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In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

Commonly used variants of the proportional-integral, PI, and the integral-proportional, IP, compensator gains selections, merged with the $\mathfrak {D}$ -decomposition technique are presented in this paper. The motivation to this work is the willingness to check whether such a combination can lead to unified and perhaps simplified approach in this matter. Therefore, the $\mathfrak {D}$ -decomposition technique has been effectively combined with the frequency- and the time-domain driven requirements regarding the control dynamics. Criteria such as the gain- and phase-margins, $(GM, PM)$ , the sensitivity, $M_{\text {S}}$ , the pole placements by means of the $(\sigma, \omega _{\text {d}})$ and the $(\xi, \omega _{\text {n}})$ , and the overshoot with the rise time $(\delta, t_{\text {r}})$ are considered. It has been shown that the control design effort can be reduced by the means of the $\mathfrak {D}$ -decomposition to intuitive judgments in the proportional and integral gains coordinates $(K_{\text {P}}, K_{\text {I}})$ with parametric curves. As such it can be thought of as a promising scenario in a case considered. The analyses are presented and discussed in details. They are conducted basing on example of output voltage closed loop control of the Dual Active Bridge, DAB, converter. The circuit operates under the phase shift control scheme. The control-to-output transfer function identification and the control circuit delays are included in the analyses. The case analysis have shown that the time domain requirements are less effectively met with the PI regulator when compared to its IP configuration. This is due to the commonly used simplifications during conversion between the $(\xi, \omega _{\text {n}})$ and the $(\delta, t_{\text {r}})$ in presence of uncompensated zero of the closed loop transfer function. The paper contains complete and intelligible approach to dedicated mathematical investigations verified experimentally.

In this paper, a quaternion-based adaptive dynamic surface control method is proposed for attitude tracking control for small-scale unmanned helicopters with external disturbance and uncertain dynamics. The quaternion formalism is introduced and a quaternion-based multi-input-multi-output nonlinear model is derived from the attitude dynamics of a small-scale helicopter. The low-complexity controllers are designed by the dynamic surface control method as it eliminates the problem of the explosion of items. The singularity problem is avoided by substituting Euler kinematic equations in the nonlinear model with quaternion expressions and integrating the quaternion expressions into the design process of the dynamic surface control. For improving the robustness of the control system, the radial basis function networks are applied to approximate the uncertain dynamics. The external disturbance is also compensated in the controllers’ design. This paper proves that the proposed method can guarantee the uniformly ultimate boundness of this attitude system. Simulation results are presented finally and show the effectiveness of this control approach.

Pitting corrosion of rotating machinery is one of the most common faults in industrial engineering. The convolutional neural network (CNN) is increasingly applied to the fault diagnosis. However, the conventional CNN method will reduce the feature dimension of the collected signal and cause the loss of information during the pooling process. In this paper, a new method based on dilated CNN combined with spatial dropout (DCSD) is proposed to diagnose the early faults of rotating machinery. By filling the convolution kernel, the DCSD method can increase the receptive field of the CNN without increasing the number of parameters while retaining more features of the raw vibration signal of the rotating machine. To avoid the dropout method eliminates the adjacent elements with a strong correlation, the Spatial Dropout method is adopted to reduce the overfitting problem of deep networks. The early pitting gears experiment was designed to verify the DCSD method in this paper. The raw vibration signal data of 6 different healthy states were collected to verify the effectiveness of the method. The experimental results show that the DCSD method proposed can effectively distinguish the different early gears pitting, and the diagnostic accuracy is better than other popular deep learning methods.

Aero-turbines usually work in various non-stationary and harsh operating environments. Its blades easily appear early crack faults in long-time operation, and it is difficult to dig out discriminant features of early crack. To solve these problems, in the paper, a novel intelligent approach for early crack diagnosis of turbine blades using three-dimensional blade tip clearance is presented. In order to improve feature learning ability to obtain better generalization ability, the paper firstly develops a novel deep learning method based on deep belief networks (DBNs). Considering the fact that the feature degradation easily occurs in deeper layers because of the change of the distribution in each layer's outputs with the increase of the layers, it is hard to decide which layer to learn features is useful for fault diagnosis. Accordingly, in the pre-training process, the global back-reconstruction (GBR) mechanism is introduced into DBNs to optimize the feature learning ability. The GBR mechanism can be realized between the input layer and hidden layers by “shortcut connection”, and the layer to learn more discriminant features can be determined automatically without prior knowledge. Moreover, due to three-dimensional blade tip clearance (3-DBTC) acquired from three different directions of turbine blades contains much more useful crack failure multiscale information, it is suitable to be used as an input from which to extract multiscale discriminant features. Eventually, in the supervised training, the softmax regression model is employed to classify the health conditions of turbine blades using these sensitive features learned from 3-DBTC. The experimental results show that the proposed method can effectively identify the crack of turbine blades with fairly high diagnostic accuracies and significantly outperform other methods considered in the paper.

This paper discusses the problem of fault estimation for two-dimensional (2-D) Fornasini-Marchesini (FM) dynamical systems. The objective of this paper is to design a fault estimation filter to reconstruct the characteristics of faults in low-frequency domain and satisfy different performance levels. Utilizing the generalized Kalman-Yakubovich-Popov (GKYP) lemma, this problem is transformed into a multi-objective optimization problem, which is non-convex in essence. On this basis, an optimization algorithm is proposed to solve the non-convex optimization problem. Sufficient conditions are derived for the proposed fault estimation filter. Finally, simulation results show the effectiveness of the theoretical results.

In this paper, a fault-tolerant control (FTC) method for robotic manipulators is proposed to deal with the lumped uncertainties and faults in case of lacking tachometer sensors in the system. First, the third-order sliding mode (TOSM) observer is performed to approximate system velocities and the lumped uncertainties and faults. This observer provides estimation information with high precision, low chattering phenomenon, and finite-time convergence. Then, an FTC method is proposed based on a non-singular fast terminal switching function and the TOSM observer. This combination provides robust features in dealing with the lumped uncertainties and faults, increases the control performance, reduces chattering phenomenon, and guarantees fast finite-time convergence. Especially, this paper considers both two periods of time, in which before and after the convergence process takes place. The stability and the finite-time convergence of the proposed controller-observer technique is demonstrated using the Lyapunov theory. Finally, to verify the effectiveness of the proposed controller-observer technique, computer simulation on a serial two-link robotic manipulator is performed.

Repetitive controllers (RCs) are known for their ability of controlling periodic exogenous signals, even if these signals have high harmonic content. Due to the variety of repetitive controllers proposed in the literature, which are significantly different from each other, a comparative evaluation in a collective way is analytically complex. This fact implies a greater difficulty to select the appropriate RC strategy when designing a control system, what makes most control system designers to not use this class of controllers. In order to solve this problem, the present paper develops a unified approach for representation of (real and complex) repetitive controllers, which is based on the use of multiple primitive repetitive cells in parallel. Through this approach the main characteristics of a repetitive controller, such as stability properties, dynamic response, set of harmonic components that are effectively compensated and computational burden, are easily identified. Furthermore, in order to validate the potential of the proposed unified approach, comparative studies of $nk \pm m$ RCs and $nk+m$ RCs are performed. An experimental application based on a three-phase shunt active power filter is implemented to validate the theoretical evaluation presented in this paper.

This paper deals with the problem of grid map merging in multi-robot SLAM (simultaneous positioning and mapping) where the initial relative pose between robots is unknown. When robots encounter each other, it is easy to obtain a map transformation between robots for grid map merging if bilateral observation measurements are available between robots. However, since the bilateral observation measurements are obtained by encounters between robots, they may limit the availability of using multi-robot systems. To overcome the limitation, spectra-based map merging can be applied without any observation measurements between robots. However, it requires sufficient overlapping areas between indivisual maps of robots, which can also limit the availability of using multi-robot systems. In this paper, therefore, to overcome both limitations, an extension of spectra-based map merging using not bilateral but unilateral observation measurements. The proposed method was tested with datasets obtained from real experiments with mobile robots equipped with a sensor fusion system which can obtain unilateral observation measurements to other robots. Experimental results showed that the proposed map merging method works successfully without any bilateral observation measurements.

This paper presents a hybrid control strategy which combines the nearest level control technique with PWM control technique on a cascaded H-Bridge Multilevel Inverters (MLI). MLIs have become very popular due to its several advantages in the application areas like industrial drives and grid-connected renewable energy generation systems. With the reduced number of switches, driver circuits, conduction losses, switching losses, voltage stresses, size, and cost of the system, MLI has outperformed its two-level counterpart in terms of power quality issues. Reduction in the Harmonics and consequently the Total Harmonic Distortion (THD) in the output voltage has added to the benefits of MLI. Topological advancements in MLI require a satisfactory output voltage control strategy. In this work, a hybrid control strategy for a smooth and wider range of control of output voltage is proposed. The hybridized scheme enhances the control range of the voltage considerably. Moreover, the THD is also reduced compared to the conventional sinusoidal PWM scheme. A thorough analysis of the scheme has also been presented in the paper. SIMULINK/ MATLAB environment is used for testing the simulation model of the proposed control scheme. This hybridized controlling technique is also simulated for thermal modelling on PLECS software and power loss analysis is performed. The mathematical and simulation analyses are validated on an experimental prototype using a TMS320F28335 DSP controller card. The hybrid scheme has also been tested for a fault tolerant model of the cascaded H-bridge inverter. The performance of the hybrid scheme under different fault condition has also been shown to work satisfactorily.

The conventional methods of observer poles placement in sensor fault detection usually adopt the trial-and-error methods. These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. Therefore, this paper proposes a fuzzy-based observer tuning method to optimize and adapt the selection of poles locations to determine the optimal gains of the observer, and it is experimentally applied to a composite sensor fault detection. Fuzzy logic is a promising method that could overcome the trial-and-error method challenges by introducing better adaptation and system robustness. The proposed observer structure includes adaptive tuning corresponding to an unknown input. Utilizing self-tuning for the observer correction stage, the gain is going to be updated online using the proposed fuzzy adaptive poles placement (FAPP) system. This paper validated the system simulation by implementing fault detection algorithms by using a real-time embedded observer-based system. The experimental results demonstrate the effectiveness of the proposed fuzzy-based observer schemes at detecting sensor faults in the Brushless DC (BLDC) motors, with significantly better performance than conventional counterparts’ methodologies. The experiments indicate that the average estimation error is 0.146, which less by 43.8% than was obtained for high levels of noise and disturbances compared with the traditional Luenberger observer approach.

This paper is concerned with the stability analysis problems of asynchronous sampled-data systems with use of a input-delay approach, where a sampled-data system can be reformulated into a time-delay system having incremental delays. For these systems, this paper introduces a new looped-functional to utilize both integral states and their interval-normalized ones. Utilization of these two types of integral state variables in a construction of stability conditions has been effective in reducing the conservatism. Further, generalized equalities are proposed for the case utilizing these two types of integral states. The sampled-data system formulation consists of a sampled state, a system state and its derivative. Here, the sampled state between two consecutive sampling instants is a constant value and thus can be eliminated by an integration with Legendre polynomials of positive degrees. Consequently, the system formulation turns into the equalities consisting of state variables, sampled-state variables, integral state variables and their interval-normalized ones without additional slack matrices. Based on the proposed equalities, a large computational burden can be reduced while reducing the conservatism. The effectiveness of the proposed approaches is demonstrated via three numerical examples for the stability analysis of asynchronous sampled-data systems.

Accurate characterization of keyhole pool behavior is important to improve the penetration recognition and quality detection during the variable polarity plasma arc welding (VPPAW) of aluminum alloy. However, the low-level hand-crafted visual/acoustic features are often incapable of sufficiently representing the dynamic characteristics of keyhole pool under complex welding conditions. In this paper, we developed an end-to-end visual-acoustic penetration recognition (VAPR) framework based on a hybrid convolutional neural network (CNN) and extreme learning machine (ELM), which consists of three consecutive phases: (1) visual-acoustic data preparation, (2) multi-features extraction, and (3) penetration classification. Specifically, we applied a flexible dual-sensor acquisition system for synchronously collecting/preprocessing the visual-acoustic signals and exploring the internal correlation between the visual-acoustic features and keyhole pool behavior as well as weld penetration status. Then we established two individual CNNs for learning high-level visual-acoustic features directly from the raw visual images and 2-D time-frequency spectrograms. With the visualization of CNN-based deep learning features, we also explained the physical meanings of visual-acoustic features. To further improve the prediction performance of CNN model, we employed the ELM model as a strong tool to classify penetration status. By comparing with other state-of-the-art methods, our hybrid CNN-ELM approach with visual-acoustic fusion has a superior performance in terms of the classifying accuracy. The VAPR framework in this paper will provide some guidance for post-process quality inspection and realize an adaptive control of VPPAW process.

This paper investigates linear active disturbance rejection control (ADRC) for processes with time delay. In the past years several modified active disturbance rejection control methods, including Smith predictor based ADRC (SP-ADRC), predictor observer based ADRC (PO-ADRC) and delayed designed ADRC (DD-ADRC) have been proposed to tackle systems with time delay. In this paper it is shown that these modified ADRCs can be interpreted in the framework of a two-degree-of-freedom (TDOF) internal model control (IMC), so the analysis and design can all be done via the well-known IMC framework. With the aid of the TDOF-IMC framework, the three modified ADRCs are compared and some interesting conclusions are drawn. Analysis and simulation results show that PO-ADRC structure is the best delay compensation structure among the three methods. However, the overall performance of the three methods will also depend on the tuned parameters, and the robustness measure is helpful in tuning the parameters to achieve compromise in performance.

With the construction of periodic functions relative to the meshing frequency, rotating frequency, and the faulty frequency, etc., the phenomenological model provides a simple and effective way of representing the vibration signal of the planetary gearbox. However, due to that the meshing vibration is simplified, the traditional model can only reflect the modulation characteristic of the signal, but not the impact characteristic. Therefore, an improved phenomenological model is proposed, which can satisfy these two characteristics of the vibration signal at the same time. To consider the meshing vibration correctly, two key points should be determined: occurring moment of impact signals, and their relative amplitudes. In order to calculate the occurring time, this paper proposes modifying the reference point of the meshing phase and using the modified phases to represent it. Then two experiments are designed to verify the correctness of the modified phases and the occurring moment. Fortunately, these experimental results also provide a factual basis for determining the relative amplitudes of these impact signals. Subsequently, signal model of the gearbox in the healthy state is established based on the meshing vibration characteristics. The simulation results show that the new model can satisfy the modulation and impact characteristics. In addition, combined with the time-varying meshing stiffness of the gear pair, the paper theoretically analyzes the influence of fault type, location, and size on the impact frequency in a fault meshing period. Finally, the correctness of the new phenomenological model is verified by experiments.

Improving robot performance while simultaneously ensuring compliance and human safety has been appealing for a long time and remains a challenge. To this end, we propose a new approach for kinematic control of redundant manipulators to deal with multiple prioritized tasks and at the same time produce a novel compliance behavior in the null space of main task. Different from typical Voigt model based compliance control methods, the compliance control approach proposed in this paper is based on Maxwell model, which shows superior performance on impact absorption and contact reducing than its counterparts. In comparison with typical compliance control, the annoying and even harmful return force is removed and human comfortableness can be improved consequently. Besides, this novel compliance control method is implemented in the null space of higher priority task without disturbing the main end-effector task. The effectiveness of our approach has been practically evaluated and verified in experiments on a 7 DOF redundant collaborative robot manipulator. A novel cushion-like and plastic deformed whole body compliance has been realized while the continuity and quality of robot main task have been maintained. Promisingly, the approaches proposed in this paper are general and manipulator independent, which can also be applied to other emerging collaborative and even hyper redundant manipulators. Besides, research results of this work can also potentially inspire other robotic research related with human robot friendly interaction and collaboration.

In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.

In this paper, an idea about the hydraulic loading device is proposed to simulate the working mechanical environment of the steam valve joystick. The simulation based on the mathematical model of active and passive loading system is utilized to reveal that the stiffness of connection components in loading system has an important influence on the control accuracy of the system. Additionally, the paper provides a design on the structure of the connecting components of valve stem. For the control strategy, a dual inertia link is used to stabilize the system. Besides, a compensation method of partial structural invariance is adopted to reduce the influence of redundant force in passive loading test. Finally, The development mode of control engineering based on xPC rapid control prototype model is used to validate the loading system. The simulation and experimental results show that the dual inertia link and compensation method are effective techniques, and the passive loading error can be controlled within the tolerance of 300N.

This paper proposes an extension to the established loop-shaping method, where the loop-shaping method is modeled mathematically and solved as a mathematical optimization program. In this process, a novel cost function is proposed and employed as the objective of optimization in the controller tuning problem. This cost function evaluates different characteristics of the system in the frequency domain, such as the stability margins and the operability bandwidth. These characteristics reflect the quality of load disturbance rejection, set-point tracking, and operability bandwidth of the control system. Consequently, the proposed technique circumvents running a time-domain simulation completely. More importantly, the proposed technique can be used with any controller structure. The generic cost function can be customized to fit any control objective and application. This is conducted in the second half of this paper, with the aim of obtaining a system with near-optimum time-domain performance with respect to the integral of time times absolute error (ITAE) criterion. At the same time, the control system obtained with the customized cost function has better operability bandwidth than that of a system optimized in the time-domain for the ITAE criterion. The proposed optimization model is analyzed for two plant models: first order and second order plus dead-time (FOPDT), (SOPDT), respectively.

This paper proposes a direct adaptive neural control law for a class of affine nonlinear multi-input-multi-output (MIMO) systems of the form $\dot {x}=f(x)+G(x)u$ using feedback linearization when both $f(x)$ and $G(x)$ are to be estimated. It is challenging to estimate $f(x)$ , a vector, and $G(x)$ , a matrix, to be used in synthesizing the control law, simultaneously, because of the dimensional inconsistency arising with the available neural structures, which do not have multiple layers of outputs. This problem is addressed in this paper by exploiting the power of matrix vectorization and reshaping techniques using the Kronecker product. The strategy may be visualized as equivalent to the neural structure consisting of multiple layers of outputs that result from the appropriate manipulation of matrices corresponding to the proposed estimations. The weight update laws, for both the radial basis function neural networks that estimate both $f(x)$ and $G(x)$ , are derived such that the proposed control law achieves the twin objective of the derived tracking performance as well as closed-loop system stability in the sense of Lyapunov. The ratios $\alpha $ and $\beta $ are proposed in line with the widely used concept of Rayleigh’s quotient adopted in structural dynamics to evaluate the natural frequency of a system. The simulation results obtained from the use of a twin rotor MIMO system are presented here to demonstrate the feasibility and effectiveness of the proposed control law. The superiority of this approach lies in the development of suitable control law for a MIMO system in the absence of knowledge about the nonlinearities.

The impedance-ratio criterion is widely adopted for analyzing the small-signal stability of systems with cascaded power electronic converters. However, the impedance ratio is formed differently for different cascaded systems. In this paper, a generic impedance-sum criterion is proposed for the stability of general power electronic systems with two converters/sources. The two converters/sources can be in parallel operation, such as parallel-operated voltage-controlled inverter systems in microgrids and parallel-operated current-controlled inverter systems, or in series (cascaded) operation, such as cascaded DC/DC converter systems and grid-connected current-controlled converters. In this paper, it is shown at first that in order for a converter/source to be stable both the impedance and its inverse should be stable, that is, the impedance should not have any right-half-plane (RHP) zeros or RHP poles. Then, it is shown that a system with two individually stable converters/sources is stable if and only if the sum of the individual impedances does not have any RHP zeros or, equivalently, the impedance sum does not encircle the origin clockwise. This generic criterion is then demonstrated via applications covering all four possible cases. Published version

This paper is devoted to the distributed optimization problem of heterogeneous multi-agent systems, where the communication topology is jointly strongly connected and the dynamics of each agent is the first-order or second-order integrator. A new distributed algorithm is first designed for each agent based on the local objective function and the local neighbors’ information that each agent can access. By a model transformation, the original closed-loop system is converted into a time-varying system and the system matrix of which is a stochastic matrix at any time. Then, by the properties of the stochastic matrix, it is proven that all agents’ position states can converge to the optimal solution of a team objective function provided the union communication topology is strongly connected. Finally, the simulation results are provided to verify the effectiveness of the distributed algorithm proposed in this paper.

This paper considers the problem of load frequency control (LFC) design for multi-area power systems. Owing to the multi-area power system with a feature of spatial distribution, it becomes necessary to use the network as a signal transmission medium. The following two problems need to be addressed: 1) the transmission signals transmitted over the communication network are vulnerable to be attacked by adversaries. How to ensure the security of the control system and 2) multi-area power systems have a large amount of data need to be exchanged via the network, how to guarantee the network quality of service and the control performance under the limited network bandwidth. To address these problems, in this paper, a resilient output feedback control strategy in secure means is proposed for LFC systems based on the security criteria. Moreover, a new event triggered mechanism (ETM) is developed, under which the average data releasing rate is much lower than the one using the conventional data transmission mechanism, while it becomes high when the system encounters some external disturbances. To validate the effectiveness of the proposed approach, simulations are performed on a 3-area power system. The results show that the proposed control strategy can maintain the LFC system with a satisfactory control performance in the presence of stochastic cyber-attacks, and the average data releasing rate is of a lower level to save communication and computation resources.

This paper aims at providing two competitive solutions to the problem of estimating state as well as sensor and actuator faults. The development starts with a common system description, which is transformed into a nonlinear descriptor system-like form. The appealing property of such an approach is that the sensor fault is eliminated from the output equation by suitably extending the state vector. Subsequently, a novel fault estimation scheme is proposed and its error dynamics are carefully described. Having such a unified framework, two competitive design procedures are proposed. One is based on the celebrated $\mathcal {H}_{\infty }$ while the other employs the quadratic boundedness strategy. With the estimators, the paper provides a unified framework for assessing the uncertainty of the resulting estimates in the form of uncertainty intervals. Thus, both solutions can be compared from the theoretical perspective. The final part of the paper contains a numerical simulation example concerning a twin-rotor system that clearly shows the comparison and performance of the above approaches. Subsequently, an experimental study concerning a multi-tank system using real data is reported.

The aluminum strands break and surface damage will gradually develop into irreversible destruction to the conductors and the whole transmission lines, and even lead to serious power outages. In order to find out the potential strands break and damage faults and prevent its further deterioration, a recognition method of conductor break and surface defects in transmission lines' unmanned aerial vehicle (UAV) inspection is presented in this paper. First, a conductor image is obtained by the UAV image acquisition system, and then, the conductor region is extracted by the adaptive threshold segmentation after the enhancement processing by the gray variance normalization method (GVN). Second, the conductor break is detected by the square wave transformation (SWT) of its grayscale distribution curves, which is simple and effective. Meanwhile, the conductor surface defects are identified by the projection algorithm of the GVN image of the conductor region. Finally, calculating the number of broken strands and filtering the suspect defects, the final fault diagnosis results can be obtained. We analyze the performance of the technology by a series of experiments, and the results demonstrate that the proposed method can measure the conductor break and surface defects faults with the average accuracy of 90.45% and 92.05%, respectively. The health condition of the conductor can be monitored based on the method presented in this paper, by which the safe operation of transmission lines can be guaranteed.

In this paper, the stability of continue-time switched positive delay systems (SPDSs) with all unstable subsystems is investigated. Based on the time-scheduled multiple co-positive Lyapunov-Krasovskii functional (MCLKF) method combined with fast average dwell time (ADT) techniques, a sufficient condition is obtained to ensure the underlying system is exponentially stable. Compared with the results in existing literature, our work has two advantages. On the one hand, the proposed method is independent of the maximum of corresponding elements of each subsystem matrix, which overcomes the limitation of the existing method. On the other hand, our result is less conservative than that of the previous literature. In addition, the stability condition of the system in this paper is given by matrix inequality, which can be solved by nonlinear programming. Finally, two simulation examples are provided to illustrate the correctness and effectiveness of the main result.

In this paper, a novel direct torque control (NDTC) scheme utilizing composite active vectors based on discrete duty ratio modulation method is presented for permanent magnet synchronous motor (PMSM) in industrial robot servo control system. The errors in the PMSM driven by conventional DTC (CDTC) are compensated by single active vector during each control period, leading to unsatisfied steady-state performance. To improve the operation performance of the industrial robot servo control system effectively, the errors in the PMSM are compensated independently in NDTC. Therefore, two active vectors should be applied to compensate flux error and torque error, respectively, namely flux active vector and torque active vector. In order to maintain the merit of the fast dynamic response in CDTC, the introduced active vectors should be selected from the fixed six active vectors supplied by a two-level voltage source inverter. To describe the scheme of NDTC clearly, the angle between the applied active vector and the stator flux linkage is used to represent the error compensational effect provided by the applied active vector, namely active angle. Additionally, the compensational effect is defined as active factor. Subsequently, the duty ratio value of the active vector applied time can be derived easily. To simplify the calculations of the duty ratio values, novel discrete calculation method for the active factor by hysteresis controllers is presented in this paper. The effectiveness of the proposed NDTC strategy is verified through the experimental results on a 100-W PMSM drive system.

This paper presents an adaptive position tracking controller for a special kind of tailsitter vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). The distinctive part of this VTOL aircraft is its thrust-vectoring propulsion system, which allows the aircraft to achieve long-range flight at high-efficiency. However, there are two main problems encountered in the control design for this aircraft in vertical mode. One problem is that the mass and inertia tensor are unknown parameters because of the fuel consumption. The other problem is that the thrust vector is bounded both in amplitude and orientation, leading to the boundedness of control input. In this paper, we use a complementary filter to estimate the mass and an adaptive law to compensate the error caused by inertia uncertainty. A Lyapunov-based control law with nested saturation function is proposed to solve the problem of input boundedness. Finally, the global asymptotic stability is proven for the simplified model and the effectiveness is illustrated through simulation.

This paper deals with the negative effects of the in-vehicle network on the integrated anti-slip control (ASC) and direct yaw-moment control (DYC) of all-wheel-independent-drive electric vehicles (AWID-EVs). In the integrated control design of the modern AWID-EVs, increasing control components, e.g., sensors, controllers, and actuators, are usually connected via an in-vehicle network, such as a controller area network (CAN), rather than the traditional point-to-point communication. However, the application of CAN would also bring about unexpected problems, e.g., signal asynchrony, multiple-package transmission, and signal delay, which may degrade the control performance and even destroy the stability of the system. This paper presents a co-design methodology to deal with all these challenges caused by CAN and guarantees a satisfactory vehicle dynamics performance. First, a hierarchical structure is designed for the integrated ASC and DYC control of AWID-EVs over CAN, and an active torque distribution strategy based on a well-known maximum transmissible torque estimation approach is adopted. Then, a scheduling-based communication idea is introduced to deal with all these problems caused by CAN. Third, a Lyapunov-based pole assignment theory is applied to estimate the parameter values in the scheduling design and to guarantee the satisfactory dynamic performance of the control system. A generalized linear quadratic regulator controller is designed for the system synthesis to ensure the tracking control of the vehicle. Finally, simulations and preliminary hardware-in-loop tests indicate that the proposed co-design methodology can deal with the negative effects of the in-vehicle network and ensure reliable vehicle dynamics performance.

In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the “feature extraction formula” is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal linearly independent vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance.

This paper develops a novel adaptive gain integral terminal sliding mode control with time-delay estimation to enhance the control performance of a pneumatic artificial muscle system. The main contribution of the paper is that the proposed control method can enable the benefits of both terminal sliding mode technique and an integral sliding mode approach. Thus, the controlled system not only achieves finite time convergence and robust performance but also attenuates the drawback of the reaching phase in the conventional sliding mode control approach. To develop the control algorithm, the mathematical of the pneumatic artificial muscle system is first design, which includes a nominal system and all uncertainties and disturbances in the system dynamics. Then, a backstepping terminal sliding mode is designed to achieve a finite time convergence of tracking errors in the nominal system. In addition, an integral terminal sliding mode approach is proposed to reject the uncertainties and disturbances. To enhance the control performance, a time-delay estimation is employed to approximate the nonlinearity and disturbance in the system and an adaptive gain scheme is coupled directly to estimate the ideal robust control gain, which can reduce the chattering phenomenon and increase the tracking accuracy. The stability of the controlled system is analyzed using Lyapunov theory. Moreover, the effectiveness of the proposed control algorithm is verified through a series of experimental tests on a developed pneumatic artificial muscle system.

In this paper, a nonlinear integrated multi-body dynamic (MBD) model of a deep-sea mining (DSM) system was developed. A particular nonlinear interaction model between the mining vehicle and the seabed sediment was derived. The fluid resistance and the resistance torque acting on the vehicle were obtained by the computational fluid dynamics (CFD) numerical simulations. In addition, a rigid-flexible coupled discrete element method (DEM) was adopted to model an extremely long mining riser efficiently and accurately. Developed by the C# program, a user-defined subprogram was intended to establish the DEM model of the riser. A motion control system model for trajectory tracking of the vehicle was developed in which the fuzzy control algorithm and the fuzzy adaptive PID control algorithm were used to control the speed and speed ratio of the tracks, respectively. Furthermore, the collaborative simulation between the dynamic model and the control model was achieved, and a coordinated motion mode for the total mining system was proposed and simulated. Accordingly, the trajectory tracking accuracy and slip of the vehicle, the spatial state change of the riser, and the interaction forces among subsystems were analyzed. This paper can provide a valuable theoretical basis and technical reference for the integrated design, performance prediction, and operation control of the practical DSM system.

In this paper, we propose a robust point cloud registration method for ground vehicles. Given the vast developments in the field of autonomous vehicles, the use of point cloud data has increased. The simultaneous localization and mapping (SLAM) algorithm is typically used to generate sophisticated point cloud maps. In the SLAM algorithm, the quality of the map depends on the performance of loop closure algorithms. The iterative closest point (ICP) algorithm is widely used for loop closure of the point cloud. However, the ICP algorithm might not work well for ground vehicles because it was originally developed for 3D reconstruction in computer vision field. Therefore, this paper proposes a method to find robust matching correspondences in the ICP algorithm on ground vehicle conditions. The performance of the proposed method is compared with other conventional methods by using KITTI open datasets. The source code is publicly released on the Github website.

Based on the parallel robotic manipulator, this paper proposes a motion control strategy for the novel robotic spine brace for spinal rehabilitation exercises. However, several shortcomings of this parallel robotic manipulator, such as dynamic coupling in joint space, low response frequency in roll and pitch directions, and bad influence of device’s gravity, result in bad effects on the performance of the robotic spine brace system. For solving these problems of parallel robotic manipulator, a new motion control structure, modal space dynamics-velocity feed-forward (MSDF) motion control strategy, is designed in this paper. A robotic spine brace system model and an actuator dynamic model are expressed using the Kane method. Stability of the robotic system with the MSDF control method is analyzed. For evaluating the performances of the proposed motion control structure, an experimental parallel robotic manipulator is built. Experimental results reveal that the presented MSDF motion control strategy can eliminate those disadvantages efficiently.

This paper describes a state-based approach for supervisor synthesis of discrete-event systems under partial observation, based on predicates and predicate transformers. We focus on the normality property and provide an iterative algorithm for state-based normality synthesis. A condition is provided to simplify the algorithm. To bridge the gap between language-based normality synthesis and state-based normality synthesis, we prove that their synthesis results are mutually consistent. This paper also aims to build a useful foundation for non-blocking supervisor synthesis of partially-observed state tree structures, which is essentially a state-based approach. The proposed approach is illustrated with a Guideway example taken from the literature.

A major vulnerability of mobile robots operating in more environments is their fragility in facing disturbances. A two-fold solution is proposed in this paper. First, a deformable structure was designed to reduce turbulence and to adapt to the uneven ground. Second, a novel control strategy is employed to avoid the limitations of the existing methods. In particular, among the existing solutions, the PID controller is known to be severely limited in handling disturbances and failures and the model-based designs all require detailed mathematical model, which may not be readily available, especially in the case of failures. To address these issues, a solution based on active disturbance rejection control is proposed in this paper, for its simplicity in design and tuning and its robustness against parameter variations and even failures in the pose deformation system. The proposed solution is systematically validated in the simulation, and the results are promising. The performance of the system was maintained in the presence of disturbances and uncertain dynamics, and the reliability of the robot is considerably improved when the unknown leg failures occurred.

This paper deals with the speed synchronization controller design for networked integrated motor-transmission (IMT) powertrains via controller area network (CAN). It is well known that, in current implementations, CAN has been widely used in the control system design of automotive powertrains. However, on the other hand, the application of CAN would not only lead to network-induced delays but also bring about protocol constrains, e.g., data package capability and utilization ratio limitation, which would deteriorate the system and make the controller design a challenging problem. This paper is to provide a co-design methodology that can cope with all these problems and ensure satisfactory control effect for the speed synchronization control of IMT powertrain systems. First, a networked IMT powertrain system using CAN as underlying network is presented and the dynamic model for the speed synchronization control is derived. Second, the network-induced delay model is introduced and improved considering data packet capability and utilization ratio limitation. The control-orient discrete-time model is also derived based on the improved delay model. Third, a co-design methodology using sliding mode controller and offline priority scheduling based on Lyapunov stability criterion is proposed. The results of simulations and tests show the effectiveness of the proposed co-design methodology.

This paper investigates the cooperative control problem of multiple nonholonomic robots for an escorting and patrolling mission with respect to a target with a time-varying velocity. The goal of the multi-robot system is to orbit a moving target in a common circle with a prescribed radius and circular velocity, while maintaining even spacing along the perimeter of the circle. This paper proposes a distributed control strategy based on the vector field that does not require each robot to know the full state of the target. A distributed estimation law is developed to enable each robot to estimate the position of the target, and a tracking differentiator is used to estimate the velocity of the target. Based on these estimates of the target, the distributed control law is designed to ensure the asymptotic convergence of the multi-robot system to achieve the desired motion. Under some mild assumptions about the interaction graph among the target and the robots, and the velocity of the target, explicit stability and convergence analyses are presented using Lyapunov tools. Simulation results from test cases of a group of nonholonomic mobile robots verify the effectiveness of the proposed distributed control algorithms.

In this paper we present the design, development and experimental validation of a novel Binary-Controlled Variable Stiffness Joint (BpVSJ) towards haptic teleoperation and human interaction manipulators applications. The proposed actuator is a proof of concept of a passive revolute joint, where the working principle is based on the recruitment of series-parallel elastic elements. The novelty of the system lies in its design topology, including the capability to involve an (n) number of series-parallel elastic elements to achieve ( $2^{n}$ ) levels of stiffness, as compared to current approaches. Accordingly, the level of stiffness can be altered at any position without the need to revert to the initial equilibrium position. The BpVSJ has low energy consumption and short switching time, and is able to rotate freely at zero stiffness without limitations. Further smart features include scalability and relative compactness. This paper details the mathematical stiffness modeling of the proposed actuator mechanism, as well as the experimentally measured performance characteristics. The experimental results matched well with the physical-based modeling in terms of stiffness variation levels. Moreover, Psychophysical experiments were also conducted using (20) healthy subjects in order to evaluate the capability of the BpVSJ to display three different levels of stiffness that are cognitively realized by the users. The participants performed two tasks: a relative cognitive task and an absolute cognitive task. The results show that the BpVSJ is capable of rendering stiffness with high average relative accuracy (Relative Cognitive Task relative accuracy is 97.3%, and Absolute Cognitive Task relative accuracy is 83%).

One of the classic approaches to controlling power networks, as large-scale systems, has been the use of a centralized control architecture. This approach is currently used less frequently due to its computational complexity. Another possible approach is the use of a decentralized control architecture. However, this approach can lead to unacceptable global performance of the system due to the lack of knowledge about the available interactions among subsystems. A third approach is the application of a cooperatively distributed architecture. On the other hand, one technique that has proved to be quite efficient for the control of power system frequency is model predictive control (MPC). In this paper, the performance of cooperatively distributed MPC is compared with that of the centralized MPC and the classical automatic generation control methods. The main contribution of this paper is that the load variations are applied to the system in the form of consecutive pulses. Additionally, the disturbance levels considered here have higher values. Moreover, the range of control input variations is reduced; therefore, the constraints are chosen more strictly. Finally, the total error of the system is determined, and the discussed methods are evaluated by the newly defined indices. According to simulation results, a feasible cooperation-based MPC method leads to relatively desired performance and computational speed and so can be an appropriate practical option for controlling power systems.

Fault diagnosis is an important topic both in practice and research. There is intense pressure on industrial systems to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering from potential faults as early as possible. From the historical perspective, this paper divides fault diagnosis into previous research and industrial big data era. According to primary drivers, this paper classifies fault diagnosis into knowledge-driven, data-driven, and value-driven methods. Among them, the former two approaches belong to the previous research on fault diagnosis. They mainly depend on expert experience and shallow models to detect and extract failures from relatively small size data. With the continuous exponential growth of data, it is insufficient to mine valuable fault information from massive multi-source heterogeneous data. The huge diagnostic value embodied in industrial big data has driven the emergence of the third category, which belongs to fault diagnosis based on big data. It consists of big data processing and analysis corresponding to high efficiency, cost effectiveness, and generality, which can deal well with problems that previous methods faced. We introduce the concept of a device electrocardiogram from the perspective of applicability to outline the present status of fault diagnosis for big data, and compare it with traditional diagnostic system. We also discuss issues and challenges that need to be further considered. It would be great valuable to integrate or explore more advanced diagnostic methods to handle collected industrial big data and put them into practice to mine the huge hidden diagnostic value.

In this paper, an enhanced linear inverted pendulum model (LIPM) and a gait planning algorithm are proposed. The LIPM is a widely used concept for gait reference generation, and it provides a simplified model for planning a center of mass trajectory when given a proper zero moment point trajectory. However, one of the assumptions of LIPM is that the legs of the robot are massless, so that the mass of the supporting leg can be neglected for simplification, and it conflicts with the mass distributions of human beings and most humanoid robots. Hence, this paper proposes a double-link LIPM (DLIPM) to eliminate the conflict about mass distribution. In addition, a gait planning algorithm is proposed for natural walking reference generation. In the simulation results, the proposed method is implemented based on a model of a teen-sized humanoid robot named David Junior. The simulation results validate the feasibility and practicability of the proposed method. Moreover, comparisons between conventional LIPM and DLIPM demonstrate the performance of the proposed DLIPM method. Eventually, the proposed method is implemented on David Junior for the weight-lifting event in the 2015 FIRA RoboWorld Cup, an event which David Junior won first place.

The problem of output regulation for a class of cascade switched nonlinear systems is investigated in this paper. Sufficient conditions for the problem to be solvable are given using the average dwell time method and the multiple Lyapunov function method. The problem for each subsystem to be solvable and may not be solvable are discussed, respectively. The main results are obtained based on full information feedback and error feedback. In addition, the method of designing the switched law is also presented in this paper. Finally, the simulation examples show that the results are very effective.

Cyber-physical systems (CPS) are a collection of transformative technologies for managing interconnected physical and computational capabilities. Recent developments in technology are increasing the availability and affordability of sensors, data acquisition systems, and computer networks. The competitive nature of industry requires manufacturers to implement new methodologies. CPS is a broad area of engineering which supports applications across industries, such as manufacturing, healthcare, electric power grids, agriculture, and transportation. In particular, CPS is the core technology enabling the transition from Industry 3.0 to Industry 4.0 (I 4.0) and is transforming global advanced manufacturing. This paper provides a consolidated review of the latest CPS literature, a complete review of international standards, and a complete analysis of patent portfolios related to the 5C’s CPS architecture model by Lee et al. The critical evaluation of international standards and the intellectual property contained in CPS patents is unaddressed by the previous research and will benefit both academic scholars and industry practitioners. The analysis provides a basis for predicting research and development future trends and helps policy makers manage technology changes that will result from CPS in I 4.0. This paper covers the emerging I 4.0 standards from the International Organization for Standardization, the International Electrotechnical Commission, and China’s Guobiao standards followed by a patent analysis covering global patents issued in the U.S., Europe, China, and the World Intellectual Property Organization.

In this paper, a novel optimal adaptive-gains super-twisting sliding-mode control (OAGSTSMC) using actor-critic approach is proposed for a high-speed permanent-magnet synchronous motor (PMSM) drive system. First, the super-twisting sliding-mode controller (STSMC) is adopted for reducing the chattering phenomenon and stabilizing the PMSM drive system. However, the control performance may be destroyed due external disturbances and parameter variations of the drive system. In addition, the conservative selection of the STSMC gains may affect the control performance. Therefore, for enhancing the standard super-twisting approach performance via avoiding the constraints on knowing the disturbances as well as uncertainties upper bounds, and to achieve the drive system robustness, the direct heuristic dynamic programming (HDP) is utilized for optimal tuning of STSMC gains. Consequently, an online actor-critic algorithm with HDP is designed for facilitating the online solution of the Hamilton-Jacobi-Bellman (HJB) equation via a critic neural network while pursuing an optimal control via an actor neural network at the same time. Furthermore, based on Lyapunov approach, the stability of the closed-loop control system is assured. A real-time implementation is performed for verifying the proposed OAGSTSMC efficacy. The experimental results endorse that the proposed OAGSTSMC control approach achieves the PMSM superior dynamic performance regardless of unknown uncertainties as well as exterior disturbances.

This paper studies the prescribed finite time stabilization control problem of multi-input linear control systems subject to uncertainties and state constraints. Different from finite-time and fixed-time control methods, a novel switching nonsingular coordinate transformation function based on the block decomposition technique is developed, resulting in a solution that the control action is continuous and bounded based on the transformed system. Moreover, the boundedness of closed-loop system states is ensured with prescribed finite time performance. Furthermore, we incorporate the barrier Lyapunov function into the stabilization control design to restrain the amplitudes of system states. Numerical examples are provided to verify the effectiveness of the proposed prescribed finite time control strategy.

In this paper, a sliding mode observer (SMO) for the speed-sensorless direct torque vector control of linear induction motor(LIM) is proposed, considering the influence of dynamic end effects. The proposed SMO estimate the motor speed, the rotor flux, the angular position of the rotor flux, and the speed dependent parameter $w$ , only by using the measured stator voltages and stator currents. The nonlinear sliding mode technique guarantees very good performance for different situations of motor speed. The proposed SMO does not use complicated observer gains and robust to the parameter variation over the full-speed range. Moreover, the convergence of SMO is proven by using the Lyapunov theory. Furthermore, LIM Drive state observability is presented with detailed analysis. The performance of the proposed SMO is verified by hardware-in-the-loop test (HIL) system. Furthermore, a comparative study with and Luenberger observer(LO) and model reference adaptive system(MRAS) is presented.

The slippage occurs between the fingers and object will result in grasp failure that badly affects the prosthetic hand’s operation. In this paper, a new slippage prevention strategy is proposed to improve the prosthetic hand’s grasp stability. The proposed method, which comprises a slipping process model, a multi-threshold detection scheme, and a force regulation policy, can have a minimal loading force intervention after stabilization. First, the slipping process is modeled with special considerations on the nonlinear and noise characteristics of the system. Then, the multiple thresholds are selected for slippage detection to reduce the influence of loading force variation and system nonlinearity. Lastly, a loading force regulation policy consisting of a serials of constant, slight force increments is applied to suppress the detected slippage and minimize the force growth. One merit of our method is that the parameters selected in the algorithm largely depend on the system characteristics of the hand system, but not the grasped object. Experimental results show that the proposed method has better detection accuracy on slippage detection and minimal loading force intervention after slippage suppression.

This paper deals with the problem of signal source traversal based on a quadrotor in an environments with obstacles by proposing a data-driven receding horizon control approach. The proposed control approach includes two parts: One part is a data-driven learning method while the other part is a receding horizon control method. Gaussian process as a kind of data-driven learning method is used to provide the efficient posterior probability distribution of the possible positions for all the signal sources based on the received signal strength data. It should be pointed out that the obstacles in the environment obstruct signal detection and the antennas diverge the direction of signal transmission such that the positions of signal sources are frequently updated. In order to cope with this kind of dynamic events, a receding horizon control approach in the light of a new cost function is proposed to enables the quadrotor to traverse signal sources, where the generated trajectory is smooth and the quadrotor can be guided to efficiently avoid “L” shape obstacles. Finally, simulation and experimental results show that the quadrotor controlled by the proposed data-driven receding horizon control approach can move along the smooth trajectory to avoid obstacles and traverse all the signal sources.

Robots that can execute various tasks automatically on behalf of humans are becoming an increasingly important focus of research in the field of robotics. Imitation learning has been studied as an efficient and high-performance method, and imitation learning based on bilateral control has been proposed as a method that can realize fast motion. However, because this method cannot implement autoregressive learning, this method may not generate desirable long-term behavior. Therefore, in this paper, we propose a method of autoregressive learning for bilateral control-based imitation learning. A new neural network model for implementing autoregressive learning is proposed. In this study, three types of experiments are conducted to verify the effectiveness of the proposed method. The performance is improved compared to conventional approaches; the proposed method has the highest rate of success. Owing to the structure and autoregressive learning of the proposed model, the proposed method can generate the desirable motion for successful tasks and have a high generalization ability for environmental changes., Copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works