors. This is more general than the local-feedback of a recurrent unit because inputs can simultaneously stir the present state and the prior state to regulate suitable errors. Using a self-organizing algorithm allows increasing or decreasing the layers so that the structure of the new self-organizing fuzzy CMAC (NSOFC) is constructed automatically. The proposed control system consists of a NSOFC and a compensation controller. The NSOFC is the main tracking controller, and imitates an ideal controller; and the compensator expels the approximation error. A Lyapunov stability function is used to make the system stable, and an adaptive proportional integral method allows online updating of the parameters for efficient control. An inverted double pendulum system and a magnetic levitation system are used to demonstrate that the proposed method gives good tracking performance. [ABSTRACT FROM AUTHOR]

Human neural motor system has the intelligence to learn new skills, and then to generalize these skills naturally. But it is not easy for a robot to demonstrate such intelligent behaviors. Inspired by the neural motor behaviors, a framework of broad learning based novel adaptive neural control is proposed in this article, such that in the presence of dynamic disturbance, robots can learn a set of basic skills and then generalize these skills to the neighboring movements naturally as our human motor system. This is achieved by incorporating the deterministic learning with the broad learning system that can accumulate and reuse the learned knowledge. The broad learning enabled adaptive neural control has been rigorously established in theory and tested in both simulation and experimental studies. Simulation results and performance of the Baxter robot in the experiments have shown the effectiveness and superiority of the proposed method in comparison to the conventional adaptive neural control. [ABSTRACT FROM AUTHOR]

An event-triggered load frequency control problem for networked multiarea power systems is discussed, where the event-triggered control scheme has an adaptive threshold. This article is motivated by the consideration for a bandwidth-limited communication channel. To reduce the communication network bandwidth burden, this article develops an adaptive event-triggered control scheme to reduce the number of transmitted packets while keeping the desired control performance. From numerical simulations, we find that if the adaptive law is chosen inappropriately, then it is possible that the event-triggered scheme with an adaptive threshold is more conservative than one with a fixed threshold. Motivated by this, an improved adaptive law of the threshold for the event-triggered scheme is given such that the adaptive event-triggered scheme outperforms one with a constant threshold. By using the Lyapunov method and linear matrix inequality techniques, stability and stabilization criteria are obtained. Finally, numerical examples are given to illustrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural network backstepping control design for an experimental helicopter with three rotational degrees of freedom. The helicopter system is described by a state-space model with unmodeled dynamics and external disturbances. By resorting to radial basis function neural networks with online gradient descent training, such unmodeled dynamics and external disturbances can be compensated in real time. With the help of Lyapunov stability criteria, it is proved that the desired trajectories can be boundedly tracked by the corresponding attitude angles of the helicopter under the proposed adaptive neural network backstepping controller. Compared with robust adaptive integral backstepping technique, our approach has smaller root mean squared error. Finally, four sets of contrastive experiment results are, respectively, given to show the effectiveness and advantages of the proposed control scheme. [ABSTRACT FROM AUTHOR]

A distributed adaptive controller is proposed in this paper for a class of underactuated Lagrangian systems to control the actuated variables to track a dynamic leader and keep unactuated ones bounded under a directed communication graph. A finite-time observer is introduced to estimate the leader's velocity. Based on two sliding variables defined for the actuated and unactuated channels, adaptive controllers are designed for the underactuated Lagrangian systems subject to uncertain parameters and external disturbances without or with actuator faults. The convergences of the proposed controllers are proven based on the separation principle between the observer and the controller. Finally, simulations and experiments are conducted to verify the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

The precise tracking control problem for n-link flexible-joint (FJ) robotic systemsis addressed in this paper. A new adaptive fuzzy command filtered control strategy is presented, where fuzzy logic systems are utilized to approximate the unknown nonlinearities of FJ robot systems. Compared with existing backstepping-based methods, the proposed scheme can not only overcome the so-called “explosion of complexity” problem, but also reduce filter errors because of the introducing of an error compensation mechanism. Moreover, regardless of the number of fuzzy rules, only one parameter is required to be adjusted online, which reduces significantly the computational cost. The proposed scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The simulation results of a two-link robot system confirm our theoretical analysis and a comparison study demonstrates the advantages of the design method in comparison with existing results, such as the backstepping method and the dynamic surface control method. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive output-feedback fault-tolerant control (FTC) problem for a class of uncertain nonlinear pure-feedback systems subject to actuator faults. To address the challenges incurred by the actuator faults and unmeasured state, a novel fault compensation observer-based adaptive controller is designed by estimating fault parameters online. By combining the backstepping design framework with prescribed performance and command filters techniques, it is proved that the proposed FTC scheme guarantees that all the signals in the closed-loop system are bounded and that the tracking error remains within the predefined bounds even if actuator faults occur. Compared with the existing output-feedback FTC design methods, the compensation state observer removes the restriction that the fault parameters are known or the uncontrollability caused by input saturation. The simulation and experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Mass imbalance frequently exists in a rotating machine. It may induce unbalance force, causing undesirable vibrations and noise. Active magnetic bearing (AMB) has been found to be a good device to deal with such problem. This paper is concerned with the unbalance compensation for a three-pole AMB system. An adaptive compensation scheme is proposed based on the theory of immersion and invariance (I&I) control. It is to immerse a higher-order system into a lower-order target system. The lower-order target system in this paper is the three-pole AMB system with known mass imbalance and with a stabilizing integral sliding mode controller (ISMC). On the other hand, the higher-order system is the three-pole AMB system with both the ISMC levitation controller and the adaptive compensator. The adaptive compensator is designed to deal with the case that the mass imbalance may be unknown and may be changing during operation in practical applications. It estimates the information of mass imbalance online and is incorporated into the ISMC so that the unbalance force can be eliminated. Both numerical simulations and experimental results verify the effectiveness of the proposed adaptive compensator for mass imbalance. [ABSTRACT FROM AUTHOR]

The finite-time attitude control of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances with unknown bound is investigated in this paper. First, an adaptive multivariable finite-time stabilizing control algorithm for second-order multivariable systems is developed based on the improved supertwisting and the equivalent control algorithms. Furthermore, the finite-time stability conditions are derived via the Lyapunov technique. The remarkable feature of the developed algorithm is to eliminate the overestimation of the control gains without loss of the sliding motion. Then, the proposed algorithm is applied for quadrotor UAVs to solve the finite-time attitude-tracking problem. Finally, a comparison of the proposed multivariable algorithm with its scalar counterpart is presented and validated both by simulation and experiment results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive backstepping control scheme is presented to solve the position control problem for a gear transmission servosystem with unknown asymmetric sandwiched dead-zone nonlinearity. All the parameters involved in both the system and dead-zone models are allowed to be totally unknown. In contrast to the previous results where the effects of symmetric dead zone can be treated as additive disturbances in a transformed system, most of the unknown parameters of the transformed system are now time varying and dependent on the real-time value of relative displacement between the driving and driven gears. To deal with the above issue, new compensating terms in the quadratic form are introduced in the design of the virtual control signal for each recursive step. With the adaptive laws modified correspondingly, we can still show that all the closed-loop signals are ensured to be bounded and the output regulation error will converge to a compact set. Simulation results are provided to show the effectiveness of the proposed adaptive control scheme. [ABSTRACT FROM AUTHOR]

An adaptive generalized predictive control (GPC) scheme for an industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of the industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of the neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN-GPC has a satisfactory tracking and control performance. [ABSTRACT FROM AUTHOR]

Increasing interactions between urban electrical and gas systems have the potential to transform smart grids into smart cities, where coordinated dispatch for interdependent urban infrastructure is desirable. This paper proposes an adaptive robust day-ahead energy-reserve co-optimization approach for urban energy systems, in which day-ahead energy-reserve dispatch and real-time energy-balancing regulation are employed as first- and second-stage decisions, respectively. By introducing an auxiliary variable to denote the worst-case balancing cost, we transform the trilevel optimization problem into a single-level model considering generated representative scenarios. Moreover, we propose an iterative algorithm composed of nested problems, where the master problem solves the single-level robust model considering the binding scenario subset identified by the subproblem. In this way, a more computationally tractable model can be obtained, owing to the greatly reduced number of scenarios. Finally, test results on an integrated urban energy network verify the computational superiority of the proposed solution methodology afforded by identifying the binding scenario subset. The economic benefits of the proposed adaptive robust framework are also discussed. [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]

In this paper, a hybrid magnetic bearing (HMB) prototype system is designed and analyzed. Two compact bearings are used to suspend the rotor in five degrees of freedom. Electromagnets are used for axial suspension of the rotor, while permanent magnets are used for the passive radial stability. A brushless DC motor is designed in order to rotate the shaft around its axis. The 3-D finite-element model of the HMB system is established and distribution of magnetic fields in the air gaps and magnetic forces on the rotor under various control currents and displacements is calculated. A nonlinear adaptive sliding-mode controller is designed for the position control of the rotor in axial direction. Since the control characteristics of the active magnetic bearing system are highly nonlinear and time varying with external interference, a radial basis function compensator is designed first, and then, a sliding-mode control law is used to generate the control input. The stability analysis for the designed controller is given based on the Lyapunov theorem. Experimental setup is built to guide the design process. The performance of the HMB system based on the designed control algorithm is evaluated under different operating conditions. [ABSTRACT FROM PUBLISHER]

The three-dimensional multiple constrained guidance problem for a hypersonic gliding vehicle (HGV) against a stationary ground target in dive phase is investigated. A coupled nonlinear dynamic model is established on account of the bank-to-turn control scheme adopted by the HGV, which formulates the relation between line-of-sight angles and trajectory control variables. On this basis, a finite-time nonlinear disturbance observer is introduced to estimate the lumped uncertainty. Utilizing the estimation as feedforward compensation, a guidance law based on the adaptive multivariable super-twisting sliding mode control is designed to realize precise strike with designated impact angles. The finite-time stability of closed-loop guidance system is proven through the Lyapunov technique. Numerical simulations are executed to validate the mission adaptiveness and the robustness of the presented guidance scheme. [ABSTRACT FROM PUBLISHER]

Load frequency control (LFC) is a very important method to keep the power systems stable and secure. However, due to the introduction of communication networks in multi-area power systems, the traditional LFC method is not effective again. This motivates us to investigate an adaptive event-triggering H\infty LFC scheme for multi-area power systems. Compared with the existing time-invariant event-triggering communication scheme, an adaptive event-triggering communication scheme is presented, where the event-triggering threshold can be dynamically adjusted to save more limited network resources, while preserving the desired control performance. Compared with the existing emulation-based method, where the controller must be known a priori, the stability and stabilization criteria derived in this work can provide a tradeoff to balance the required communication resources and the desired control performance. The effectiveness of the proposed method is verified by two numerical examples. [ABSTRACT FROM PUBLISHER]

In this paper, the robust trajectory tracking design of uncertain nonlinear systems is investigated by virtue of a self-learning optimal control formulation. The primary novelty lies in that an effective learning based robust tracking control strategy is developed for nonlinear systems under a general uncertain environment. The augmented system construction is performed by combining the tracking error with the reference trajectory. Then, an improved adaptive critic technique, which does not depend on the initial stabilizing controller, is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with respect to the nominal augmented system. Using the obtained control law, the closed-loop form of the augmented system is built with stability proof. Moreover, the robust trajectory tracking performance is guaranteed via Lyapunov approach in theory and then through simulation demonstration, where an application to a practical spring–mass–damper system is included. [ABSTRACT FROM PUBLISHER]

Under the weaker conditions on system power and nonlinear functions, this paper focuses on the robust adaptive state constraint control for a class of uncertain switched high-order nonlinear systems. With the help of $p$-times differentiable unbounded function, a robust adaptive state-feedback controller is designed by combining the homogeneous domination approach with parameter separation principle. When the initial condition satisfies a suitable requirement, it is shown that all signals of the closed-loop system are bounded and system state is within a prespecified limit range by the proposed controller. As a practical application, the design scheme is utilized to the continuous stirred tank reactor with two modes feed stream. To further demonstrate the efficiency of the proposed controller, another numerical example is given. [ABSTRACT FROM PUBLISHER]

By employing neural network approximation architecture, the nonlinear discounted optimal regulation is handled under event-driven adaptive critic framework. The main idea lies in adopting an improved learning algorithm, so that the event-driven discounted optimal control law can be derived via training a neural network. The stability guarantee and simulation illustration are also included. It is highlighted that the initial stabilizing control policy is not required during the implementation process with the combined learning rule. Moreover, the closed-loop system is formulated as an impulsive model. Then, the related stability issue is addressed by using the Lyapunov approach. The simulation studies, including an application to a power system, are also conducted to verify the effectiveness of the present design method. [ABSTRACT FROM PUBLISHER]

The gripper rotation system of a heavy-duty manipulator is generally driven by hydraulic motors, and it is difficult to guarantee the accuracy of the rotation angle using conventional control methods due to the existence of dead zones with unknown characteristics. This paper proposes an adaptive inverse dead-zone control method, in which the gradient projection technique is applied to design an adaptive updating law for estimating the inverse dead-zone parameters online. First, a dynamic model of the gripper rotation system is presented with an analysis of its dead-zone characteristics. Then, the controller is designed based on a Takagi–Sugeno fuzzy model, and the estimated values of the inverse dead-zone parameters are shown to converge to their true values. Finally, the performance of the proposed adaptive control method is experimentally demonstrated on an actual forging manipulator. Comparisons with a fixed inverse dead-zone control method demonstrate the effectiveness and applicability of the proposed control method. [ABSTRACT FROM PUBLISHER]

Combining with artificial potential field and robust $H_\infty$ methods, the neural network (NN)-based adaptive synchronized tracking control is proposed for accommodation vessel (AV). The control task is to drive AV synchronous tracking floating production storage and offloading (FPSO). For finishing the task, NN is employed to approximate the unknown nonlinear dynamics of AV; $H_\infty$ method is to guarantee the system states of AV robust to exogenous disturbances; artificial potential method aims to produce the attractive and repulsive forces to assist AV maintaining desired distance with FPSO so that the gangway connecting both AV and FPSO is operated smoothly. Finally, it is proven that the proposed control scheme can guarantee that all error signals of the tracking control are Semi-Globally Uniformly Ultimately Bounded (SGUUB) and AV can synchronously track FPSO to desired accuracy. The simulation results further demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The variable stiffness actuator (VSA) has been equipped on many new generations of robots because of its superior performance in terms of safety, robustness, and flexibility. However, the control of robots with joints driven by VSAs is challenging due to the inherited highly nonlinear dynamics. In this paper, a novel disturbance observer based adaptive neural network control is proposed for robotic systems with variable stiffness joints and subject to model uncertainties. By utilizing a high-dimensional integral Lyapunov function, adaptive neural network control is designed to compensate for the model uncertainties, and a disturbance observer is integrated to compensate for the nonlinear VSA dynamics, as well as the neural network approximation errors and external disturbance. The semiglobally uniformly ultimately boundness of the closed-loop control system has been theoretically established. Simulation and extensive experimental studies have also been presented to verify the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper studies the fault-tolerant tracking control problem for a class of strict-feedback nonlinear systems subjected to actuator faults and external disturbances. The prior knowledge for actuator fault, nonlinearity, and external disturbance is totally unknown, besides the control directions. Based on a backstepping approach, an adaptive fault-tolerant control scheme is developed, without utilizing neural networks. In the control design, a group of new feedback mechanisms are proposed to compensate for the unknown system dynamics and actuator faults. Furthermore, to relax a requirement of the initial system states, a modification technique is designed to adjust the reference signal and virtual control laws for a short time. It is shown that the global closed-loop stability is guaranteed and the tracking performance is achieved. The above result is illustrated via simulations on a one-link manipulator and a ship autopilot. [ABSTRACT FROM PUBLISHER]

Excessive vertical and lateral motion of the train body could endanger the operation safety of a high-speed train. The situation is further complicated if actuation faults occur. This paper investigates a semiactive approach for suppressing such motions. By using the structural properties of the system model, a new control scheme is proposed to account for various factors such as input nonlinearities, actuator failures, and external disturbances in the system simultaneously. The resultant control scheme is capable of automatically generating the intermediate control parameters and literally producing the PD-like controller—the whole process does not require precise information on system model or system parameter. Furthermore, unlike traditional PD control, the proposed one has the stability-guaranteed algorithms to self-adjust its PD gains and there is no need for human tuning or trial and error process. Such user-friendly feature is deemed favorable for practical implementation. The effectiveness of the proposed controller is tested using computer simulations in the presence of parametric uncertainties and varying operation conditions. [ABSTRACT FROM AUTHOR]

This paper focuses on chaos analysis-based adaptive backstepping control of the microelectromechanical resonators. To better understand the deep-rooted operational mechanism, the bifurcation diagram, phase diagrams, and corresponding time histories are presented to analyze the nonlinear dynamics and chaotic behavior of the microelectromechanical resonators. Based on the potential function, it can be shown that the microelectromechanical resonators undergo homoclinic and heteroclinic orbits which relate closely to the appearance of chaos in the resonator response. To suppress chaos and vibration, an adaptive neural network-based backstepping scheme is developed to tune the random motion into regular motion without the need for more precise information of system model. The tangent barrier Lyapunov function is used to develop a control scheme for the microelectromechanical resonators capable of preventing output constraint violation. By using tracking differentiator in controller design, the “explosion of complexity” of backstepping and poor precision of the first-order filters is prevented. Meanwhile, to increase the robustness and adaptivity, an adaptive neural network is employed to approximate uncertain nonlinear item in the framework of backstepping. Finally, numerical simulations are conducted to validate the effectiveness and robustness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper aims to develop a neural-network-based robust adaptive output-feedback motion controller for a class of hysteretic nonlinear systems, where the inverse hysteresis approach is adopted. The developed scheme is applied to the piezoelectric positioning stage, showing the advantages of the proposed approach by the experimental results. The main contributions are: 1) by using the neural networks as an approximator, the nonlinear function in the systems can be completely unknown; 2) by designing a high-gain observer to estimate the states of the system and cope with the uncertainties of the system, only the output of the control system is required measurable; 3) experiments on the piezoelectric positioning stage were conducted where the piezoelectric positioning stage is considered as a third-order system under the condition only the output of the system is available; 4) by adjusting the initial conditions of the states observer and adaptive laws of unknown parameters, the arbitrarily small L \infty norm of the tracking error is deviated. It is proved that all the signals in the closed-loop systems are semiglobally ultimately uniformly bounded. [ABSTRACT FROM PUBLISHER]

An adaptive voltage-sensor-based maximum power point tracking algorithm employing a variable scaling factor for a single-ended primary-inductance converter is presented. In this method, only a voltage divider circuit is used to sense the photovoltaic (PV) panel voltage. This method can effectively improve both transient and steady-state performance by varying the scaling factor as compared with the fixed step size and adaptive step size with fixed scaling factor. For sudden change in solar insolation or in start-up, this method leads to faster tracking, whereas in steady state, it leads to lower oscillations around maximum power point. The steady-state behavior and drift phenomena are also addressed in this paper to determine the tracking efficiency. The duty cycle is generated directly without using any proportional–integral control loop to simplify the control circuit. MATLAB/Simulink is used for simulation studies, and a microcontroller is used as a digital platform to implement the proposed algorithm for experimental validation. The proposed system is implemented and tested successfully on a PV panel in the laboratory. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive neural output feedback control scheme is proposed for uncertain nonlinear systems that are subject to unknown hysteresis, external disturbances, and unmeasured states. To deal with the unknown nonlinear function term in the uncertain nonlinear system, the approximation capability of the radial basis function neural network (RBFNN) is employed. Using the approximation output of the RBFNN, the state observer and the nonlinear disturbance observer (NDO) are developed to estimate unmeasured states and unknown compounded disturbances, respectively. Based on the RBFNN, the developed NDO, and the state observer, the adaptive neural output feedback control is proposed for uncertain nonlinear systems using the backstepping technique. The first-order sliding-mode differentiator is employed to avoid the tedious analytic computation and the problem of “explosion of complexity” in the conventional backstepping method. The stability of the whole closed-loop system is rigorously proved via the Lyapunov analysis method, and the satisfactory tracking performance is guaranteed under the integrated effect of unknown hysteresis, unmeasured states, and unknown external disturbances. Simulation results of an example are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain nonlinear systems. [ABSTRACT FROM PUBLISHER]

In this paper, observer and command-filter-based adaptive fuzzy output feedback control is proposed for a class of strict-feedback systems with parametric uncertainties and unmeasured states. First, fuzzy logic systems are used to approximate the unknown and nonlinear functions. Next, a fuzzy state observer is developed to estimate the immeasurable states. Then, command-filtered backstepping control is designed to avoid the explosion of complexity in the backstepping design, and compensating signals are introduced to remove the effect of the errors caused by command filters. The proposed method guarantees that all signals in the closed-loop systems are bounded. The main contributions of this paper are the proposed control method can overcome two problems of linear in the unknown system parameter and explosion of complexity in backstepping-design methods and it does not require that all of the states of the system are measured directly. Finally, two examples are provided to illustrate its effectiveness. [ABSTRACT FROM PUBLISHER]

In order to achieve higher precise positioning of the throttle plate, an adaptive servo control strategy is presented for the electronic throttle control system. Compared with the existing results on the electronic throttle control schemes, in this paper, the throttle valve reference tracking controller comprises a proportional-integral-derivative-type feedback controller with adaptive gain parameters, an adaptive feedforward compensator, and adaptive nonlinearity compensators for friction, limp-home (LH), and backlash. The closed-loop controller is realized by only utilizing the information of the throttle valve position measured by a cheap potentiometer of low resolution. The theoretical proof and analysis show that the designed throttle control system can ensure fast and accurate reference tracking of the valve plate angle in the case of the uncertain parameters related to production deviations, variations of external conditions and aging, and the effects of transmission friction, return-spring LH, and gear backlash nonlinearity with uncertain parameters. Moreover, the capability of the adaptive controller to preserve the transient performance and accuracy is evaluated in both simulation and experiment. [ABSTRACT FROM PUBLISHER]

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]

A controller area network (CAN)-based distributed system may develop faults at run-time. These faults need to be detected and diagnosed. This paper proposes a new algorithm named adaptive fault diagnosis algorithm for CAN (AFDCAN). It is designed for low-cost resource-constrained distributed embedded systems. The proposed algorithm detects all faulty nodes on the CAN. It allows new node entry and reentry of repaired faulty nodes during a diagnostic cycle. AFDCAN is found to provide high fault tolerance and to ensure reliable communication. It uses single-channel communication deploying the bus-based standard CAN protocol. A hardware implementation of the proposed algorithm has been used to obtain the results. The results show that the proposed algorithm diagnoses all faults in the system. Analysis of the proposed algorithm proves that the algorithm uses a definite and bounded number of testing rounds and messages to complete one diagnostic cycle. [ABSTRACT FROM PUBLISHER]

This paper proposes an online learning adaptive radial basis function neural network (RBFNN) to deal with measurement errors and environment disturbances to improve control performance. Since the weight matrix of the adaptive neural network can be updated online by the state error information, the adaptive neural network can be constructed directly without prior training. Moreover, with the parameter optimization rule, the residual approximation error can be reduced by the maximum absolute position error, average position error, and mean square position error in sampling windows. The applicability of the proposed method is validated by a series of simulations and flight tests. The adaptive RBFNN control method can realize hovering, straight flight, and autonomous landing control under wind disturbances. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of designing adaptive output feedback sliding mode controllers for a class of linear systems with matched external disturbances, where the norm bound of the external disturbance is unknown. First, we derive a new design approach of linear sliding surface in the singular system framework, and propose an adaptive output feedback sliding mode controller such that the motion of the closed-loop system is uniformly ultimately bounded stable. Second, to improve the transient performance, a memory-based adaptive output feedback sliding mode controller is also proposed. Finally, an illustrative example is utilized to demonstrate the design procedures proposed in this paper. It is shown that the memory-based adaptive output feedback sliding mode control approach provides a better transient performance. [ABSTRACT FROM PUBLISHER]

This paper presents a precise positioning robust hybrid intelligent control scheme based on the effective compensation of nonsmooth nonlinearities, such as friction, deadzone, and uncertainty in a dynamic system. A new adaptive fuzzy wavelet echo state network algorithm is proposed to improve performance in terms of approximating unknown uncertainties in conventional neural network algorithms. A strict feedback controller and adaptive laws for estimating the unknown friction and deadzone parameters were developed using the recursive steps of dynamic surface control and a sliding mode control based on Lyapunov stability theory. Lyapunov stability analysis confirmed the boundedness and convergence of the closed-loop system. The performance of the proposed control scheme was validated experimentally by applying it to the precise positional control of a robot manipulator. [ABSTRACT FROM PUBLISHER]

In this paper, an adaptive fuzzy controller is developed for a class of multiinput single-output nonlinear systems, where the system differential output has a monotonically increasing or decreasing relationship with each system differential input. The system dynamics is modeled in fuzzy domain, and the fuzzy inverse model is automatically constructed, which uniquely determines the inverse relationship for each differential input–output pair. Based on this inverse model, a multilevel fuzzy controller is designed with an embedded adaptive structure for each control variable to compensate for system uncertainties and time-varying parameters. Two sets of simulation examples are presented to illustrate the effectiveness of the proposed controller for this class of multivariable systems under different operating conditions. [ABSTRACT FROM PUBLISHER]

This paper proposes an adaptive critic-based real-time redundancy resolution scheme for kinematic control of a redundant manipulator. The kinematic control of the redundant manipulator is formulated as a discrete-time input affine system, and then, an optimal real-time redundancy resolution scheme is proposed. The optimal control law is derived in real time using an adaptive critic framework. The proposed single network adaptive critic-based methodology defines the additional task in terms of integral cost function which results in global optimal solution. With adaptive critic-based optimal control scheme, the optimal redundancy resolution is achieved in real time without the computation of inverse, which makes the method computationally efficient. The problem formulation as proposed in this paper is first of its kinds. Furthermore, the real-time optimal redundancy resolution using an integral cost function has been comprehensively solved, which is also a novel contribution. The proposed scheme is tested in both simulation and experiment on a seven-degree-of-freedom PowerCube manipulator from Amtec Robotics. [ABSTRACT FROM PUBLISHER]

In this paper, a novel adaptive wavelet fuzzy cerebellar model articulation control system is developed for the trajectory tracking control of voice coil motors (VCMs). Since the precise model of the VCM is difficult to obtain, a model-free intelligent control system is developed for the precise trajectory tracking of VCMs. This control system comprises a wavelet fuzzy cerebellar model articulation controller (CMAC) (WFCMAC) and a fuzzy compensator. The novel WFCMAC incorporates the wavelet decomposition property with a fuzzy CMAC fast learning ability, and it is used to imitate an ideal controller. This WFCMAC is a generalization network. In some special cases, it can be reduced to a fuzzy neural network (NN), a wavelet NN, and a CMAC. Moreover, the fuzzy compensator is developed to attenuate the effect of the approximation error caused by the WFCMAC approximator. The online learning algorithm of the controller's parameters is derived based on the Lyapunov function; thus, the stability of the system can be guaranteed. Finally, the developed control system is applied to the trajectory tracking control of a VCM. From the experimental results, it is verified that the proposed WFCMAC control scheme can achieve favorable tracking performance even if the system model of the VCM is unknown. Furthermore, the salient merits are also indicated in comparison with a proportional–integral–derivative controller. [ABSTRACT FROM AUTHOR]

Surface electromyogram (sEMG) signals, a noninvasive bioelectric signal, can be used for the rehabilitation and control of artificial extremities. Current sEMG pattern-recognition systems suffer from a limited number of patterns that are frequently intensified by the unsuitable accuracy of the instrumentation and analytical system. To solve these problems, we designed a multistep-based sEMG pattern-recognition system where, in each step, a stronger more capable relevant technique with a noticeable improved performance is employed. In this paper, we utilized the sEMG signals to classify and recognize six classes of hand movements. We employed an adaptive neuro–fuzzy inference system (ANFIS) to identify hand motion commands. Training the fuzzy system was performed by a hybrid back propagation and least-mean-square algorithm, and for optimizing the number of fuzzy rules, a subtractive-clustering algorithm was utilized. Furthermore, this paper employed time and time–frequency domains and their combination as the features of the sEMG signal. The proposed recognition scheme utilizing the combined features with an ANFIS classification provided the best result in identifying complex hand movements. The maximum identification accuracy rate of 100% and an average classification accuracy of the proposed ANFIS system of 92% proved to be superior in comparison with relevant studies to date. [ABSTRACT FROM AUTHOR]