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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]

This paper introduces the bridgeless triple-mode resonant ac–dc converter and presents its dynamic model and control technique. The proposed converter operates in triple mode: discontinuous-conduction mode (DCM), continuous-conduction mode (CCM), and missing resonance mode (MRM). It has the advantages of small number of required components, medium power capacity, and high efficiency, but it is difficult to control because DCM, CCM, and MRM have their own dynamic characteristics. To solve this control problem, the mode boundaries are first identified and, corresponding to each mode, the dynamic model is developed to design the controller. In each mode, we propose to add a repetitive controller (RC) with a phase-lead compensator to the conventional duty ratio plus feedback controller. The RC is used to suppress the deterministic periodic disturbances and the phase-lead compensator is used to compensate for the incurred phase lag. A 1-kW digitally controlled converter prototype is built and used to demonstrate the validity of the proposed modeling and control approach. [ABSTRACT FROM AUTHOR]

It has been shown that the closed-loop transfer function representation of the unconstrained model predictive control (MPC) for single input single output systems is similar to a two-degree-of-freedom controller, which is capable of improving the tracking performance of positioning systems. Conventionally, the optimal parameters of the above-mentioned transfer function are result of a quite complicated MPC tuning. This paper proposes a new approach to obtain the parameters directly instead, where the input/output constraints are not considered. The method combines conventional feedback and adaptive feedforward techniques to minimize the tracking error as well as mitigate the influence of the load disturbance. Experiments on an ultra precision positioning system actuated by piezoelectric actuator show that the proposed method achieves much better tracking performance over a well-tuned conventional MPC. [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 is devoted to attitude tracking control of fractionated spacecraft with wireless communication. We consider the practical case that the spacecraft suffers from uncertain inertia parameters, external disturbances, and even unknown and time-varying actuator faults. Within the framework of the backstepping method, a novel event-triggered adaptive fault-tolerant control scheme is proposed. In our design, an event-triggering mechanism is introduced to determine the time instants for communication, which successfully avoids continuous communication and Zeno phenomenon. Then, with the aid of a bound estimation approach and a smooth function, the impacts of the actuator faults, as well as the network-induced error, are effectively compensated for. Moreover, by employing the prescribed performance control technique, it is shown that the attitude tracking errors can converge to predefined arbitrarily small residual sets with prescribed convergence rate and maximum overshoot, no matter if there exist unknown actuator faults. Compared with conventional adaptive attitude control schemes, the proposed scheme significantly reduces the communication burden, while providing high reliability and stable, rapid, and accurate response for attitude maneuvers. Simulation results are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes the design and control of a three-phase four-wire distribution static compensator (DSTATCOM) for elimination of several power quality (PQ) issues, namely harmonics, reactive power, load unbalancing, and neutral current. A three-phase voltage source converter (VSC) and a zigzag transformer based configuration are utilized as a DSTATCOM. The zigzag transformer is operated to provide a suitable path for load neutral current and other PQ problems are mitigated by designing a suitable control for VSC. The control of VSC is developed using Euclidean direction search (EDS) technique based adaptive control theory. This control algorithm is designed to extricate fundamental constituents from the load current and used to generate switching pulses for VSC. The improved behavior of the proposed EDS-based control is observed by comparing it with other existing controllers. Real-time performance of the EDS control is verified on a developed prototype in the laboratory using VSC and DSP (dSPACE1104 R&D controller). [ABSTRACT FROM AUTHOR]

A path-following controller based on an uncertainty and disturbance estimator (UDE) for a quadrotor with a cable-suspended payload is proposed in this paper. The quadrotor and the payload are subject to unknown wind disturbances. The controller resembles a cascade architecture. For the outer loop, a UDE-based translational control law is proposed. The controller asymptotically stabilizes the quadrotor along a given path and estimates the lumped disturbances with a low-pass filter. For the inner loop, an attitude tracking controller is used to control the direction of the lift vector so that the actual lift force can asymptotically follow the reference force generated by the translational controller. The stability of the system with the translational controller and the attitude tracking controller has been shown to be asymptotically stable using the reduction theorem. With the help of the reduction theorem, the design of the translational and the attitude control can be decoupled, providing the flexibility of implementing different attitude controllers without redoing the stability analysis. As shown in the simulation, the control law can stabilize the quadrotor on the desired path under different wind disturbances. [ABSTRACT FROM AUTHOR]

In this paper, the control problem for an underactuated quadrotor unmanned aerial vehicle (UAV) with a suspended payload is investigated. An energy-based nonlinear controller is proposed that is able to control the quadrotor UAV's position and the payload's swing angle asymptotically. An adaptive control design is developed to compensate for the unknown length of the cable which is used to connect the UAV and the payload. The Lyapunov-based stability analysis is employed together to prove the stability of the closed-loop system. Detailed real-time experimental results illustrate the good performance of the proposed controller. [ABSTRACT FROM AUTHOR]

In complex terrain, semi-airborne transient electromagnetic surveying can effectively solve the detailed exploration problems to replace ground manual operation. This method has the advantages of high efficiency, deep exploration depth, and high signal-to-noise ratio. As the core component of the system, the constant-current transmission converter determines the primary field source of the surveying system. High-accurate and stable-output electromagnetic transmission technology is the key issues of the surveying equipment development. In this paper, the converter circuit topology is designed in detail and a novel model reference adaptive proportional-integral control algorithm is put forward. The rising overshoot of current is eliminated and falling edge is linear. Finally, the 20-kW high-power transmitting instrument has been completed and used for field experiments. A large number of experimental results show that the transmitting instrument can achieve high-power constant-current emission, stable output, and good dynamic performance. And the steady-state accuracy is better than the traditional transmitter. The converter successfully improved the semi-airborne transient electromagnetic system detection capability and achieved satisfactory results in field experiments. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body. The NNs are used to approximate the unknown mass of car body. It is commonly known that the stability and security of the ASSs will be weakened when the constraints are violated. Thus, the control problem of the time-varying vertical displacement and speed constraints for the quarter-car ASSs is a very important task because of the demand of the handing safety. The time-varying barrier Lyapunov functions are used to guarantee the constraints of the vertical displacement not violated, and it can prove the stability of the closed-loop system. Finally, a simulation example for the ASSs is employed to show the feasibility and rationality of the proposed approach. [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]

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]

This paper presents a widely adaptive time-delayed control (WATDC) without imposing any restrictions on allowable control gains to make the most of the advantages of time-delayed control (TDC) and, then, applies the WATDC to robot manipulators. Since the proposed WATDC can provide arbitrarily high gains by using the time-varying bounds of time-delayed estimation errors, it has a room for improving the tracking performance and robustness of the existing stabilizing TDCs that have narrow range of control gains depending on system parameters comprising the inertia of a robot manipulator. Furthermore, the proposed WATDC employs a fast adaptive law to generate wide-ranging and well-timed control gains that depend on the magnitude of tracking errors. In other words, timely adaptation over a wide range of control gains makes the WATDC provide suitable control effort for desirable output responses. Additionally, to suppress unfavorable inherent time-delayed estimation errors arising from one sample delayed measurements, the proposed WATDC works together with a sliding-mode control that has switching gains based on the time-varying bounds of time-delayed estimation errors. In simulations and experiments with robot manipulators, the proposed WATDC has better tracking performance and robustness than the existing TDCs due to a wide range of adaptive control gains and the time-delayed-estimation-error-dependent switching gains. [ABSTRACT FROM AUTHOR]

In this work, the design of a novel the flexure-linked dual-drive H-gantry is first introduced. Compared to conventional stacked X–Y tables, the advantages of this gantry are on decoupled X–Y actuation, better mechanical reliability, larger driving force, and shorter response time. However, after deriving its reduced-order dynamical model, it is found that the precision of the end-effector is significantly affected by the position dependent torque caused by the inter-parallel-axes driving forces on the fixed inertia frame and coupling forces from the pair of flexures. These form barriers that limit precision positioning and tracking performance over a large workspace. Thus, in the following part, a robust integral of signum of error (RISE) control with novel jerk adaptation is proposed on top of the standard proportional-integral-derivative (PID) and feedforward scheme. Compared with some prior RISE schemes with gain adaptation, the proposed scheme yields bounded robust gain in the presence of noisy velocity estimation. Real-time experiments on the actual testbed indicate that both improvement of motion precision and prevention of resonant mode excitation over a large workspace are successfully achieved in the proposed scheme. [ABSTRACT FROM AUTHOR]

The hysteresis behavior of piezoelectric actuators is primarily responsible for the decrease of the precision and performance of the nanopositioning systems. To compensate for hysteresis, an inverse model is proposed in this paper as a segment similarity feedforward compensator. Furthermore, the inverse model is improved by using an adaptive method to eliminate the accumulated error of the similarity compensator. The proposed adaptive similarity compensator has the advantages of simple structure, less storage space, and fast calculation speed, and it is available for most engineering situations. Experiments are carried out with the proposed similarity compensator and adaptive similarity compensator. The experimental results show that the compensator can achieve good results and better performance, especially for high-frequency excitations. [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]

This paper addresses the problem of asymptotic trajectory tracking with prescribed transient behavior for a fully actuated marine surface vessel subject to unmodeled dynamics and external disturbances. A prescribed transient performance constraint is introduced to specify the transient behavior of the output tracking errors. Neural network (NN) approximation and adaptive control techniques are employed to compensate for the unmodeled dynamics. In general, the NN approximation errors always exist in the NN-based adaptive control systems, and typically result in uniformly ultimate boundedness, where the tracking errors cannot be stabilized asymptotically. In order to obtain an asymptotic tracking performance, a robust integral of the sign of the error (RISE) feedback term is introduced in feedback control design to compensate for the NN approximation errors and external disturbances. Consequently, RISE-based adaptive NN controller with compensations of unmodeled dynamics and external disturbances is proposed such that an asymptotic tracking performance is achieved with guaranteeing prescribed transient behavior. The performance of the proposed tracking controller is shown by simulation results. [ABSTRACT FROM AUTHOR]

This article exhibits an output voltage-tracking control algorithm incorporating a self-tuning algorithm and disturbance observers (DOBs) for dc–dc boost converters without tracking error integrators, considering model nonlinearities, parameter variations, and load uncertainties. The proposed algorithm is designed such that it forces the output voltage to track its target trajectory driven by a time-varying low-pass filter (LPF), despite model-plant mismatches. This paper makes two main contributions. First, it constructs the self-tuning algorithm by updating the closed-loop cut-off frequency in the time-varying LPF to improve the transient tracking performance. Second, it embeds first-order DOBs in the proportional-type nonlinear controller to achieve both performance recovery and the offset-free properties. The experimental verifications are carried out to illustrate the effectiveness of the closed-loop system driven by the proposed technique with a 3-kW prototype dc–dc boost converter; the 46% improvement of the tracking performance is observed, compared with the conventional feedback-linearization controller. [ABSTRACT FROM AUTHOR]

An active disturbance rejection control (ADRC) controller using phase-locking loop observer (PLLO) is proposed in this paper to improve the disturbance rejection property of speed control system. First, to simplify the comparison with other controllers, a universal speed controller design method based on disturbance estimation is employed, and a unified expression for different controllers is presented. Then, several common speed controllers are compared with each other, and two PLLO-based ADRC controllers are presented. After that, considering the variations of load inertia, robustness of the proposed ADRC system to inertia variation is analyzed. Finally, to improve the tracking performance for sinusoidal speed reference, an adaptive speed control system using identified inertia is presented. Simulation and experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive method for tuning a proportional-resonance controller for synchronization of the grid-connected inverters is presented. In the proposed approach, the grid frequency is obtained by minimizing the error signal using a frequency-locked loop mechanism that consists of a resonant adaptive filter and a perturbation-based extremum seeking algorithm. Simulations and experimental studies are presented to demonstrate performance of the proposed controller in face of the frequency variations of an emulated grid voltage waveform. The results are compared with conventional nonadaptive methods, which indicate that performance characteristics for voltage/frequency tracking and power factor can be achieved based on the IEEE 1547 standard. [ABSTRACT FROM AUTHOR]

Iterative feedback tuning (IFT) enables the data-based optimization of feedback controller parameters without the plant model and disturbance model. However, the traditional IFT suffers from a generally slow convergence rate and requires multiple experiments in each iteration, which severely limit its applicability for precision motion industry. In this paper, a new framework for IFT with its focus on the practical applicability is synthesized. Specifically, in order to improve the convergence rate, a novel two-loop iterative algorithm of IFT is proposed by introducing a weighted gradient of the performance criterion. This algorithm seeks to directly minimize the performance criterion instead of its linear approximation in each iteration. Furthermore, an unbiased estimate method of the auxiliary variables is developed based on the impulse response experiment. These guarantee the proposed approach high convergence rate with less experiments required per iteration. Comparative simulation and experimental results demonstrate that the proposed approach converges much faster than the traditional IFT, and outperforms the model-based approach in terms of the tracking performance. The ease of implementation and effectiveness makes the proposed approach highly suitable for industrial applications. [ABSTRACT FROM AUTHOR]

This paper studies the practical use of discrete-time direct adaptive control in a high-precision inertially stabilized platform's turbulence isolation system for the purpose of enhancing isolation performance. Under low-frequency and low-velocity environments, the velocity-stabilized loop of the platform shows severe nonlinear characteristic; therefore, its isolation performance is limited. In previous research, we constructed a nonlinear model of the velocity-stabilized loop by using an improved Stribeck friction model, and also designed a feed-forward compensation strategy. Both have obtained outstanding performance during practical experiments. However, errors still exist as disturbance of the unmodeled part and environmental change causes the system's parameter to vary. To solve this, a novel discrete-time direct model reference adaptive control based on nonlinear friction compensation is introduced to the original proportional-integral control system. An improved projection algorithm and a recursive least-square algorithm with fading memory are respectively used to design the adaptive law. By using a turbulence observer to provide a reference signal, both types of controls are applied to the carrier turbulence isolation system. Results of the practical experiments prove that model reference adaptive control can further enhance the system's isolation ability. [ABSTRACT FROM AUTHOR]

This paper designs a simple model reference adaptive control based speed controller (MRAC-SC) for a surface-mounted permanent magnet synchronous motor drive. The proposed adaptive scheme is designed to track a reference model that ensures the desired exponential decay of a controlled speed error trajectory. Also, the proposed MRAC method includes an adaptive compensating control term and a stabilizing feedback control term. The former is proposed to compensate for uncertain model parameters (i.e., inertia, friction, and load torque) and the latter is constructed to asymptotically stabilize the error dynamics. The asymptotic stability of the closed loop is guaranteed with both control terms using the Lyapunov approach. The comparative studies between the proposed MRAC-SC, the nonadaptive model reference speed controller, and the conventional proportional–integral SC are performed to justify a fast transient response, a good tracking possibility, and robustness against the parameter uncertainties of the proposed MRAC technique. [ABSTRACT FROM AUTHOR]

This paper presents a speed estimation scheme by combining a second-order sliding-mode observer (SMO) with the model reference adaptive system (MRAS) in the sensorless-vector-controlled linear induction motor (LIM) drives for medium-low speed maglev applications. A state space-vector model of the LIM considering the dynamic end effects is rearranged in order to be represented by the form of the super-twisting algorithm (STA). Then, an STA-SMO-based stator current observer of the LIM with Popov's hyperstability theory is designed, which is used to replace the reference model of the MRAS-based scheme. Correspondingly, the actual stator current model is regarded as the adaptive model of the MRAS for speed estimation. Meanwhile, because the variation of stator resistance associated with stator temperature may lead to a large estimated error and even system instability, a parallel stator resistance on-line identification scheme is adopted in the proposed speed estimation scheme to reduce the speed error and improve the stability of the sensorless control system. Compared to the speed estimation schemes based on the Luerberger observer with MRAS and the single-manifold SMO, the proposed speed estimation scheme exhibits better estimated performance. The effectiveness and feasibility of the proposed speed estimation scheme have been verified by simulation and hardware-in-the-loop tests. [ABSTRACT FROM AUTHOR]

For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton–Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot. [ABSTRACT FROM AUTHOR]

This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control applications. The HESS consists of a supercapacitor serving as the main power source and a fuel cell serving as the auxiliary power source. First, a Hammerstein-type neural network is proposed to identify the HESS, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Second, a feedforward neural network based on a back-propagation training algorithm is designed to formulate the proportional-integral-derivative (PID)-type neural network, which is used for the adaptive control of the HESS. Meanwhile, a dynamic antiwindup signal is designed to solve the operational constraint of the HESS. Then, an appropriate power reference signal for the HESS can be generated. Third, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

This paper presents a practical adaptive time-delay control scheme (ATDC) and then applies it to robot manipulators. The proposed ATDC uses a time-delayed signal to cancel complex nonlinear dynamics and disturbances, and hence to provide a simple structure. The adaptive law of the proposed ATDC is developed to adjust the control gains appropriately while in operation and then to achieve good tracking performance with tolerant fluctuation and fast convergence speed. Such adaptive control gains are shown to have an effect of adding an adaptive auxiliary control to a conventional time-delay control (TDC). Moreover, the proposed ATDC includes a typical sliding-mode control as a second auxiliary control to suppress the time-delay estimation (TDE) errors caused by the time-delayed signal so that robust performance can be achieved. It is shown by the bounded input bounded output stability theory and a Lyapunov approach that the TDE errors are bounded and the tracking errors are uniformly ultimately bounded, respectively. The practical effectiveness of the proposed ATDC, or the synergistic effect of two auxiliary controls, is illustrated in simulations and experiments with robot manipulators. [ABSTRACT FROM PUBLISHER]

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]

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]

The proposed paper is mainly focused on achieving stable operation of microgrid having reconfigurable architecture leading to huge variation in network parameters. The variation in network parameters may not be easily handled by conventional droop controllers, which are mainly designed while assuming fixed network configuration. However, these assumptions become invalid for a microgrid having small mesh network with reconfigurable structure. Therefore, it is most important for a microgrid to remain stable not only during various changes in droop characteristics but also during dynamic topological changes. The $L_1$ controllers are well known for their robustness under wide parametric variations. Therefore, a novel L_{1} adaptive controller has been designed to achieve enhanced stability of microgrid under the varying network configuration and variable droop controller characteristics. The proposed method is simulated in MATLAB/ Simulink and verified on field programmable gate array (FPGA)-based real world hardware platform. [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]

This paper proposes a new adaptive patient-cooperative control strategy for improving the effectiveness and safety of robot-assisted ankle rehabilitation. This control strategy has been developed and implemented on a compliant ankle rehabilitation robot (CARR). The CARR is actuated by four Festo Fluidic muscles located to the calf in parallel, has three rotational degrees of freedom. The control scheme consists of a position controller implemented in joint space and a high-level admittance controller in task space. The admittance controller adaptively modifies the predefined trajectory based on real-time ankle measurement, which enhances the training safety of the robot. Experiments were carried out using different modes to validate the proposed control strategy on the CARR. Three training modes include: 1) a passive mode using a joint-space position controller, 2) a patient–robot cooperative mode using a fixed-parameter admittance controller, and 3) a cooperative mode using a variable-parameter admittance controller. Results demonstrate satisfactory trajectory tracking accuracy, even when externally disturbed, with a maximum normalized root mean square deviation less than 5.4%. These experimental findings suggest the potential of this new patient-cooperative control strategy as a safe and engaging control solution for rehabilitation robots. [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]

The advancements in dc microgrids and the increase in distributed generation systems have led to a new trend toward the coexistence of multiple power converters from different sources (renewable, storage, etc.) supplying a variety of loads of different natures in a weak network. The loads can behave as passive loads (resistances) or be implemented by tightly regulated power converters, leading to constant power load (CPL) behavior. The CPLs present a characteristic negative incremental resistance that can alter the response of the system, even causing instability. In this work, a novel embedded technique based on a digital lock-in amplifier is proposed that enables the real-time detection of the dynamic impedance present in a power converter. The proposed technique uses a very efficient algorithm, along with standard sensors available in the converter, to measure the magnitude and phase of the dynamic load, and uses this information to improve the performance of the converter. A sample application of the proposed technique in an adaptive control system is described. Although the total output power of the converter is independent of the nature of the load, the converter's dynamic response is not. The interaction of the CPL, passive load, and control loop will determine not only the stability but also the transient response. The proposed instrument allows the incremental load of the converter to be accurately measured while reducing the complexity and sensor requirements, and improving the performance of the controller. Simulations of the proposed technique are presented to illustrate its behavior. Experimental results for different kinds of loads are presented to validate the proposed strategy. [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]

The issue of couplings makes the attitude control design for hypersonic vehicles (HSVs) challenging. This paper focuses on the characterization of coupling and its application in the control system synthesis. A novel coupling characterization index is introduced via the Lyapunov stability theory to indicate whether a coupling harms or benefits the system. The control scheme is designed to eliminate the detrimental couplings while keeping the beneficial couplings. The novelty of this paper is that the system couplings are explicitly utilized in the controller design leading to better dynamic performance. The robustness of the proposed controller is enhanced by using a disturbance observer. The effectiveness of the proposed scheme is validated by simulations in the HSV attitude system. [ABSTRACT FROM AUTHOR]

In this paper, a new active current balancing method for a light-emitting diode (LED) driver with multichannel outputs is proposed. In the proposed structure, a low-power nonisolated dc–dc converter in series with each LED string is used to regulate the LED string current. Different from conventional active current balancing methods, in the proposed method, the nonisolated dc–dc converters only process a small portion of the total output power. Thus, the cost and size can be greatly reduced. An adaptive bus voltage control method is also proposed to minimize the output power of the nonisolated dc–dc converters. The proposed method features good modularity, expandability, and individual current control capability. Performance of the proposed method is validated by the experimental results from an 80-W prototype with four channel outputs. [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]

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 proposes an approximation-free adaptive tracking error constraint control scheme for unknown Lagrangian systems. A transformed filtered tracking error surface has been constructed to guarantee the transient and steady-state performance of the position control of the Lagrangian systems combined with adaptive estimation laws for unknown upper bounds of the parameters of the Lagrangian systems. To obtain fast error convergence, a finite-time control has been adopted in the proposed controller. In addition, the proposed controller is effective for the constrained positioning of Lagrangian mechanical systems with unknown deadzone input nonlinearity, without requiring nonlinear approximations or identification processes for unknown parameters of dynamics and deadzone. The effectiveness of the proposed constraint control scheme has been proven by simulation and experimental evaluations for an XY table and an articulated manipulator system in the presence of unknown deadzone. [ABSTRACT FROM AUTHOR]

A model-free robust adaptive controller is proposed for control of humanoid robots with flexible joints. The proposed controller uses a time-delay estimation technique to estimate and cancel nonlinear terms in robot dynamics including disturbance torques due to the joint flexibility, and assigns desired dynamics specified by a sliding variable. A gain-adaptation law is developed to dynamically update the gain of the proposed controller using the magnitude of the sliding variable and the gain itself. The gain-adaptation law uses a leakage term to prevent overestimation of the gain value, and offers stable and chattering-free control action. The effectiveness of the proposed adaptive controller is experimentally verified on a humanoid robot equipped with flexible joints. Tracking performances of the autotuned adaptive gain are better than those of the manually tuned constant gains. The proposed control algorithm is model-free, adaptive, robust, and highly accurate. [ABSTRACT FROM AUTHOR]

It is a common hypothesis in the field of robot-assisted gait rehabilitation that the active involvement and voluntary participation of neurologically impaired subjects in the robotic gait training process may enhance the outcomes of such therapy. An adaptive seamless assist-as-needed (AAN) control scheme was developed for the robotic gait training. The AAN control scheme learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. The overall AAN control architecture works on the basis of a robust adaptive control approach. The performance of seamless AAN control scheme was evaluated during treadmill training with a compliant robotic orthosis having six degrees of freedom. Two experiments, namely, trajectory following experiment and the AAN experiment were carried out to evaluate the performance of seamless adaptive AAN control scheme. It was found that the robotic orthosis is capable of guiding the subjects’ limbs on reference trajectories during the trajectory following experiment. Also, a variation in robotic assistance was recorded during the AAN experiment based on the voluntary participation of human subjects. This work is an advance on the current state of the art in the compliant actuation of robotic gait rehabilitation orthoses in the context of seamless AAN gait training. [ABSTRACT FROM AUTHOR]

This paper aims to study on feedback control for a networked system with both uncertain delays and, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1) delays and packet dropouts are accurately detected online by a network problem detector; 2) a so-called proportional-integral-based neural network grey model (PINNGM) is developed in a general form to be capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3) using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4) based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability, and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions. Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data. [ABSTRACT FROM AUTHOR]

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]

In this paper, an electrolytic capacitor-less ac–dc light-emitting diode (LED) driver, consisting of a power factor correction (PFC) converter and a buck/boost bidirectional converter, is investigated. The buck/boost bidirectional converter is connected in parallel with the output of the PFC converter and serves to absorb the second harmonic current in the PFC output current, leaving only a dc component to drive the LEDs. This paper proposes an adaptive voltage control scheme for the storage capacitor in the buck/boost bidirectional converter to make the storage capacitor voltage adaptively decrease as the load becomes lighter. Hence, the power losses of the buck/boost bidirectional converter could be reduced at light load. Experimental results are provided to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

In this paper, an improved winding current control scheme for switched-reluctance motor drive is developed and evaluated. The current feedback controller (CFBC) is augmented with an observed back electromotive force (EMF) current feedforward controller (CFFC) and a current tracking error adapted dynamic commutation shift controller (DCSC). In the proposed DCSC, the rising winding current tracking errors of all phases are extracted, synthesized, and processed to generate the suitable advanced commutation shift angle. The effects of motor back EMF and nonlinear winding inductance can be reduced to yield improved winding current tracking waveforms under a wide operation range. The gradual improvements in current responses and efficiencies by augmenting the CFBC with the CFFC and then the DCSC are verified experimentally. Then, the robust speed control scheme is properly designed to achieve good acceleration/deceleration, reversible running, and dynamic characteristics. [ABSTRACT FROM AUTHOR]

This paper addresses the adaptive control of teleoperation system with actuator saturation. To unify the study of actuator saturation, passive/nonpassive external forces, asymmetric time-varying delays, and unknown dynamics in the same framework, a novel switched control scheme is developed, where a special switched filter is investigated. In the saturation scenario, the designed controller consists of a generalized controller and a nonlinear saturation function. By placing the nonlinear saturation function on the outside of the generalized controller, and further jointing with the design of switched filter, the designed generalized controller needs not consider the actuator saturation. Specifically, it is designed to be in the form of proportional plus damping injection plus switched filter. Then, the complete closed-loop system is modeled as a special switched system. It is finally established to be state-independent input-to-output stable, where the ultimate boundedness of the tracking error is ensured for any bounded exogenous forces, which is demonstrated by the experimental studies. [ABSTRACT FROM AUTHOR]