This paper investigates the adaptive tracking control problem for uncertain nonlinear systems using self-organizing deep neural networks (SODNNs). Conventional DNN-based control approaches typically employ fixed depth architectures with multiple hidden-layers to model uncertain nonlinear dynamics. Such fixed structures often face an inherent trade-off between approximation accuracy and computational efficiency, resulting in either excessive computational burden or insufficient representational capability. To address these issues, an event-triggered self-organizing mechanism is proposed, which enables the DNN to dynamically adjust its network depth by adding or removing hidden-layers when prescribed triggering conditions are violated. In this way, the network structure is dynamically adapted to the evolving system dynamics, achieving an effective balance between approximation precision and computational burden. Furthermore, an adaptive learning rate strategy is developed, in which the learning rates of individual layers are adjusted online based on the state tracking error and the norm of the corresponding weight gradients. This mechanism enhances approximation efficiency, control performance and robustness against uncertainties. Rigorous Lyapunov-based analysis is provided to guarantee that all closed-loop signals are ultimately bounded under the proposed control framework. The effectiveness and practical feasibility of the proposed method are validated through numerical simulations and real-time experiments conducted on a Franka Emika Panda robotic manipulator.

This paper presents an adaptive command filtered control scheme using composite auxiliary signals for a class of uncertain strict-feedback nonlinear systems subject to input saturation. To explicitly constrain the filtered errors, barrier functions are incorporated into the command filtering loop, effectively mitigating their adverse impact on system stability and alleviating the “explosion of complexity" phenomenon inherent in backstepping designs. Furthermore, by constructing a composite auxiliary system, the proposed method achieves a unified compensation mechanism for both bounded filtered errors and input saturation effects. Compared with existing approaches, the proposed strategy simultaneously addresses filtered error and input saturation issues without relying on smooth saturation approximations, thereby avoiding approximation-induced errors and enhancing overall control performance. The effectiveness and practical applicability of the method are validated through numerical simulations and real-time experiments conducted on a Franka Emika Panda robotic arm.

Large-scale assembly production suffers from inefficient resource allocation, difficulties in coordinating interests across workstations, and severe congestion issues due to massive scale, complex tasks, and fluctuating constraints. To address these challenges, this study proposes a resource-efficient scheduling decision-making method based on a mixture pulsation model (DMMPM). The main contributions are as follows: 1) quantitative criteria for pulsation rhythm (takt-time) consistency in assembly production are defined, and a mixture equilibrium model for multiworkstation collaborative scheduling is developed, to integrate production rhythm alignment with workforce optimization within a coalition-driven profit maximization framework; 2) a spatiotemporally constrained task-allocation method driven by theoretical allocation batches is designed, balancing interstation resource demand conflicts and production rhythm synchronization requirements; and 3) a bi-level "scheduling-collaboration" architecture is proposed, where scheduling agents generate workstation-level strategies and collaboration agents coordinate cross-workstation coalition strategies through profit distribution, thereby enabling the efficient integration of decentralized decision-making and global optimization. The mathematical model is validated using ILOG CPLEX. Compared with conventional approaches, DMMPM significantly reduces the integrated scheduling cost and demonstrates superior decision-making capability and improved control of pulsation rhythm in large-scale aircraft manufacturing scenarios.

This article investigates the distributed approximate optimal control problem for pursuit-evasion differential games (PEDGs) of multiagent systems (MASs). Initially, interactions between pursuer agents and the evader agents are formulated using a divide-and-conquer algebraic graph approach, where all agents desire to maintain cohesion with their teammates. Subsequently, a state event-triggered mechanism (ETM) is introduced to conserve communication resources. Meanwhile, a polymeric hierarchical sliding mode surface (HSMS) incorporating local neighbor errors is constructed such that the system response rate is improved. To enhance team coordination, a novel dynamic target allocation algorithm is designed to execute the rational allocation among pursuers. Furthermore, based on the adaptive dynamic programming (ADP) with a single-critic neural network (NN) architecture, the HSMS-based event-triggered optimal control policies are further designed via solving the coupling Hamilton-Jacobi-Bellman (HJB) equations. Finally, a simulation conducted in the representative two-pursuer-two-evader scenario is presented to validate the effectiveness of the proposed control scheme.

This study addresses the safe consensus tracking issue for a specific category of multiagent systems (MASs) featuring a static directed communication graph. Each follower agent is subject to external disturbances and governed by unknown pure-feedback nonaffine dynamics. To facilitate the back-stepping approach in nonaffine systems, the mean value theorem (MVT) is employed. Additionally, dynamic surface control (DSC) is implemented to mitigate the intricacies typically encountered in back-stepping frameworks. For the approximation of the unknown nonlinearities, radial basis function neural networks (NNs) are utilized. Integrating these methodologies with principles from graph theory and barrier Lyapunov functions (BLFs), we propose a tailored neuro-adaptive distributed control scheme. The objective of this scheme is to ensure that followers can accurately track the leader’s path while maintaining the globally uniformly bounded (GUB) property of all system signals within the closed loop. Comparative simulation results demonstrate the effectiveness and superiority of the proposed control method.

This article studies the optimal cluster synchronization (OCS) problem for multiagent systems (MASs) with the unknown perturbation under a multicluster game (MCG) framework. To estimate the unknown perturbation, a novel perturbation observer nested with a parameter adaptive law is first designed. Subsequently, a coupling performance index function relevant to the synchronization error and the control policy with the quadratic form is constructed. By utilizing the distributed adaptive dynamic programming (ADP) technology with a single-critic architecture, the optimal control policy is designed by solving the Hamilton–Jacobi–Bellman (HJB) equation associated with the coupling performance index function. Meanwhile, an adaptive OCS control policy with a single-critic neural network (NN) updating law is further proposed, and it is proven that the designed adaptive OCS control policy constitutes the generalized Nash equilibrium (GNE) point of the MCG. Based on the Lyapunov extension theorem, all signals of the closed-loop MASs are ensured to be bounded. Finally, a simulation example is presented to validate the effectiveness of the proposed adaptive OCS control method.

This paper investigates high-order nonlinear multi-agent systems with state constraints and input saturation. A novel control scheme incorporating Neural Networks and Barrier Lyapunov Functions is designed to achieve adaptive fixed-time consensus control. This innovative scheme effectively addresses the complexity explosion problem typical in traditional controller designs while ensuring that the closed-loop system remains within its constraints. During the design process, a first-order sliding mode differentiator was introduced, and compensations were made for filter errors to ensure stability and consistency within a fixed-time. Additionally, experiments using Matlab numerical simulations and the StarSim semi-physical simulation platform confirm that the proposed control scheme significantly surpasses traditional methods in efficiency and accuracy, validating its effectiveness and practicality for solving the consensus problem in high-order nonlinear multi-agent systems.

In this study, an observer-based output feedback bounded control strategy is investigated for states unmeasurable nonlinear systems subjected to actuator hysteresis and unidirectional input constraints. The inevitable complex actuators restrictions and hysteresis that exist in practice are considered and transformed by applying a new model transformation operations. To cope with the challenge introduced by the coupling of control inputs and an actuator nonlinear term, a new fuzzy state observer is constructed by introducing a signal filter so that the unmeasurable states and generalized uncertainties, including the hysteresis term, disturbances, and uncertainties, can be estimated synchronously. An output feedback bounded control strategy is proposed that achieves high-precision positioning using a performance-prespecified dynamic surface controller and guarantees that the control signal is bounded within the input constraints by constructing the logarithm Lyapunov functions. The stability of the closed-loop system is proven. Furthermore, the investigated scheme is validated on the piezoelectric micropositioning stage, which is a mechanical system with unknown actuator hysteresis and unidirectional input constraints. Note to Practitioners—This study is motivated by the problem of bounded motion control for smart material-driven micropositioning stages with hysteresis and unidirectional input constraints. The development of micro/nano technology places higher requirements on micro/nanopositioning control. As one of the most commonly used smart material-driven micropositioning stages, piezoelectric micropositioning stage is widely used in mechanical engineering, aerospace technology, bioengineering and other fields. However, influenced by the inherent properties of materials, piezoelectric actuators have complex hysteresis nonlinearity and are subject to unidirectional input constraints, which impair the positioning accuracy and the equipment operation safety. Existing control methods for hysteretic systems are either limited in control performance due to the hysteresis modeling accuracy or difficult to be applied to practical systems with only measurable outputs, and all of existing methods ignore the effect of unidirectional input constraints on the control performance. This study proposes an output feedback bounded control strategy that eliminates the negative effects of hysteresis without constructing an inverse hysteresis model, and realizes high-precision positioning control with prespecified performance, where the bounds of control input is known a priori and satisfy the input constraints.

In this article, a distributed optimal security control method is proposed for multiagent systems (MASs) containing multiple attackers and defenders within a differential adversarial game framework. Initially, the control inputs of the defenders’ systems are considered to suffer from denial-of-service (DoS) attacks from the attackers, then the coupled performance index functions associated with the state errors are constructed. By using the distributed adaptive dynamic programming (ADP) technology, a modified radial basis function neural network (NN) is implemented such that the coupled performance index functions are approximately identified, and by solving the coupled Hamilton-Jacobi-Bellman (HJB) equation, an ADP-based optimal security control policy with a single-critic NN updating law is further proposed. Meanwhile, it is proven that the proposed optimal security control policy constitutes the Nash equilibrium point of the differential adversarial game. Finally, a simulation example is given to validate the effectiveness of the proposed distributed optimal security control method.

This study designs an adaptive consensus output feedback quantized controller for high-order nonlinear multi-agent systems with full-state and tracking error constraints. The key features of this control strategy include: 1) A finite-time prescribed performance barrier function with online parameter adjustment, combined with dynamic surface technology, which simplifies the controller design and ensures all agent states remain within predefined limits; 2) An error threshold function is used to meet tracking error constraints, improving response speed and maintaining constraint compliance; 3) The use of quantizers in the system reduces communication load during signal transmission. Stability analysis shows that all signals in the closed-loop system remain consistently and semi-globally uniformly bounded. Additionally, the effectiveness of the control strategy is validated through experiments on a multi-agent cooperative control platform.

Determining the neural network (NN) approximation sets for adaptive control of strict-feedback uncertain systems has posed a persistent challenge. This article proposes a novel and constructive solution that incorporates signal substitution technique, barrier functions (BFs), and backstepping approach. By applying the signal substitution technique, all system states are transformed into state error variables, facilitating the approximation of unknown system functions through NNs. The use of BFs subsequently allows for the restriction of state errors, enabling the calculation of exact bounds for the NN weight estimators. This process reveals the determination of the approximation sets of NN in advance. Illustrative examples are conducted to validate the effectiveness of the proposed approach.

This article deals with the observer-based control problem of networked periodic piecewise systems under encoding-decoding frameworks. An encoder with a uniform quantizer, which can compress and encrypt data, is provided to process the measurements from the sensors. The processed data is transmitted over the network to the decoder to recover the original data and then to the remote control station, thereby reducing the communication burden and ensuring data security. Then, by constructing the periodic Lyapunov function with linear interpolation terms, exploiting an effective technique-singular value decomposition-sufficient conditions with linear matrix inequality (LMI) constraints for selecting the observer and controller parameters are derived to achieve the exponentially ultimate boundedness of closed-loop systems. Moreover, to eliminate extra steady-state errors caused by encoding-decoding mechanisms (EDMs), a dynamic quantization factor that can make the asymptotic upper bound tend to zero is designed. Finally, numerical examples are provided to illustrate the effectiveness of the derived theoretical results.

The trajectory tracking control for the photothermal-driven liquid crystal elastomer (LCE) actuator presents a significant challenge due to its hysteresis nonlinear characteristic and its inherent complex deformation mechanism. To address this challenge, this article proposes a trajectory tracking control method for the LCE actuator utilizing a nonlinear compensator and a state observer. The proposed control is a multistep control, which includes temperature control from the input voltage to the LCE temperature and displacement control from the LCE temperature to the LCE displacement. In the proposed method, we design a non-Lipschitz continuous state-feedback controller to realize finite-time convergence control of the temperature. As for the displacement control, we design a state observer to estimate the change rate of the LCE displacement. Meanwhile, a nonlinear inverse compensator is designed to compensate for the hysteresis nonlinearity of the LCE dynamics, which simplifies the complex nonlinear control problem into a linear control problem. Hence, the pole placement method can be utilized to design a trajectory tracking controller to achieve the control objective. The proposed control method is validated by tracking control experiments with different target trajectories.

Nonnegative matrix factorization (NMF) is a widely recognized approach for data representation. When it comes to clustering, NMF fails to handle data points located in complex geometries, as each sample cluster is represented by a centroid. In this article, a novel multicentroid-based clustering method called graph-based multicentroid NMF (MCNMF) is proposed. Because the method constructs the neighborhood connection graph between data points and centroids, each data point is represented by adjacent centroids, which preserves the local geometric structure. Second, because the method constructs an undirected connected graph with centroids as nodes, in which the centroids are divided into different centroid clusters, a novel data clustering method based on MCNMF is proposed. In addition, the membership index matrix is reconstructed based on the obtained centroid clusters, which solves the problem of membership identification of the final sample. Extensive experiments conducted on synthetic datasets and real benchmark datasets illustrate the effectiveness of the proposed MCNMF method. Compared with single-centroid-based methods, the MCNMF can obtain the best experimental results.

Taking into consideration the issue of the quadrotor unmanned aircraft robots (UARs) actuated by motors with hysteresis input, this research presents an adaptive dynamic implicit inverse control technique based on neural networks to achieve the desired trajectories. The following summarizes the primary technologies: 1) the hysteresis effect in UARs has been considered and eliminated by the proposed implicit inverse algorithms, which means a searching method for acquiring the real control signals is designed resulting in selecting to avoid constructing the hysteresis direct inverse model; 2) precise tracking is accomplished by designing an adaptive dynamic surface control (DSC) technology with enhanced state observer under the constraint that only the position data is available. In the meanwhile, the$L_{\infty }$performance can be obtained by selecting the suitable parameters; and 3) the underactuated Drone platform has been constructed as well as the control results have implemented to confirm that the successful application of the proposed implicit inverse control algorithms.

This article investigates the leader-follower consensus for nonlinear multiagent systems (MASs) and proposes an adaptive dynamic programming (ADP)-based hierarchical Stackelberg-Nash optimal game control method. Initially, a coupled performance index function associated with consensus errors is constructed. As the positive-definite function with the quadratic form is allocated to the constructed consensus errors-based performance index function, the original system stabilization problem is converted into the issue of seeking an optimal control strategy profile for the leader and followers. Under the hierarchical Stackelberg-Nash differential game framework, the optimal control strategies are derived in sequence and further proved to compose the equilibrium points of Stackelberg-Nash differential games. Afterward, based on the ADP technique, a modified single-critic neural network (NN) is implemented and the coupled Hamilton-Jacobi–Bellman (HJB) equation is approximately identified. Under the proposed control scheme, the leader-follower consensus of the considered MAS can be achieved while consuming less control cost. Meanwhile, all signals of the MAS are ensured to be uniformly ultimately bounded. Finally, a numerical simulation and an application to the electrode regulating system of the three-phase electric arc furnace are given to verify the effectiveness of the proposed control method.

In this paper, we present an adaptive control method for a conically shaped dielectric elastomer actuator (CSDEA) with different loads to achieve its tracking control objective. Firstly, a dynamic model of the CSDEA is constructed to describe its asymmetrical, rate-dependent hysteresis behavior and creep behavior simultaneously. Then, an offline parameter identification method based on the nonlinear-least-squares algorithm is presented to obtain the nominal values of the model parameters of the CSDEA corresponding to the load of 200 (g). The obtained values are regarded as the initial values of the adaptive online parameter identification. Next, an adaptive online parameter identification method (AOPIM) based on the least-mean-square algorithm is proposed, which can dynamically calculate and update the model parameter values to cope with the load changing of the CSDEA. Lastly, based on two kinds of analytical inverses of the dynamic model and the proposed AOPIM, two adaptive inverse compensators are respectively designed to realize the tracking control of the CSDEA. The control experiments with different desired trajectories and different loads are implemented to demonstrate the effectiveness of the presented adaptive control method. Since the root-mean-square errors of the results of all control experiments are lower than 2.7%, the presented method is remarkable from the perspective of the practical application.Note to Practitioners—The dielectric elastomer material has beneficial properties of high energy density, low mass density, large deformation, fast response and good biological compatibility. Thus, the soft actuator based on the dielectric elastomer material (SADE) has shown great potentials for being used in soft robots. Nevertheless, the SADE has the complex nonlinear behaviors, which brings a big challenge for its precision control. To deal with this issue, some approaches have been presented in previous literatures. However, the load of the SADE is usually fixed in these studies. This paper focuses on the tracking control of the SADE with different loads. To this end, a dynamic model of the CSDEA is constructed, whose parameter values are dynamically calculated and updated to cope with the load changing of the CSDEA. Through calculating the analytical inverse of the dynamic model, two adaptive inverse compensators are developed. The control experimental results demonstrate that the presented adaptive control method is effective. Considering that the load of the SADE is usually diverse in practical applications, this study pays the way for the application of the SADE.

In this study, the data-driven sliding-mode iterative learning tracking control problem of a piezoelectric-actuated micro-positioning (PAMP) stage is investigated. To improve the convergence performance of the data-driven sliding mode iterative learning control (DDSILC) method, a novel iteration-dependent parameter learning mechanism is proposed. Subsequently, an enhanced DDSILC (E-DDSILC) scheme is constructed. The novel parameter-learning mechanism is designed such that the tracking error in time-varying systems can converge to zero in the time domain at the final iteration, and to significantly improve the transient performance of the system. Additionally, the effect of control parameters on the convergence performance is analyzed, which enables the parameters to be adjusted reasonably and efficiently. Several comparison experiments are conducted on the PAMP stage to verify the effectiveness of the proposed control approachNote to Practitioners—Owing to the gradual industrial development toward the high-end manufacturing, many products, such as vascular robots and precision chips, have reached the micro/nano level of accuracy. The piezoelectric-actuated micro-positioning (PAMP) stage has been widely used in high-precision fields, such as fluorescence microscopy, nanoimprint lithography, and laser interferometry, owing to its fast response and ability to generate micro-nano displacements. However, because of the hysteresis and other nonlinear characteristics existed of the PAMP stage, advanced control algorithms are required to address the nonlinearity and satisfy the requirements of high-precision control. When the PAMP stage is used for precision manipulation tasks, such as nanolithography and micro/nanoimaging, the advanced control algorithms present the following restrictions: high dependence on offline models and the necessity to select parameters via trial-and-error method. These limitations render it difficult to implement control approaches. Hence, this study proposes an E-DDSILC scheme, which adopts the dynamic linearization to obtain the nonlinear information of the system. Unlike the conventional DDSILC method, the proposed scheme with enhanced iterative learning mechanism guarantees error convergence in the time domain. Furthermore, the effects of the main parameters on the transient and steady-state performances are investigated. As an offline model is not required for the proposed method and the effects of the parameters are explicit, the operation time is reduced and the feasibility of the controller in practical implementation is guaranteed.

This paper concerns with the fault estimation problem for a class of unmanned marine vehicles with sensor faults and non-differentiable actuator faults. Firstly, the presented unmanned marine vehicles are modeled as a class of continuous-time switched systems. In order to resolve the non-differentiable actuator faults, two kinds of learning observer methods are proposed to allow the augmented systems independent of the time-derivative of actuator faults. The first one is a kind of timeline-based learning observer, which produces the satisfactory estimation results by using the estimated information from the previous moment and the measured output estimation error information. The second one is an iterative learning observer, where the actuator faults are accurately estimated by the iterative process. The monotonic convergence of the iterative process is guaranteed based on the derived linear matrix inequality conditions. Under arbitrary switching signals, the simultaneous estimations of states, faults and disturbances can be achieved via the proposed observer design approaches. Finally, simulation results are provided to illustrate the effectiveness of the developed two kinds of learning observers.Note to Practitioners—The mass of unmanned marine vehicles (MVs) is switched back and forth as the unmanned MV performs the tasks, such as loading, dispatching and retrieving detectors. Hence, in order to better describe the dynamic behaviour, the unmanned MVs are described as the mass-switched systems. In general, any actual systems with switching properties can be characterised as the switched systems. At present, the fault estimation problem of the mass-switched unmanned MVs has not yet been investigated to date. This paper presents a theoretical study. Existing approaches to fault estimation of switched systems generally require differentiable actuator faults, which may be difficult to guarantee. In order to relieve this restriction, this paper proposes the new learning observer-based estimation method for switched linear systems. Based on the physical data of the unmanned MV, simulation results show that the proposed method is feasible.

Magnetic shape memory alloy-based actuator (MSMA-BA) is constructed based on the strain mechanism of MSMA material and the magnetic effect of electric current. It can generate macroscopic deformation with micro-nano scale resolution. However, the rate-dependent and load-dependent hysteresis characteristics in MSMA-BA will reduce the positioning accuracy and hinder its applications. In this study, a long short-term memory (LSTM)-based U model with exogenous inputs is proposed to describe the complex dynamic hysteresis characteristics. Then, an LSTM-based iterative learning control (ILC) scheme is proposed to realize the reference trajectory tracking control of the MSMA-BA. Additionally, a dynamic expansion compression factor (DECF) is introduced in the controller to accelerate the convergence speed of system. The convergence of the proposed LSTM-based ILC scheme is analyzed with the consideration of state uncertainty, output disturbance, and the initial state error. It will promote the further applications of ILC in practical situations. Experiments are carried out on MSMA-BA to validate the effectiveness of the proposed method. The experimental results indicate that the proposed modeling and control methods exhibit excellent performance.

This work addresses the state estimation problem for recurrent neural networks over capacity-constrained communication channels. The intermittent transmission protocol is used to reduce the communication load, where a stochastic variable with a given distribution is used to describe the transmission interval. A corresponding transmission interval-dependent estimator is designed, and an estimation error system based on it is also derived, whose mean-square stability is proved by constructing an interval-dependent function. By analyzing the performance in each transmission interval, sufficient conditions of the mean-square stability and the strict (Q,S,R) - γ -dissipativity are established for the estimation error system. Finally, the correctness and the superiority of the developed result are illustrated by a numerical example.

This article proposes fuzzy-logic systems (FLSs)-based decentralized adaptive implicit inverse control scheme for a class of large-scale nonlinear systems with time delays and multihysteretic loops. Our novel algorithms feature hysteretic implicit inverse compensators designed to effectively mitigate multihysteretic loops in large-scale systems. In this article, hysteretic implicit inverse compensators can replace the traditional hysteretic inverse models, which are exceedingly difficult to construct, and no longer necessary. The authors provide three contributions: 1) a searching mechanism to obtain the approximate value of the practical input signal from the so-called hysteretic temporary control law; 2) the arbitrarily small$\mathit{L}_{\infty}$norm of the tracking error attained by utilizing the proposed initializing technique, which applies the combination of FLSs and a finite covering lemma to deal with time delays; and 3) the construction of a triple-axis giant magnetostrictive motion control platform, which validates the effectiveness of the proposed control scheme and algorithms.

This article investigates the fault-tolerant coordinated tracking control problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults and communication delays. By supplementing the commonly used Gaussian functions in the fuzzy neural networks (FNNs) with sine-cosine functions and constructing two kinds of recurrent loops within the FNN architecture, double recurrent perturbation FNNs are cleverly designed to learn the unknown terms containing faults and uncertainties. Then, adaptive laws are designed for double recurrent perturbation FNNs. Moreover, by assimilating fractional-order calculus into the sliding-mode surfaces and the control signals, refined transient-state and steady-state adjustment performances can be obtained. It is shown by Lyapunov stability analysis that all fixed-wing UAVs can coordinately track their desired trajectories and the tracking errors are uniformly ultimately bounded. Comparative simulation results are provided to show the effectiveness of the proposed control strategy.

This article presents a distributed fault-tolerant control (FTC) scheme for nonlinear fractional-order (FO) multiagent systems (MASs) with the order lying in (0, 1], such that the proposed control architecture can be directly applied to both FO and integer-order (IO) systems without any modifications. To handle the unexpected actuator faults encountered by the FO MASs, a hierarchical FTC mechanism is developed for each system by constructing an event-triggered distributed FO estimator at the upper layer to estimate the leader system's output via conditionally triggered neighboring information, and an FTC unit at the lower layer to counteract the loss-of-effectiveness faults via Nussbaum function with FO criteria. To further address the unknown nonlinear functions involving bias faults and periodic disturbances, the Fourier series expansion technique is used to construct the input variables of fuzzy neural networks (FNNs), such that the FNNs with dynamically adjusted weight matrices, centers, and widths can be developed for each FO system to act as the learning module. It is shown by FO Lyapunov stability analysis that all follower systems can track the leader system against faults and periodic disturbances. Simulation results on FO systems and hardware-in-the-loop experiment results on IO fixed-wing unmanned aerial vehicles show the extensive feasibility of the developed scheme.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

The dielectric electro-active polymer (DEAP) actuator, which features large deformation and fast response, has been widely utilized in soft robots. However, the complex hysteresis, creep and quadratic input nonlinearity of the DEAP actuator pose great difficulties and challenges for its controller design. In this article, an inverse compensation-based global fast terminal integral sliding mode control scheme with lumped uncertainty fuzzy estimation is presented for the high-precision tracking control of the DEAP actuator. On the basis of the analytical inverse of a modified Prandtl–Ishlinskii model and a square root module, an inverse compensator is developed to compensate for the hysteresis and the quadratic input nonlinearity of the DEAP actuator. For the system after inverse compensating, a K-observer is adopted to estimate the immeasurable state, and a fuzzy system is used to estimate the lumped uncertainty. Then, a global fast terminal integral sliding mode controller is devised and cascaded in series with the inverse compensator to form a closed-loop control system. The stability and the finite time convergence of the closed-loop control system are proven in theory. Finally, the practical application feasibility and the excellent performance of the developed control scheme are demonstrated via experiments on a testbench for the DEAP actuator.

The inherent hysteresis property of Ni-Mn-Ga alloy material is the main reason that affects the positioning accuracy of Ni-Mn-Ga alloy-based actuator. This study proposes an iterative learning control based on feedforward neural network (ILCBFNN) to eliminate the effect of hysteresis on actuator positioning accuracy. In addition, the convergence analysis problem of the system that is subject to system irreversibility, local Lipschitz nonlinearity, and iteration-dependent uncertainty, is investigated. Specifically, ILC is combined with the FNN to improve the adaptability and performance of the ILC. The global Lipschitz-like condition is established using the principles of mathematical induction and contraction mapping. Then, the convergence of the ILC process is analyzed by studying the variation of tracking error along the iteration axis. The obtained convergence condition ensures that the tracking error converges to a small region proportional to the initial state error. Experimental results verify the feasibility of proposed ILCBFNN method.

A dielectric elastomer smart actuator (DESA) has shown great potentials in soft robot applications. The control of the DESA is one of key issues for soft robots. However, the precise control of the DESA is a challenge work, especially when tracking high-frequency trajectories and guaranteeing a certain robustness. To this end, this article presents a robust tracking control method for a DESA to track various high-frequency trajectories. First, to characterize the complex nonlinear properties (including the quadratic input, asymmetric and rate-dependent hysteresis, as well as creep) of the DESA, its dynamic model is built by combining a square function module, a modified Prandtl–Ishlinskii (P–I) hysteresis model, and a linear system. Subsequently, by combining the inverse of the modified P–I hysteresis model with a square root function module, the quadratic input and asymmetric hysteresis properties of the DESA are compensated to reduce the tracking control error. Since model uncertainties and external disturbances are unavoidable in practical applications, a robust controller is designed to achieve the tracking control of the DESA, the controller also guarantees the robustness of the control system. Finally, the effectiveness of the proposed control method is demonstrated through a series of tracking control experiments with different high-frequency trajectories, whose maximal frequency is 8 Hz. The root-mean-square errors of all the experimental results are lower than 1.5%, which proves that the presented method is remarkable from the perspective of the practical application.

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

This article presents a model reference adaptive control method (MRACM) for the high-precision tracking control of a dielectric elastomer material-based intelligent actuator (DEMIA). First, a dynamics model of the DEMIA is established to delineate its complex nonlinear behaviors. Second, a feed-forward inverse compensation control method (FICCM) is proposed to compensate for the nonlinear behaviors of the DEMIA, so as to preliminarily achieve its tracking control. Third, due to the fact that model uncertainties and external disturbances are inescapable in practical applications, a MRACM based on the established nominal model of the DEMIA is further presented to improve the tracking control performance. The stability of the entire control system is proved via the Lyapunov method. In the end, a series of tracking control experiments with different multifrequency expected trajectories are executed to illustrate the validity of the proposed control methods. The root-mean-square errors of all control experiment results are less than 2.8%, which reflects that the proposed MRACM is distinguished from a practical application perspective.

In this paper, using the ball and beam system as an illustration, a control scheme is developed on human-robot collaboration, i.e., a two-level hierarchical framework is proposed to establish a robust human-robot collaboration (HRC) policy. On the high level, a deep reinforcement learning (DRL) algorithm is presented to plan the desired beam rotational velocity. The low level is constructed by a human-intention perception module and a robust collaboration policy design module. For the first module, a probabilistic model is fitted by using the Gaussian process regression (GPR) approach to predict human-hand velocities, and prediction results follow Gaussian distributions where mean values and variances represent predicted human-hand velocities and corresponding prediction confidences, respectively. For the second module, a robust collaboration policy is established by fusing a proactive policy and a conservative policy, where the proactive policy is used to control the robot to achieve the desired beam rotational velocity by using the predicted human-hand velocities. The conservative policy is designed to ensure the collaboration safety. The weighted parameters for fusion are adaptively tuned based on the prediction precision and confidence. Experiments are conducted on controlling ball position on a beam jointly by a human and a robot with vision data, and experimental results show the effectiveness of the designed robust collaboration policy. Note to Practitioners—Predicting human future behaviors and moderating robot behaviors accordingly is a long-standing problem for human-robot collaboration (HRC) tasks, such as assembling, transporting, etc. Existing approaches generally regard human behaviors as noises or only build simple human models without prediction confidence. This paper proposes a learning-based hierarchical framework that will derive a robust and safe HRC policy considering human behaviors, prediction confidence, and task-related optimality. The framework is validated by a representative experiment where human and robot are asked to jointly control a ball and beam system.

This article presents an active human-following control of the lower limb exoskeleton for gait training. First, to improve safety, considering the human balance, the OpenPose-based visual feedback is used to estimate the individual's pose, then, the active human-following algorithm is proposed for the exoskeleton robot to achieve the body weight support and active human-following. Second, taking the human's intention and voluntary efforts into account, we develop a long short-term memory (LSTM) network to extract surface electromyography (sEMG) to build the estimation model of joints' angles, that is, the multichannel sEMG signals can be correlated with flexion/extension (FE) joints' angles of the human lower limb. Finally, to make the robot motion adapt to the locomotion of subjects under uncertain nonlinear dynamics, an adaptive control strategy is designed to drive the exoskeleton robot to track the desired locomotion trajectories stably. To verify the effectiveness of the proposed control framework, several recruited subjects participated in the experiments. Experimental results show that the proposed joints' angles estimation model based on the LSTM network has a higher estimation accuracy and predicted performance compared with the existing deep neural network, and good simultaneous locomotion tracking performance is achieved by the designed control strategy, which indicates that the proposed control can assist subjects to perform gait training effectively.

Liquid crystal elastomer (LCE) is a promising candidate for actuation in light-driven soft robot applications. Due to the fact that LCE has complex hysteretic nonlinearities, which are highly dependent on the environment, modeling of actuators made of LCE is a very challenging issue. In this article, a model is proposed to describe the deformation of the LCE actuator accurately and analytically by considering the dynamic phase transition process of LCE molecules. First, an overview of the physical process of LCE's deformation is presented, and the schematic of the LCE actuator, as well as the modeling scheme are then introduced. Next, a thermodynamic analysis of the system's free energy is performed to establish the model for the LCE actuator, which gives the relationship between the system's deformation and the temperature. Here, to describe the complex hysteretic nonlinearity in the model, the dynamic process of the phase transition of LCE molecules is exploited. To effectively identify the model parameters, a two-step parameter identification strategy based on the differential evolution algorithm and nonlinear least-squares method is utilized. Finally, experimental results verify the validity of the proposed model. This modeling provides an approach to describe LCE's deformation with high accuracy and can fully reflect the physical nature of LCE's deformation, especially hysteresis. It can be utilized as a basis for accurate control over LCE actuators in photoresponsive soft robot applications.

The security control issue of nonlinear networked systems is considered in this article on the basis of interval type-2 fuzzy modeling strategy. For avoiding communication congestion, a stochastic scheduling strategy called Markovian communication protocol is introduced to coordinate the sensor transmission order. An asynchronous observer is designed for estimating the unmeasured states via the hidden Markov model. In addition, a more comprehensive scenario on deception attack is considered, in which attacks occur both in the sensor–observer and controller–actuator communication channels with different types of deception signals. In view of slack matrix approach and stochastic analysis technique, some sufficient conditions for ensuring the ultimately boundedness of the resulting closed-loop system are obtained. In the end, simulations show the validity of the proposed protocol-based fuzzy control method.

This article considers the prescribed performance output-feedback cooperative control of multiagent systems with nonparametric uncertainties. Scale and performance functions are proposed to construct a barrier function, based on which the adaptive prescribed performance control scheme is developed. Therein, the fuzzy observers are employed to estimate the unavailable states. The proposed scheme has the following characteristics: first, the tracking errors are always limited to a specified boundary. Second, the tracking error converges to a specified accuracy within a given time, and the settling time and tracking accuracy are determined only by the user and no longer depend on the initial conditions. Third, the distributed fuzzy controller design uses only the output information from agent and its neighbor. The effectiveness and practicality of the proposed method are demonstrated by two simulation examples.

Whole-body control (WBC) has emerged as an important framework in manipulation for mobile manipulators. However, most existing WBC frameworks require known dynamics. Considering whole-body manipulation and optimization with unknown dynamics, this article presents the WBC of a nonholonomic mobile manipulator using model predictive control (MPC) and fuzzy logic system. First, by constructing a dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system, an MPC-based WBC strategy is proposed for mobile manipulator. Such a strategy can provide the optimal control inputs with the specified optimization index and constraints. Thereafter, a primal-dual neural network effectively addresses the constrained quadratic programming (QP) problem over a finite receding horizon brought by the MPC. Then, in order to convert the intermediate control signals into the optimal control torques that can be executed by actuators, an adaptive FLS is employed to approximate the unknown dynamics. The novel elements of the current design control approach refer to the dynamics-based feedback linearized robotic MIMO system and the combination of an MPC module with an adaptive fuzzy controller. Finally, the trajectory tracking experiments performed on a mobile dual-arm robot demonstrate the effectiveness of the proposed method.

Most existing approximation-based adaptive control (AAC) approaches for unknown pure-feedback nonaffine systems retain a dilemma that all closed-loop signals are semiglobally uniformly bounded (SGUB) rather than globally uniformly bounded (GUB). To achieve the GUB stability result, this article presents a neuro-adaptive backstepping control approach by blending the mean value theorem (MVT), the barrier Lyapunov functions (BLFs), and the technique of neural approximation. Specifically, we first resort the MVT to acquire the intermediate and actual control inputs from the nonaffine structures directly. Then, neural networks (NNs) are adopted to approximate the unknown nonlinear functions, in which the compact sets for maintaining the approximation capabilities of NNs are predetermined actively through the BLFs. It is shown that, with the developed neuro-adaptive control scheme, global stability of the resulting closed-loop system is ensured. Simulations are conducted to verify and clarify the developed approach.

This study addresses estimator design for a class of nonuniformly sampled neural networks under the scenario of the sampling interval being inaccessible to the estimator. A new quantization model is described by a hidden Markov chain, where the emission probability depends on the network status and sampling interval. Two variables called hidden mode and observed mode are defined based on the assumption that the data receiver can recognize the quantization density instead of the sampling interval, and the associated observed mode-dependent estimator is designed. An augmented estimation error system is obtained, and the strict$(\mathcal {Q},\mathcal {S},\mathcal {R})-\gamma -$dissipativity for the nonuniformly sampled neural networks is investigated. Then the estimator gain is calculated by solving a set of linear matrix inequalities. Finally, the effectiveness of the proposed approach is demonstrated via an example.

This special issue of the IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (T-ASE) focuses on how the state-of-the-art achievements and applications in the general area of artificial intelligence in automation for autonomous unmanned systems applications. As Guest Editors, we are very pleased to present the selected 16 articles, whose topics are specifically related to artificial intelligence real-time object detection, recognition, localization, control optimization, motion planning, formation control, adaptive control, and autonomous decision-making.

In this article, the problem of adaptive tracking control is tackled for a class of high-order nonlinear systems. In contrast to existing results, the considered system contains not only unknown nonlinear functions but also unknown rational powers. By utilizing the fuzzy approximation approach together with the barrier Lyapunov functions (BLFs), we present a new adaptive tracking control strategy. Remarkably, the BLFs are employed to determine a priori the compact set for maintaining the validity of fuzzy approximation. The primary advantage of this article is that the developed controller is independent of the powers and can be capable of ensuring global stability. Finally, two illustrative examples are given to verify the effectiveness of the theoretical findings.

This article investigates a distributed fractional-order fault-tolerant formation-containment control (FOFTFCC) scheme for networked unmanned airships (UAs) to achieve safe observation of a smart city. In the proposed control method, an interval type-2 fuzzy neural network (IT2FNN) is first developed for each UA to approximate the unknown term associated with the loss-of-effectiveness faults in the distributed error dynamics, and then a disturbance observer (DO) is proposed to compensate for the approximation error and bias fault encountered by each UA, such that the composite learning strategy composed of the IT2FNN and the DO is obtained for each UA. Moreover, fractional-order (FO) calculus is incorporated into the control scheme to provide an extra degree of freedom for the parameter adjustments. The salient feature of the proposed control scheme is that the composite learning algorithm and FO calculus are integrated to achieve a satisfactory fault-tolerant formation-containment control performance even when a portion of leader/follower UAs is subjected to the actuator faults in a distributed communication network. Furthermore, it is shown by Lyapunov stability analysis that all leader UAs can track the virtual leader UA with time-varying offset vectors, and all follower UAs can converge into the convex hull spanned by the leader UAs. Finally, comparative hardware-in-the-loop (HIL) experimental results are presented to show the effectiveness and superiority of the proposed method.

This article proposes a fractional-order intelligent fault-tolerant synchronization tracking control (FO-I-FTSTC) scheme for multiple unmanned airships (UAs) against actuator faults. Within the developed control architecture, fixed-time prescribed performance functions (PPFs) are first designed to transform the synchronization tracking errors into a new set of error variables, such that the original errors are strictly confined within the prescribed bounds. Then, fractional calculus and sliding mode surface are sequentially introduced to construct the FO errors. Moreover, to handle the unknown terms and bias faults in the FO sliding-mode error dynamics, fuzzy neural networks with recurrent loops are artfully constructed to act as the intelligent learning units. Furthermore, the norm of the loss-of-effectiveness fault factors is introduced for each UA to reduce the number of adaptive parameters. The distinct feature of the proposed method is that the FO-I-FTSTC performance is significantly enhanced by integrating recurrent fuzzy neural networks, fractional calculus, and fixed-time PPFs into a unified framework, leading to a high-precision control scheme. It is shown by Lyapunov analysis that all UAs can track their desired references in a synchronized manner, and the synchronization tracking errors are bounded and strictly confined within the prescribed error bounds. Comparative hardware-in-the-loop experiments are presented to show the effectiveness of the proposed FO-I-FTSTC scheme.

This article deals with an uncertain two-link rigid-flexible manipulator with vibration amplitude constraint, intending to achieve its position control via motion planning and adaptive tracking approach. In motion planning, the motion trajectories for the two links of the manipulator are planned based on virtual damping and online trajectories correction techniques. The planned trajectories can not only guarantee that the two links can reach their desired angles, but also have the ability to suppress vibration, which can be adjusted to meet the vibration amplitude constraint by limiting the parameters of the planned trajectories. Then, the adaptive tracking controller is designed using the radial basis function neural network and the sliding mode control technique. The developed controller makes the two links of the manipulator track the planned trajectories under the uncertainties including unmodeled dynamics, parameter perturbations, and persistent external disturbances acting on the joint motors. The simulation results verify the effectiveness of the proposed control strategy and also demonstrate the superior performance of the motion planning and the tracking controller.

This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme.

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

In this article, the adaptive fuzzy tracking control problem for a class of uncertain strict-feedback systems with unknown nonlinearities is investigated withparticular emphasis on global stability. The proposed control scheme is designed by integrating the barrier Lyapunov functions (BLFs) with the techniques of fuzzy approximation and backstepping. The novel integral BLFs (iBLFs) are introduced to overcome the design difficulties induced by the virtual control coefficients and determinea priorithe compact set for guaranteeing the validity of fuzzy approximation. Compared with existing approximation-based control results, the developed controller not only guarantees global stability without requiring prior information of system nonlinearities and assumptions on the time derivatives of virtual control coefficients, but also prevents the “explosion of complexity” issue without attaching additional filters. The simulation results further confirm the effectiveness of the theoretical findings.

The study on the stabilization of planar underactuated manipulators without gravity is well recognized as a major challenge since the system includes a second-order nonholonomic constraint when the passive link is not located at the first link. It is important to solve this difficulty for applications such as systems working in aerospace or underwater. This article presents a position control method based on bidirectional motion planning and intelligent optimization for this kind of system. The control objective is to move the end effector of the manipulator from its initial position to a given target position. The differential evolution algorithm is applied to solve the target angles of all links corresponding to the target position of the end effector. Then, bidirectional motion planning is performed, which consists of forward and backward motions. Each motion is planned by designing a trajectory for every active link based on their initial and target angles. During the forward motion, all active links except the first one are moved to their target angles, and the first active link and the passive link to the intermediate angles. For the backward motion, the first active link is moved to its target angle, the other active links remain at their target angles, and the passive link will be moved to its target angle at the same time. The planned trajectories are chosen based on the time-scaling method and differential evolution algorithm to make sure that the forward and backward planned motions can be connected smoothly. Finally, the trajectory tracking controllers for all active links are designed based on the sliding-mode control method. The proposed control method is verified on planar four-link underactuated manipulators with different passive joints. This strategy has the advantage that it works for planar underactuated manipulators with a second-order nonholonomic constraint whose passive link can be at different positions. Meanwhile, by combining intelligent optimization with bidirectional motion planning, the control process becomes simpler and more effective.

This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the “explosion of complexity” problem in the backstepping method is overcome and the L∞tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme.

This article considers the funnel output tracking control for a class of high-order uncertain nonlinear systems with the powers of positive odd rational numbers, aiming to accomplish output tracking with prescribed accuracy when both the system nonlinearities and the powers of the system are unknown. For such a purpose, a robust funnel control algorithm, i.e., a continuous, static, and universal, state-feedback controller is explicitly constructed, which achieves the state errors evolving within the predesigned performance space. Benefits of the proposed funnel output tracking controller comparing with the current approaches lie in the fact that the exact knowledge of system nonlinearities, including generally required bounding functions, is not needed to be a priori. Moreover, all the powers in each high-order subsystem are permitted to be any unknown positive odd rational numbers as well. The efficacy of the developed algorithm is confirmed through two illustrative examples.

This article addresses the visual regulation problem of a differential-drive mobile robot with an arbitrarily installed monocular camera in the indoor environment. A three-stage controller is designed by using a novel nonadaptive switching approach, where the unknown image depth and the uncalibrated camera-to-robot translation parameters do not need to be estimated. Convergence of the error systems with the designed controller in each stage is analyzed. Moreover, the existence of the switching time instants from each stage is proved. The simulation results are presented to show the effectiveness of the proposed approach.

This article proposes a targeting posture control approach with dynamic obstacle avoidance of differential-drive wheeled mobile robot (WMR) systems in the presence of unknown skidding, slipping, input disturbances, model uncertainties, and torque saturation. First, a nonlinear model predictive control (NMPC) scheme is presented to generate a feasible trajectory from a starting posture to a targeting posture where dynamic obstacles in the environment and physical constraints of the robots are considered. Second, a robust virtual control law at the kinematic level is introduced for the robots to follow the trajectory. Third, taking the consideration of the unknown skidding, slipping, input disturbances, and model uncertainties in the dynamic model being lumped as a total disturbance, which is estimated by the linear extended state observer (LESO), a disturbance compensation-based saturation controller is designed to make the real velocity of the robot converge to the virtual velocity command. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

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

The problem of quasi-synchronization (QS) for the Markovian jump master-slave neural networks with time-varying delay is studied in this article, where the mismatch parameters and unreliable communication channels are considered as well. A set of stochastic variables with different expectations are used to describe the fading phenomena of parallel communication channels. An impulsive-driven transmission strategy is designed to reduce the communication load, and a corresponding impulsive controller is then designed. A synchronization error system (SES) is obtained, and a convex QS condition is established for the SES. A linear matrix inequality-based iterative algorithm is proposed to reduce the bound of the SES, and the corresponding controller gains are calculated. A numerical example is provided to illustrate the effectiveness of the developed result.

In this article, a globally stable adaptive control strategy for uncertain strict-feedback systems is proposed within predefined neural network (NN) approximation sets, despite the presence of unknown system nonlinearities. In contrast to the conventional adaptive NN control results in the literature, a primary benefit of the developed approach is that the barrier Lyapunov function is employed to predefine the compact set for maintaining the validity of NN approximation at each step, thus accomplishing the global boundedness of all the closed-loop signals. Simulation results are performed to clarify the effectiveness of the proposed methodology.

This paper investigates the distributed finite-time fault-tolerant containment control problem for multiple unmanned aerial vehicles (multi-UAVs) in the presence of actuator faults and input saturation. The distributed finite-time sliding-mode observer (SMO) is first developed to estimate the reference for each follower UAV. Then, based on the estimated knowledge, the distributed finite-time fault-tolerant controller is recursively designed to guide all follower UAVs into the convex hull spanned by the trajectories of leader UAVs with the help of a new set of error variables. Moreover, the unknown nonlinearities inherent in the multi-UAVs system, computational burden, and input saturation are simultaneously handled by utilizing neural network (NN), minimum parameter learning of NN (MPLNN), first-order sliding-mode differentiator (FOSMD) techniques, and a group of auxiliary systems. Furthermore, the graph theory and Lyapunov stability analysis methods are adopted to guarantee that all follower UAVs can converge to the convex hull spanned by the leader UAVs even in the event of actuator faults. Finally, extensive comparative simulations have been conducted to demonstrate the effectiveness of the proposed control scheme.

Motion tracking control design of nonholonomic mobile robot systems considering actuator dynamics is addressed in this paper. A trajectory tracking controller is designed at actuator level, which guarantees that the nonholonomic mobile robot tracks a given trajectory. A numerical example is shown to demonstrate and validate the proposed approach in this paper.

Humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. Traditional robots perform tasks independently without considering their interactions with the external environment, which leads to poor flexibility and adaptability. Comparatively, humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. In order to solve the problems of human-robot security and adaptability to unknown environment, a human-inspired control with force and impedance adaptation is proposed to interact with unknown environments and exhibit this biological behavior on the developed dual-arm exoskeleton robots. First, we propose a computationally model utilizing the sampled surface electromyogram (sEMG) signals to calculate the human arm endpoint stiffness and define a co-contraction index to describe the dynamic behaviors of the muscular activities in the tasks. Then, the obtained human limb impedance stiffness parameters and the sampling position information are transferred to the slave arm of the exoskeleton as the input variables of the controller in real-time. In addition, a variable stiffness observer is used here to compensate for the errors of the calculated stiffness by sEMG signals. The experimental studies of human impedance transfer control have been conducted to show the effectiveness of the developed approach. Results of the experimental suggest that the proposed controller can achieve human motor adaptation and enable the subjects to execute a skill transfer control by a dual-arm exoskeleton robot.

This paper investigates an${H}_\infty $estimator design for time-varying coupled neural networks (NNs) over a finite-horizon. In order to reduce the information exchanged among the NNs, a periodic inner-coupling strategy is proposed. In addition, a Markov driven transmission scheme is introduced to overcome the communication capacity constraint between the NNs and the estimators, where an inner-coupling-dependent Markov chain is used to improve the efficiency of the communication channel. Subsequently, the time-varying Markov estimators are designed to enhance the performance of the estimators. A recursive matrix inequality (RMI)-based sufficient condition is established to ensure that the time-varying estimation error system meets the finite-horizon${H}_\infty $performance. Afterward, the estimator gains are designed by transforming the RMIs into linear RMIs. Finally, a numeral example is used to illustrate the developed results.

In this paper, a decentralized fault-tolerant cooperative control scheme is developed for multiple unmanned aerial vehicles (UAVs) in the presence of actuator faults and a directed communication network. To counteract in-flight actuator faults and enhance formation flight safety, neural networks (NNs) are used to approximate unknown nonlinear terms due to the inherent nonlinearities in UAV models and the actuator loss of control effectiveness faults. To further compensate for NN approximation errors and actuator bias faults, the disturbance observer (DO) technique is incorporated into the control scheme to increase the composite approximation capability. Moreover, the prediction errors, which represent the approximation qualities of the states induced by NNs and DOs to the measured states, are integrated into the developed fault-tolerant cooperative control scheme. Furthermore, prescribed performance functions are imposed on the attitude synchronization tracking errors, to guarantee the prescribed synchronization tracking performance. One of the key features of the proposed strategy is that unknown terms due to the inherent nonlinearities in UAVs and actuator faults are compensated for by the composite approximators constructed by NNs, DOs, and prediction errors. Another key feature is that the attitude synchronization tracking errors are strictly constrained within the prescribed bounds. Finally, simulation results are provided and have demonstrated the effectiveness of the proposed control scheme.

In this paper, a hierarchical hybrid motion/force control architecture for the manipulation and grasping of mobile manipulators is presented, where the systems are subject to varieties of physical constraints such as Coulomb friction cones, nonholonomic/holonomic constraints, and actuator saturation limits. The incorporation of a projection-based operation space control and an adaptive controller based on the neural networks used in this paper formulates a novel control scheme, so the system stability is further guaranteed and the uncertain dynamics is handled without redesigning the minimal-order dynamics model. Considering the effects of these constraints, the actuator saturation limits are handled by an auxiliary designed system, and the neural dynamics optimization is applied for the quadratically constrained programing problem of the optimal robotic grasping. The dynamic uncertainties can be estimated online by using the developed motion/force control strategy, and the application of a novel disturbance observer is explored to ensure the good tracking performance. The experimental results are presented to verify the performance and the efficiency of the proposed method.

Focusing on the piezoelectric positioning stage, this paper proposes an adaptive estimated inverse output-feedback quantized control scheme. First, the quantized issue due to the use of computer is addressed by introducing a linear time-varying quantizer model where the quantizer parameters can be estimated on-line. Second, by using the fuzzy approximator, the developed controller can avoid the identification of the parameters in the piezoelectric positioning stage. Third, by constructing the estimated inverse compensator of the hysteresis, the hysteresis nonlinearities in the piezoelectric actuator are mitigated; Fourth, the states observer is designed to avoid the measurements of the velocity and acceleration signals. The analysis of stability shows all the signals in the piezoelectric positioning stage are uniformly ultimately bounded and the prespecified tracking performance of the quantized control system is achieved by employing the error transformed function. Finally, a computer controlled experiments for the piezoelectric positioning stage is conducted to show the effectiveness of the proposed quantized controller.

Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.

This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small L∞tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.

In this paper, an active control approach using two piezoelectric actuators (PZTAs) and an adaptive controller is investigated for suppressing the two-DOF regenerative chatter in micromilling. The PZTAs are utilized as active control elements to provide force compensation for chatter suppression. First, the dynamical model of micromilling process is demonstrated. Then, an adaptive controller is developed by employing neural networks to approximate the unknown dynamics of the cutting system and the unknown bounding functions related to the time-delayed tool vibrations, and applying the Lyapunov-Krasovskii functional to aid in treating the time-delayed effect of the regenerative mechanism of chatter. By employing the developed control approach, the tool vibrations in two directions vertical to each other are successfully suppressed. Finally, simulations are presented to validate the effectiveness of the developed control approach.

It has been established that the transfer of human adaptive impedance is of great significance for physical human-robot interaction (pHRI). By processing the electromyography (EMG) signals collected from human muscles, the limb impedance could be extracted and transferred to robots. The existing impedance transfer interfaces rely only on visual feedback and, thus, may be insufficient for skill transfer in a sophisticated environment. In this paper, physical haptic feedback mechanism is introduced to result in muscle activity that would generate EMG signals in a natural manner, in order to achieve intuitive human impedance transfer through a designed coupling interface. Relevant processing methods are integrated into the system, including the spectral collaborative representation-based classifications method used for hand motion recognition; fast smooth envelop and dimensionality reduction algorithm for arm endpoint stiffness estimation. The tutor's arm endpoint motion trajectory is directly transferred to the robot by the designed coupling module without the restriction of hands. Haptic feedback is provided to the human tutor according to skill learning performance to enhance the teaching experience. The interface has been experimentally tested by a plugging-in task and a cutting task. Compared with the existing interfaces, the developed one has shown a better performance.

This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle the uncertainty and the unknown deadzone effect, we introduce adaptive neural network (NN) control for robotic manipulators. State-feedback control is introduced first and a high-gain observer is then designed to make the proposed control scheme more practical. One radial basis function NN (RBFNN) is used to tackle the deadzone effect, and the other RBFNN is also proposed to estimate the unknown dynamics of robot. The proposed control is then verified on a two-joint rigid manipulator via numerical simulations and experiments.

This paper reports the development of an intelligent shared control system for a robotic manipulator that is commanded by the user's mind. The target objects are detected by a vision system and then displayed to the user in a video that shows them fused with flicking diamonds that are designed to excite electroencephalograph (EEG) signals at different frequency bands. Through the analysis of the invoked EEG signals, a brain-computer interface is developed to infer the exact object that is required by the user. These results are then transferred to the shared control system, which is enabled by visual servoing techniques to achieve accurate object manipulation. The task motion and self-motion (CTS) methods are coordinated to enhance the intelligence of the shared control system by equipping the robot with an autonomous obstacle avoidance function. Extensive experimental studies are performed to verify that the adaptive object tracking algorithm, the CTS method, and the least-squares method are helpful in improving the performance of the intelligent robotic system.

This paper investigates the estimator design for the neural networks, where distributed delays and imperfect measurements are included. A randomly occurred neuron-dependent nonlinearity is used to describe the uncertain measurements disturbed by neurons. The measurements are transmitted over multiple transmission channels, and Markov chains are introduced to model packet dropouts of these channels. A one-to-one map is constructed to transform $m$ independent Markov chains to an augmented one to facilitate system analysis. A new variable called channel state is defined based on the cases of packet dropouts, and the channel-state-dependent estimator is designed to trade off between the number and the performance of the estimator. Sufficient conditions are established to guarantee that the augmented system is stochastically stable and satisfies the strict $(Q, S, R)-\gamma -$ dissipativity. The estimator gains are derived using linear matrix methods. Finally, an example is applied to illustrate the effectiveness of the developed methods.

In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator's motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject's movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject's muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking.

This paper studies the state estimator design for periodic neural networks, where stochastic weight matrices B(k) and packet dropouts are considered. The stochastic variables, which may influence each other, are introduced to describe uncertainties of weight matrices. In order to model the time-varying conditions of the communication channel, a Markov chain is employed to study the jumping cases of the stochastic properties of the packet dropouts (i.e., Bernoulli process with jumping means and variances being used to handle the packet dropouts). A state estimator is constructed such that the augmented system is stochastically stable and satisfies the H∞performance. The estimator parameters are derived by means of the linear matrix inequalities method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.

Teleoperated robot systems are able to support humans to accomplish their tasks in many applications. However, the performance of teleoperation largely depends on motor functionality and human operator's skill, especially when a human operator is short of skill training. In order to adapt to various unstructured environments for the robot system and the human operator, in this paper, a teleoperation scheme using integrated tremor attenuation with a variable gain control algorithm involving surface electromyogram is proposed to achieve personalized control performance and to reduce reliance on operator's skill. For attenuating tremor, a filter based on support vector machine is developed to guarantee normal operation. This filter depends on the machine learning scheme and does not rely on a priori filter parameters. Semiphysical experiments have been performed to demonstrate the effectiveness of the proposed methods.

To suppress the regenerative chatter in milling, an active control strategy with utilization of an adaptive controller and two piezoelectric actuators (PZTAs) is developed, where the nonlinear cutting forces, uncertain dynamics of the milling, hysteresis effect of the PZTAs and time-delayed effect of the chatter are considered. Fuzzy neural networks (FNNs) are employed to approximate the unknown function of the dynamics of the milling. The Prandtl-Ishlinskii (PI) model is adopted to describe the hysteresis nonlinearities of the PZTAs and its inverse model is applied to alleviate the hysteresis effect. In order to achieve the expected tracking trajectories with prescribed performances, two performance functions are used. By employing two PZTAs as active control elements and by combining the dynamic surface control (DSC) method, the FNNs, the Lyapunov-Krasovskii functionals and the performance functions, the boundedness of all signals of the closed loop and the chatter stability are obtained. The validity of the developed control strategy is revealed by simulations.

This paper presents a teleoperation control for an exoskeleton robotic system based on the brain-machine interface and vision feedback. Vision compressive sensing, brain-machine reference commands, and adaptive fuzzy controllers in joint-space have been effectively integrated to enable the robot performing manipulation tasks guided by human operator's mind. First, a visual-feedback link is implemented by a video captured by a camera, allowing him/her to visualize the manipulator's workspace and movements being executed. Then, the compressed images are used as feedback errors in a nonvector space for producing steady-state visual evoked potentials electroencephalography (EEG) signals, and it requires no prior information on features in contrast to the traditional visual servoing. The proposed EEG decoding algorithm generates control signals for the exoskeleton robot using features extracted from neural activity. Considering coupled dynamics and actuator input constraints during the robot manipulation, a local adaptive fuzzy controller has been designed to drive the exoskeleton tracking the intended trajectories in human operator's mind and to provide a convenient way of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiment studies employing three subjects have been performed to verify the validity of the proposed method.

Robots with coordinated dual arms are able to perform more complicated tasks that a single manipulator could hardly achieve. However, more rigorous motion precision is required to guarantee effective cooperation between the dual arms, especially when they grasp a common object. In this case, the internal forces applied on the object must also be considered in addition to the external forces. Therefore, a prescribed tracking performance at both transient and steady states is first specified, and then, a controller is synthesized to rigorously guarantee the specified motion performance. In the presence of unknown dynamics of both the robot arms and the manipulated object, the neural network approximation technique is employed to compensate for uncertainties. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is integrated into the control design. Effectiveness of the proposed control design has been shown through experiments carried out on the Baxter Robot.

In this paper, an active dynamic surface control (DSC) to suppress regenerative chatter in micro-milling with nonlinear piezo-actuators (PZTAs) as active elements has been developed, where the hysteresis effects of two PZTAs are considered. The main features of the proposed approach are: (1) fuzzy neural networks (FNNs) are applied to approximate the unknown functions, including the uncertain dynamics of the high speed cutting system, the unknown bounding functions related to the time-delayed states, and the control errors introduced during the control design procedure; (2) to avoid the development of the adaptation law for each individual weight of the FNNs, the squared 2-norms of the weight vectors are constructed; (3) the utilization of two decreasing smooth performance functions ensures the boundedness of the tracking errors with the prescribed performance; (4) the Prandtl-Ishlinskii (PI) model is employed to depict the hysteresis effect of the active PZTAs and its inverse construction is adopted to mitigate the nonlinear influence; and (5) the Lyapunov-Krasovskii functional is used to cope with the time-delayed effect of chatter in micro-milling, which plays a pivotal role in developing the active control laws. The developed controller with the employments of Lyapunov-Krasovskii functionals, DSC and the adaptive FNN backstepping design can successfully suppress the chatter in micro-milling, guaranteeing all the signals of the closed-loop system are bounded. Simulation results are provided to verify the effectiveness of the proposed control approach.

This paper presents an extreme learning machine (ELM)-based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov-based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

In this paper, a fuzzy approximator based adaptive dynamic surface control scheme is proposed for a class of unknown time delay nonlinear systems preceded by asymmetric hysteresis nonlinearities. The features of the proposed method are: 1) by combining the approximated property of the fuzzy logic systems (FLSs) with the finite covering lemma, the Krasovskii functionals are disposed of, achieving the${L}_{{\infty }}$norm of the tracking error by using the initializing technique; 2) the assumptions on the time-delay functions are removed due to the use of the finite covering lemma and the FLSs; and 3) the proposed adaptive fuzzy dynamic surface control scheme can also compensate the asymmetric shifted Prandtl-Ishlinskii (ASPI) hysteresis without constructing the inverse of the ASPI model with the density function of ASPI model being unknown and estimated online to compensate the hysteresis. It is proved that all the signals in the closed-loop system are ultimately uniformly bounded and can be made arbitrarily small. Simulation results show the validity of the proposed method.

This paper presents development of energyefficient and fault-tolerant control of multiphase nonsinusoidal PM synchronous machines by making use of the Hamiltonian of optimal control theory. An analytical solution for the optimallinearization control is derived in a closed-form from the maximum principle formulation to linearize the machines and at same time to maximize machine efficiency for non-constant operational torque and speed while automatically deferring output voltage saturation. The controller can achieve voltage-totorque linearization even for faulty motors with open-circuited phase(s) making it suitable for high-risk applications. Experimental results are appended to underpin the performance of the energy-efficient controller.

The projection-based control of a rather general class of robots subject to linear and quadratic inequality constraints pertinent to unilateral contacts and friction cones is presented. The controller can also take into account other physical constraints levied by actuator saturation limits and/or existence of some unactuated joints, and can minimize actuation effort for redundant systems. Moreover, since the controller is not based on derivation of minimal-order dynamics model, it can easily handle contact switching. Therefore, a single controller can be used for different constraint conditions, which is very appealing for applications such as legged robots or grasping robots. The orthogonal decomposition of the generalized force vector yields the primary and secondary control inputs, which are used for motion control and interaction control, respectively. It is shown that the problem of minimizing actuation effort subject to the constraints due to the friction cones, unilateral contacts, and actuation limitations, can be transcripted into an optimization programming in terms of the secondary control variable. The latter optimization problem has a quadratic cost function accompanied by a set of quadratic and linear inequality constraints plus linear equality constraints that can be solved by barrier methods.

Piezo-actuated stages have become more and more promising in nanopositioning applications due to the excellent advantages of the fast response time, large mechanical force, and extremely fine resolution. Modeling and control are critical to achieve objectives for high-precision motion. However, piezo-actuated stages themselves suffer from the inherent drawbacks produced by the inherent creep and hysteresis nonlinearities and vibration caused by the lightly damped resonant dynamics, which make modeling and control of such systems challenging. To address these challenges, various techniques have been reported in the literature. This paper surveys and discusses the progresses of different modeling and control approaches for piezo-actuated nanopositioning stages and highlights new opportunities for the extended studies.

Most studies on bilateral teleoperation assume known system kinematics and only consider dynamical uncertainties. However, many practical applications involve tasks with both kinematics and dynamics uncertainties. In this paper, trilateral teleoperation systems with dual-master-single-slave framework are investigated, where a single robotic manipulator constrained by an unknown geometrical environment is controlled by dual masters. The network delay in the teleoperation system is modeled as Markov chain-based stochastic delay, then asymmetric stochastic time-varying delays, kinematics and dynamics uncertainties are all considered in the force-motion control design. First, a unified dynamical model is introduced by incorporating unknown environmental constraints. Then, by exact identification of constraint Jacobian matrix, adaptive neural network approximation method is employed, and the motion/force synchronization with time delays are achieved without persistency of excitation condition. The neural networks and parameter adaptive mechanism are combined to deal with the system uncertainties and unknown kinematics. It is shown that the system is stable with the strict linear matrix inequality-based controllers. Finally, the extensive simulation experiment studies are provided to demonstrate the performance of the proposed approach.

In this paper, a vision-based adaptive control is designed for a wheeled inverted pendulum (WIP) robot to track a moving human target by integration of multisensor data. A new algorithm is employed in the system to combine an OptiTrack camera and a Kinect camera, such that more robust and efficient performance can be achieved for human target detection and tracking. Robust adaptive control has been developed for the WIP robot to maintain its balance on two wheels and to follow the human target using visual feedback. Leader-follower control, dynamic balance control and visual tracking are efficiently combined together to achieved desired tracking and balancing performance. Extensive experiment studies have been performed to test the effectiveness of the proposed control strategies.

Magnetostrictive actuators featuring high energy densities, large strokes, and fast responses are playing an increasingly important role in micro/nano-positioning applications. However, such actuators with different input frequencies and mechanical loads exhibit complex dynamics and hysteretic behaviors, posing a great challenge on applications of the actuators. Therefore, it is important to develop a dynamic model that can characterize dynamic behaviors of the actuators, including current-magnetic flux nonlinear hysteresis, frequency responses, and loading effects, simultaneously. To this end, a comprehensive model, which thoroughly considers the electric, magnetic, and mechanical domain, as well as the interactions among them, is developed in this paper. To validate the developed model, the parameters of the model are identified where the hysteresis of the magnetostrictive actuator is described, as an illustration, by the asymmetric shifted Prandtl-Ishlinskii model. The experimental results demonstrate that the comprehensive model presents an excellent agreement with dynamic behaviors of the magnetostrictive actuator.

This paper discusses the modeling and control of the ionic polymer-metal composite (IPMC) actuators which have many promising applications in biomechatronics. A novel mathematical model in continuous-time domain of the IPMC actuator, being a stable second-order dynamical system preceded by a nonlinear hysteresis representation, is proposed. An adaptive controller is formulated for the IPMC actuator based on the proposed model. The proposed adaptive control law ensures the global stability of the controlled IPMC system, and the position error of the IPMC actuator can be theoretically guaranteed to converge to zero. The effectiveness of the proposed model and the superiority of the proposed control to the traditional proportional-integral-derivative control are verified by experimental results.

Mobile robots tracking a reference trajectory are constrained by the motion limits of their actuators, which impose the requirement for high autonomy driving capabilities in robots. This paper presents a model predictive control (MPC) scheme incorporating neural-dynamic optimization to achieve trajectory tracking of nonholonomic mobile robots (NMRs). By using the derived tracking-error kinematics of nonholonomic robots, the proposed MPC approach is iteratively transformed as a constrained quadratic programming (QP) problem, and then a primal-dual neural network is used to solve this QP problem over a finite receding horizon. The applied neural-dynamic optimization can make the cost function of MPC converge to the exact optimal values of the formulated constrained QP. Compared with the existing fast MPC, which requires repeatedly calculating the Hessian matrix of the Langragian and then solves a quadratic program. The computation complexity reaches O(n3), while the proposed neural-dynamic optimization contains O(n2) operations. Finally, extensive experiments are provided to illustrate that the MPC scheme has an effective performance on a real mobile robot system.

This paper presents fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs to provide forearm movement assistance so that a human forearm can track any continuous desired trajectory (or constant setpoint) in the presence of parametric/functional uncertainties, unmodeled dynamics, actuator dynamics, and/or disturbances from environments. Given the desired trajectories of human forearm positions, in the developed control, adaptive fuzzy approximators are used to estimate the dynamical uncertainties of the human-robot system, and an iterative learning scheme is utilized to compensate for unknown time-varying periodic disturbances. With the synthesis of the backstepping, iterative learning, and Lyapunov function approaches, the developed controller does not require exact knowledge of the exoskeleton model, and the close-loop system can be proven to be semiglobally uniformly bounded. Three comparison experiments are conducted to illustrate the effectiveness of the proposed control scheme by tracking periodic/repeated trajectories.

In this paper, a decentralized adaptive fuzzy control has been developed for two cooperating robotic manipulators moving an object with impedance interaction. The contact forces are described using gradients of nonlinear potentials; then, the deformations of the contact surface can be obtained by an impedance approach. The cooperating manipulators are considered as a combination of subsystems, and the decentralized local dynamics coupled with physical interactions among the subsystems are developed. To compensate for the effect of dynamics uncertainties and external disturbances, decentralized fuzzy control combining parameter adaptations and disturbance observers is constructed. It guarantees the motion trajectories and impedance forces of the constrained object converging to the desired manifolds. It is theoretically established that the disturbance observers compensate for unparameterizable uncertainties, while the adaptive fuzzy mechanism compensates for the fast-changing components of the uncertainties that go beyond the disturbance observers. Moreover, unknown nonlinear dynamics such as the inertia matrix, Coriolis/centripetal matrix, and frictions, as well as interconnections with nonlinear bounds, can be accommodated through online learning. The experiments on two real robots have been carried out to verify the effectiveness of the proposed theoretical results.

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

In this paper, an adaptive neural network (NN) dynamic surface control is proposed for a class of time-delay nonlinear systems with dynamic uncertainties and unknown hysteresis. The main advantages of the developed scheme are: 1) NNs are utilized to approximately describe nonlinearities and unknown dynamics of the nonlinear time-delay systems, making it possible to deal with unknown nonlinear uncertain systems and pursue the L∞ performance of the tracking error; 2) using the finite covering lemma together with the NNs approximators, the Krasovskii function is abandoned, which paves the way for obtaining the L∞ performance of the tracking error; 3) by introducing an initializing technique, the L∞ performance of the tracking error can be achieved; 4) using a generalized Prandtl-Ishlinskii (PI) model, the limitation of the traditional PI hysteresis model is overcome; and 5) by applying the Young's inequalities to deal with the weight vector of the NNs, the updated laws are needed only at the last controller design step with only two parameters being estimated, which reduces the computational burden. It is proved that the proposed scheme can guarantee semiglobal stability of the closed-loop system and achieves the L∞ performance of the tracking error. Simulation results for general second-order time-delay nonlinear systems and the tuning metal cutting system are presented to demonstrate the efficiency of the proposed method.

Compensation of hysteresis nonlinearities in smart material based actuators presents a challenging task for their applications. Many approaches have been proposed in the literature, including the inverse multiplicative scheme. The advantage for such a scheme is to avoid direct model inversions. However, the approach is mainly developed for the Bouc-Wen model. Focusing on the Preisach model which is utilized to describe magnetostrictive actuators, in this paper an inverse compensation approach for Preisach model using the inverse multiplicative structure is developed. Since the input signal is implicitly involved in the Preisach model, it imposes a great challenge to construct the inverse function of the model. To obtain an explicit expression of the input signal from its implicit form so that the inverse multiplicative technique can be applied, the Preisach model is decomposed into a non-memory part and memory part. Using this separation, it only requires to solve the inverse of the non-memory part to obtain an explicit expression of the input signal, thus avoiding constructing the inverse for entire complex dual integral formulation of the Preisach model. Experimental results for a magnetostrictive actuator demonstrate the effectiveness of the proposed approach.

Emergent heterogeneous systems must be optimized for both power and performance at exascale. Massive parallelism combined with complex memory hierarchies form a barrier to efficient application and architecture design. These challenges are exacerbated with GPUs as parallelism increases orders of magnitude and power consumption can easily double. Models have been proposed to isolate power and performance bottlenecks and identify their root causes. However, no current models combine simplicity, accuracy, and support for emergent GPU architectures (e.g. NVIDIA Fermi). We combine hardware performance counter data with machine learning and advanced analytics to model power-performance efficiency for modern GPU-based systems. Our performance counter based approach is simpler than previous approaches and does not require detailed understanding of the underlying architecture. The resulting model is accurate for predicting power (within 2.1%) and performance (within 6.7%) for application kernels on modern GPUs. Our model can identify power-performance bottlenecks and their root causes for various complex computation and memory access patterns (e.g. global, shared, texture). We measure the accuracy of our power and performance models on a NVIDIA Fermi C2075 GPU for more than a dozen CUDA applications. We show our power model is more accurate and robust than the best available GPU power models - multiple linear regression models MLR and MLR+. We demonstrate how to use our models to identify power-performance bottlenecks and suggest optimization strategies for high-performance codes such as GEM, a biomolecular electrostatic analysis application. We verify our power-performance model is accurate on clusters of NVIDIA Fermi M2090s and useful for suggesting optimal runtime configurations on the Keeneland supercomputer at Georgia Tech.

The MuMMI (Multiple Metrics Modeling Infrastructure) project is an infrastructure that facilitates systematic measurement, modeling, and prediction of performance, power consumption and performance-power tradeoffs for parallel systems. In this paper, we present the MuMMI framework, which consists of an Instrument or, Databases and Analyzer. The MuMMI instrument or provides for automatic performance and power data collection and storage with low overhead. The MuMMI Databases store performance, power and energy consumption and hardware performance counters' data. The MuMMI Analyzer entails performance and power modeling and performance-power tradeoff and optimizations. As part of the MuMMI project, we mainly focus on discussing the design and development of a MuMMI Instrument or to provide automatic performance and power data collection and storage with low overhead on multicore systems in detail, then utilize the MuMMI Instrument or to collect performance and power data for a hybrid MPI/OpenMP earthquake application to discuss application performance-power trade-off and optimizations. Our experimental results show that we reduce up to 8.5% the application execution time and lower up to 18.35% the energy consumption by applying Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Concurrency Throttling (DCT) and loop optimizations.

In this paper, an adaptive fuzzy control scheme is proposed for hybrid motion/force of trilateral teleoperation systems with a dual-master-single-slave configuration under stochastic time-varying delays in communication channels. Different from previous works on bilateral teleoperation systems, this paper addresses dual-master trilateral control of a single holonomic-constrained robotic manipulator, where the communication delays are modeled as multiple Markov chains, and the motion/force controls are investigated under consideration of unsymmetric stochastic time-varying delays and system dynamical uncertainties. Using partial feedback linearization, the whole trilateral teleoperation system, which consists of both master and slave manipulator dynamics, is transformed into three subsystems. By integrating Markov jump systems to handle random delays, adaptive fuzzy control strategies are developed for the nonlinear teleoperators with modeling uncertainties and external disturbances by using the approximation property of the fuzzy logic systems (FLSs). It is proven that the trilateral teleoperation system is stochastically stable in mean square under specific linear matrix inequality (LMI) conditions, and all the signals of the resulting closed-loop system are uniformly bounded. The proposed scheme is validated by extensive simulations.

By representing nonlinear systems as a combination of linear part and unmodeled dynamics, in this paper, an improved estimation algorithm using an adaptive neuro-fuzzy inference system (ANFIS) for unmodeled dynamics is presented. At first, the unmodeled dynamics is divided into two parts using the differential expansion of the control input at the last time instant; then, the two parts are estimated by the ANFIS. It has been shown that the proposed algorithm overcomes the problem that the unknown control input is embedded in unmodeled dynamics, which makes the true value of unmodeled dynamics difficult obtain. Moreover, the method improves the precision of the estimation of unmodeled dynamics. Second, under the assumption that the growth rate of unmodeled dynamics does not exceed its input vector, the “one-to-one mapping” and “regularization technique” are adopted to deal with the input and output data and the unmodeled dynamics, respectively. As a result, the data vector can be guaranteed to lie inside a compact set, which ensures the use of the universal approximation property of the ANFIS. On the other hand, it has been shown that datum of a system can be fully used to obtain the parameters (centers, widths) in membership functions and the network connection weights in the ANFIS by offline training. These parameters are tuned online to improve the estimation convergence rate of the unmodeled dynamics. The effectiveness of the proposed estimation method is illustrated by comparing it with the simulation results that are obtained from the other existing methods. Finally, the proposed estimation method is applied to the nonlinear switching control design. Both simulation and theoretical analysis have confirmed that the nonlinear switching control which adopts the proposed estimation method cannot only guarantee the stability and convergence of the system but can exhibit a desired dynamic performance for the closed-loop system as well.

The high conventional accuracy of pattern recognition-based surface myoelectric classification in laboratory experiments does not necessarily result in high accessibility to practical protheses. An obvious reason is the effect of signals of untrained classes caused by the relatively small training dataset. In order to make the classifier robust to untrained classes, a classification scheme is developed based on boosting and random forest classifiers in this paper. Meanwhile, a threshold, the post probability of the prediction, is introduced as a balance (i.e., adjust) between the accurate classification and the rejection of the samples belonging to some untrained classes. The experiments are conducted to compare with other two schemes using linear discriminant analysis and support vector machines. Surface electromyogram signals, labeled with seven isometric movements, are collected from six healthy subjects' forearm. It is shown that the proposed scheme can reach up to about 92% accuracy in recognizing trained classes and 20% for untrained classes. Through adjusting the threshold, the accuracy of rejecting untrained classes reaches up to around 80%, with small decrease in recognizing trained classes (down to 80%). In the analysis of experiments' results, we also find that the proposed scheme has better error distribution among the classes.

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

The exhibition of hysteresis effects in smart actuators highly affects the accuracy and stability of the control systems. In this work, a modified generalized PI (MGPI) model is proposed to describe a more general class of hysteresis shapes. Comparing to the previous works, the MGPI model not only enlarges the application of the PI model, but also makes it possible to derive the analytical inverse model. The inverse MGPI model can be used as a compensator to mitigate the hysteresis effect in the control systems. Furthermore, in order to minimize the inverse compensation error due to the modeling inaccuracy and to achieve the closed-loop stability and tracking precision, an adaptive variable structure controller is designed. The simulation results show that the proposed controller consisting of both the inverse compensator and adaptive controller has superior control performance comparing with the adaptive controller itself.

Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making.

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

Piezo micro-positioning actuators have been widely used in micro-positioning applications due to the fast expansion, high force generation, and unlimited resolution. However, these actuators exhibit some rate-dependent hysteresis effects which affect the accuracy of these micro-positioning systems and may even lead to system instability. In this paper, the rate-dependent Prandtl-Ishlinskii model is employed to characterize the rate-dependent hysteresis nonlinearities of a piezo micro-positioning stage. The analytical inverse of the rate-dependent Prandtl-Ishlinskii model is then formulated using the initial loading curve concept. This inverse is utilized as a feedforward compensator to compensate for the hysteresis nonlinearities of a piezo micro-positioning stage under excitation in the 1-50 Hz frequency range.

In this paper, adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time-varying state delays and uncertain hysteresis inputs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design, and the effect of the uncertain hysteresis with the Prandtl-Ishlinskii (PI) model representation is also mitigated using the proposed control. By utilizing the integral-type Lyapunov function, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded (SGUUB). Extensive simulation results demonstrate the effectiveness of the proposed approach.

In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

Smart actuators such as Shape Memory Alloy actuators, magnetostrictive actuators, and piezoceramic actuators show different symmetric and asymmetric hysteresis loops. Shape Memory Alloy actuators and magnetostrictive actuators, as an example, exhibit saturated output at maximum and/or minimum input. In this paper, a generalized Prandtl-Ishlinskii model is formulated to characterize hysteresis nonlinearities of different Smart actuators. In this model, a generalized asymmetric play hysteresis operator is proposed and integrated with a density function to characterize different asymmetric hysteresis loops of smart actuators. This modified play hysteresis operator shows the capability to generate minor and major hysteresis loops with varying slopes of ascending and descending input-output curve. Moreover, this operator exhibits saturated major and minor input-output relationships. The capability ability of the proposed model to characterize hysteresis loops of different smart actuators is demonstrated by comparing its output with measured saturated symmetric and asymmetric hysteresis loops of SMA actuators and magnetostrictive actuators.

Local L2gain method has been pursued to analyze the robustness of a family of stabilizing controllers near the rotating stall point in axial flow compressors. An matrix inequality is involved in computation of L2gain which is usually very difficult to solve without resorting to numerical method. In order to reduce the computational cost, an algorithm is proposed in this paper to greatly simplify the computational complexity by converting the matrix inequality into its quadratic form. Rather than obtaining accurate approximation of the local L2gain, the results would facilitate the robust design of the axial flow compressor control.

This paper concerns a problem of robust Hinfincontrol for a class of uncertain nonlinear networked control systems (NCSs), which can be represented by a T-S fuzzy model with uncertainties. Both network-induced delay and packet dropout are addressed. The controller design method is presented based on a delay-dependent approach, and the robust Hinfincontroller gain matrices are obtain by solving a set of linear matrix inequalities (LMIs). Moreover, a general Lyapunov-Krasovskii functional is used, and some slack matrices, which bring much flexibility in solving LMIs, are introduced during the proof. Simulation results show the validity of the proposed method.

In this paper, robust adaptive control strategy is presented for a wheeled mobile robot in the presence of model perturbations that violates the nonholonomic assumption. The nonholonomic constraint of the vehicle is assumed to be violated by an unknown slippage. Consequently, a perturbed kinematic model of the system is obtained. Using backstepping, the proposed controller is constructed at the dynamical level. The robust adaptive controller is to eliminate the needs for the LIP form of the system dynamics and the exact bounds of the system dynamics. All the system states are shown to be able to track the desired trajectory. The simulation results demonstrate the effectiveness of the proposed controllers.

This paper addresses the design of position tracking control of mechanical systems with classical nonholonomic constraints incorporating actuator dynamics. A robust adaptive controller is designed at the actuator level and the controller guarantees that the configuration state of the system tracks the desired trajectory. A detailed numerical example is presented to illustrate the developed method.

This paper proposes a stable motion tracking control law for mechanical systems subject to both nonholonomic and holonomic constraints. The control law is developed at the dynamic level and can deal with model uncertainties. The proposed control law ensures the desired trajectory tracking of the configuration state of the closed-loop system. A global asymptotic stability result is obtained in the Lyapunov sense. A detailed example is presented to illustrate the proposed method.

By using a state observer, a new robust trajectory tracking control scheme is developed in this paper for electrically driven robot manipulators. The role of the observer is to estimate joint angular velocities. The proposed controller does not employ adaptation, but assures robust stability of tracking error between joint angles and desired trajectories. At sacrificing asymptotical stability of the tracking errors, the configuration of the proposed controller becomes very simple, compared with regressor-based adaptive controllers. It is shown in the closed-loop system using the proposed controller that the Euclidian norm of tracking errors arrives at any small closed region with any convergent rate by setting only one design parameter. Especially for the desired trajectories converging to constant ultimate values, it is assured that tracking errors converge to zero.

Wing rock is a highly nonlinear phenomenon in which the aircraft undergoes limit-cycle roll oscillations at high angles of attack (AOA). In this paper, a simple fuzzy PD control method is employed for wing-rock suppression and tracking because fuzzy PD controller has the same performance as the conventional PD controller for linear processes, yet improves the control capability for nonlinear and uncertain processes. Simulations at various initial conditions and different AOAs demonstrate the effectiveness and robustness of the proposed scheme. Comparison with other fuzzy PD controllers in literatures is also conducted. It shows that the proposed fuzzy controller can control wing-rock with complete and fast control effect in a wide range of AOA.

This paper deals with adaptive control of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is described by a dynamic equation. By utilizing this dynamic model and by combining a fuzzy universal function approximator with adaptive control techniques, a stable adaptive fuzzy control algorithm is developed without constructing a hysteresis inverse. The stability of the closed-loop system is shown using Lyapunov arguments. The effectiveness of the proposed method is demonstrated through simulations.

The position/force tracking control of Lagrangian mechanical systems with classical nonholonomic constraints is addressed in this paper. The main feature of this paper is that 1) control strategy is developed at the dynamic level and can deal with model uncertainties in the mechanical systems; 2) the proposed control law ensures the desired trajectory tracking of the configuration state of the closed-loop system; 3) the tracking error of constraint force is bounded with a controllable bound; and 4) a global asymptotic stability result is obtained in the Lyapunov sense. A detailed numerical example is presented to illustrate the developed method.

In this paper, a global stable fuzzy observer-based fuzzy output control scheme is presented to achieve output tracking control of Takagi-Sugeno fuzzy systems. The stability of the entire closed-loop fuzzy system is ensured by the Lyapunov stability analysis. The simulation example is given to verify the theory.

When using the Lyapunov synthesis approach to construct an adaptive fuzzy control system, one important way is to regard the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled. Concerning the unknownness, generally there are two cases: a completely unknown case, and a partly unknown case. However, most of the schemes presented so far have only focused on the former. Clearly, if an unknown function belongs to the latter, the knowledge available about the function should be utilized as much as possible in the development of the control system. In the paper, our goal is to design an adaptive fuzzy controller for a class of nonlinear systems with uncertainty, which can correspond to either case. Also, we propose a unique way to deal with the uncertainty, i.e., adopt a switching function with an alterable coefficient, which is tuned by adaptive law based on the tracking error.

An adaptive feedback control scheme for the tracking of a class of discrete-time nonlinear plants is presented. A fuzzy approximator is used to adaptively compensate for the plant nonlinearities. The fuzzy approximator's parameters including nonlinear ones, are adapted using a Lyapunov-based design. The method uses a special approximation error expression, parameter projection, and variable structure control with a sector to achieve uniform ultimate boundedness.

Recently, through the use of parameterized fuzzy approximators, various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood. An important class of parameterized fuzzy approximators is constructed using radial basis function (RBF) as a membership function. However, some tuneable parameters in RBF appear nonlinearly and the determination of the adaptive law for such parameters is a nontrivial task. In this paper, we propose a new adaptive control method in an effort to tune all the RBF parameters thereby reducing the approximation error and improving control performance. Global boundedness of the overall adaptive system and tracking to within a desired precision are established with the new adaptive controller. Simulations performed on a simple nonlinear system illustrate the approach.

The well-known gradient descent based learning strategy is considered as a feedback control system. In order to improve the convergence performance of the gradient based learning, both a proportional+integral+derivative (PID) control based gradient descent learning and a fuzzy control plus gradient descent learning strategies are proposed. The convergence properties of the proposed methods are discussed. The comparison investigation concerns the gradient technique and the proposed approaches are finally illustrated.

The position/force control of mechanical systems subject to a set of Pfaffian constraints is addressed in this paper. A reduced order dynamical description of such nonholonomic mechanical systems, including the constraints, is developed. Some properties of the dynamic model are then exploited to facilitate the controller design. Based on the theory of guaranteed stability of uncertain systems, a robust control algorithms is derived, guaranteeing the uniform ultimate boundedness of the tracking errors. A detailed numerical example is presented to illustrate the developed method.

This paper addresses the problem of controller design for constrained robots with the consideration of computational efficiency. An adaptive control algorithm, based on the reduced dynamics formulation, is presented for trajectory tracking of an end-effector on a constrained surface with specified constraint forces. It is shown that the control objective can be achieved without exact knowledge of robot dynamics and online calculation of nonlinear dynamic functions. The control algorithm is constructed with at most four control parameters to be adjusted adaptively, and that number is determined independently of the number of degrees of freedom of the robotic manipulators.

In the above paper by Su-Leung (IEEE Trans. Robot Automat., vol.9, no.2, p.208-14 (1993)), an adaptive sliding mode control scheme for the trajectory control of robot manipulators is developed. The proof of Theorem 1 in the paper is accomplished by resorting to the Lasalle theorem. It is well known that the Lasalle theorem can be applied to either autonomous systems or periodic nonautonomous systems, but it is not applicable to general nonautonomous systems. The Lasalle theorem is not applicable to the manipulator tracking control system given in the paper because the tracking control system considered in the paper is neither an autonomous system nor a periodic nonautonomous system but it can be classified into general nonautonomous systems. Therefore, the proof of Theorem 1 in the paper is not correct.>

In this paper a hybrid adaptive/robust control scheme is proposed for rigid-link electrically-driven robot manipulators in the presence of arbitrary uncertain inertia parameters of the manipulator and the electrical parameters of the actuators. In contrast to the known methods, the presented design requires at most the joint velocity feedback and does not rely on the knowledge of the bounds of complexity functions. Semi-global asymptotic stability of the adaptive/robust controller is established in the Lyapunov sense. Simulation results are included to demonstrate the tracking performance.>

When a common object, attached to multiple robot arms, is cooperatively manipulated to move along a constrained surface, the control task requires the simultaneous control of the motion trajectory of the attached object on the constrained surface; the constrained force due to the contact with the surface; and the internal force exerted by the arms on the object. To accomplish such a control objective, an adaptive sliding mode control algorithm is presented by developing a new concise dynamic model of the system and exploiting its particular properties. Detailed analysis on the tracking properties of the object's position, the constrained force, and the internal force are given. The stability analysis shows that the proposed algorithm can achieve satisfactory tracking performance.>

In this paper the design of adaptive control of rigid-link electrically-driven robot manipulators is addressed in the presence of arbitrary uncertain inertial parameters of the manipulator and the electrical parameters of the actuators. In contrast to the known methods, the presented design requires at most the joint velocity feedback and does not rely on the knowledge of the bounds of complexity functions. Global asymptotic stability of the adaptive controller is established in the Lyapunov sense. Simulation results are included to demonstrate the tracking performance.>

An adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems, for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs fuzzy systems, which are expressed as a series expansion of basis functions, to adaptively compensate for the plant nonlinearities. Global asymptotic stability of the algorithm is established in the Lyapunov sense, with tracking errors converging to a neighborhood of zero. Simulation results for an unstable nonlinear plant are included to demonstrate that incorporating the linguistic fuzzy information from human experts results in superior tracking performance.>

Based on a general form for a sliding manifold, two design strategies for adaptive sliding mode control are presented for nonlinear robot manipulator systems. It is shown that, without any prior knowledge of the manipulator parameters under the deterioration of a class of state-dependent input disturbances, the controllers guarantee the occurrence of the sliding mode on the general sliding manifold, which can be a nonlinear or time-varying manifold. In the sliding mode, the desired performance can be obtained by selecting a suitable sliding manifold, which is illustrated, for example, by a nonlinear manifold.

A sliding-mode control algorithm combined with an adaptive scheme, which is used to estimate the unknown parameter bounds, is developed for the trajectory control of robot manipulators. The major contribution of this methodology lies in the use of a special matrix, called the regressor, which makes it possible to isolate the unknown parameters from the robotic dynamics. Based on the upper bounds of those unknown parameters, which are estimated by a simple adaptive law, the proposed VSS (variable-structure-system) controller guarantees the stability of the closed-loop system. The robustness analysis shows that in the presence of the uncertainties, which are assumed to be unbounded and rapidly varying, the closed-loop system can still be stabilized. Chattering is reduced by using the boundary layer technique. Simulation results show the validity of the proposed algorithm.>