Search

The aim of this article is the design of a feedforward compensator based on deep reinforcement learning (DRL) for cooperative quadrotors in close crossing flight. Quadrotors are described by state-space models that include shearing airflow disturbance from other quadrotors. This disturbance is compensated in a feedforward way using DRL. Both value based compensator and policy based compensator algorithms are proposed for training purposes. Then, Lyapunov stability criteria are used to prove that the reference trajectory can be tracked boundedly even during the training process of the proposed algorithms, and that a smaller bound of tracking error can be achieved when the compensator converges. An indoor experimental system for online training has been developed for validation purposes. Both simulation and experimental results are provided to demonstrate the effectiveness and advantages of the proposed compensator. [ABSTRACT FROM AUTHOR]

A robust output regulation of a control system aims at disturbance rejection and reference tracking in the presence of uncertainties. Technically, the control design contains two mechanisms: the compensation of nontrivial steady states caused by disturbances and/or reference trajectories and the stabilization of an error system relative to steady states. Feedforward compensation and the internal model (including an approximate integrator) are the two main techniques for the former, while the latter is on a case-by-case basis. This article studies an alternative sliding-mode-based approach that can address the two mechanisms simultaneously, thanks to the new concept of sliding-mode-based output zeroing manifold. This concept bridges the two widely studied areas: robust output regulation and sliding-mode control. The approach is also verified by its application in permanent magnet synchronous motor servo systems. [ABSTRACT FROM AUTHOR]

Controlling the multiway power flow in a multi-active-bridge (MAB) converter is important for achieving high performance and sophisticated functions. Traditional feedforward methods for MAB converter control rely on precise lumped circuit models. This article presents a machine learning (ML) method for the feedforward power flow control of an MAB converter without a precise circuit model. A feedforward neural network was developed to capture the nonlinear characteristics and predict the phases needed to achieve the targeted power flow. The neural network was trained with a large amount of data, collected with a set of known phase angles. This trained network was used to predict the phases to achieve the targeted power flow. A six-port MAB converter was built and tested to validate the methodology and demonstrate the “machine-learning-in-the-loop” implementation. Transfer learning was proven to be effective in reducing the size of the training data needed to obtain an accurate ML model. ML-based feedforward power flow control can achieve comparable accuracy as traditional model-based methods and can function without a precise lumped circuit element model of the MAB converter. [ABSTRACT FROM AUTHOR]

This article proposes a novel application method, inverse application of artificial intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of artificial neural network is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multilevel converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility, and practicality of IAAI. [ABSTRACT FROM AUTHOR]

In parallel T-type three-level rectifiers system (PT23LRs), when the open-circuit fault (OCF) of neutral-point (NP) switches occurs, the existing zero-sequence circulating current (ZSCC) suppression methods are not capable of suppressing the ZSCC any more since the ZSCC model of PT23LRs is changed. Significantly, the ZSCC suppression issue of PT23LRs under the OCF of NP switches has not been researched in previous literatures. To overcome this limitation, a novel ZSCC suppression method for PT23LRs with the OCF of NP switches is proposed. First of all, according to the principle of area equivalence, the fault-tolerant control (FTC) is accomplished by reconstructing the switching sequence. Second, a novel ZSCC model with FTC is established. Then, according to the novel ZSCC model, the ZSCC control method based on “PI + feedforward” is proposed. This method is realized by modifying the turn-on time of three-phase of fault rectifier in PT23LRs. Consequently, the method proposed in this article effectively restrains the ZSCC of PT23LRs with the OCF of NP switches. Finally, the simulation and experiment results verified the correctness of proposed method. [ABSTRACT FROM AUTHOR]

To improve the current tracking performance, this article proposes a new fault-tolerant current control for the six-phase fault-tolerant permanent magnet synchronous motor (FTPMSM) system with multifrequency quasi-proportional-resonant (QPR) control and back electromotive force (EMF) feedforward compensation, which can track the time-varying sinusoidal and nonsinusoidal reference currents in the stationary reference frame (SRF) under normal and fault conditions. First, the multifrequency QPR current controller with shunt topology is proposed to track the time-varying reference current in SRF, which can guarantee the current control performance regardless of the motor speed variation and the load torque change. Second, the optimized feedforward compensation method for the back EMF is proposed to further reduce the steady-state current tracking error, which takes the time delay of the digital implementation into consideration. Finally, the effectiveness of the proposed approach is verified on a 3 kW six-phase FTPMSM platform. The resulting six-phase FTPMSM system with the proposed current control has great current tracking performance, strong robustness to various external/internal disturbance, as well as low computational burden, which can guarantee the multiphase FTPMSM system performance in normal and fault conditions. [ABSTRACT FROM AUTHOR]

In this letter, a simplified single current loop control scheme for single-phase dual-boost inverter has been developed, combining half cycle modulation and virtual-vector (VV) based model predictive control (MPC). As the control reference can be slipped and allocated into two side boost, only a unified grid current reference is relied on in this proposed control method. Furthermore, with the concept of feedforward control, the global optimal solution space in MPC can be compressed, taking the duty ratio as boundary for the precompacted solution space. Thereby, the improved MPC can select the optimal VV in a smaller and closer subspace. Consequently, much finer control effect can be obtained. Experimental tests are provided to validate the proposed solution and its merits. [ABSTRACT FROM AUTHOR]

A new structure of cross-coupling position command shaping controller using ${H_\infty }$ control scheme for the precise tracking in the multiaxial motion control is proposed in this article, together with its stability analysis.This new structure has the advantage that the controller has a simpler design process and robuster performance than the conventional ones. The proposed controller is evaluated and compared experimentally with an uncoupled controlled and conventional system. The experimental results show that the new structure remarkably reduces contour error. In addition, this new controller can be implemented easily on a majority of motion systems in use today via reprogramming the reference position command subroutine. [ABSTRACT FROM AUTHOR]

Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials. [ABSTRACT FROM AUTHOR]

The purpose of this article is to understand how learning is disseminated between individual, project, organizational, and interorganizational levels—A phenomenon that remains underresearched, particularly in the context of interorganizational projects. This article unpacks the learning processes and underlying learning mechanisms that disseminate learning at different levels. For this purpose, we examine the Islamabad–Rawalpindi metro bus project in Pakistan. Using an abductive research approach, data for this article are collected through interviews, archival records, and illustrative materials. The findings reveal 11 learning mechanisms: 1) learning by observing; 2) learning from past experiences; 3) learning from an expert; 4) learning from the experiences of others; 5) learning by joint problem-solving; 6) learning by working together; 7) learning from mistakes; 8) learning by observing other projects; 9) learning by building organizational memory; 10) learning from existing organizational memory; and 11) learning by coordinating and consulting. These learning mechanisms trigger learning processes of intuiting, interpreting, integrating, institutionalizing, and intertwining at the individual, team, organizational, and interorganizational levels. This article enriches the understanding of project-based learning, focusing on different levels and exploring the interplay between learning processes and mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback controller with logarithmic function is constructed to address the objective of global regulation for a class of feedforward nonlinear systems with time delays. Firstly, discrete delays and distributed delays are allowed in the input and output as well as states of the considered system. Secondly, compared to the conventional research contributions of state feedback regulation, not only the condition that the output signal is needed to design the control strategy become relaxed, but also the information about the discrete delays and distributed delays that need to be known is less. Finally, different from the existing research results, based on the time-varying gain technology and Lyapunov–Razumikhin theorem, the output feedback global regulation controller constructed here is more lucid in structure and more perfect in function. Two examples are presented to demonstrate the effectiveness of global regulation about the proposed approach for the considered system. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) enables high performance for motion systems executing repetitive tasks. The robustness filter of ILC enhances the robustness w.r.t. model uncertainties and disturbances, but results in that the repetitive error cannot be eliminated. In this article, a dual-loop ILC (DILC) approach is proposed for precision motion systems to explicitly address the design tradeoff of standard ILC between robustness and tracking performance. In the proposed DILC approach, the standard ILC is paralleled with an additional feedforward signal. When ILC converges, the additional feedforward signal is updated by the converged total feedforward signal, and then, the ILC begins a new iteration. As a result, the nonzero asymptotic error caused by the robustness filter is eliminated by adding an iterative action over the feedforward signal onto ILC. Comparative simulation and experimental results confirm that, compared to ILC, the proposed DILC can significantly enhance the tracking performance without the sacrifice of robustness w.r.t. model uncertainties and disturbances. Application to an ultraprecision wafer stage illustrates that the proposed DILC decreases the peak values of moving average and moving standard deviation of the tracking error by 52.7% and 43.9%, respectively. [ABSTRACT FROM AUTHOR]

This letter proposes a generalized stabilizer-oriented design method for grid-forming virtual synchronous generator (GFVSG) attached to weak-stiffness power networks, where a rate of change of frequency feedforward control strategy (RoCoF-FCS) is put forward to improve frequency stability, enhance damping performance, and avoid aperiodic loss of synchronization (ALoS). In addition, the feedback analytical framework, which connects RoCoF and rate of change of voltage (RoCoV), is proposed to identify both wideband oscillation and ALoS. Finally, the proposed method is validated by time-domain simulations. [ABSTRACT FROM AUTHOR]

In this article, the finite-time (FT) deterministic learning control for the hypersonic flight vehicle (HFV) dynamics with model uncertainty is investigated. The design is divided into an offline training phase and an online control phase. First, in the offline training, the radial-basis-function neural networks (RBF NNs) are set along the periodic signals to guarantee the partial PE condition. Meanwhile, the offline FT composite learning laws are constructed driven by the system tracking and learning performance index. Embedding the FT composite learning in the FT command filtered control framework, the FT convergences of the system tracking and learning are guaranteed simultaneously. Moreover, the near-optimal learning knowledge is stored. In the next online process, the stored NNs weights are directly used in the online tracking controller without repeatedly updating the weights. Simulation on HFV dynamics shows that the offline FT learning control can achieve better learning and tracking performance, while recalling the stored knowledge online not only guarantees the control performance but also reduces the computational load. [ABSTRACT FROM AUTHOR]

In the primary control layer of DC microgrids, engineers usually select the control gains with a robust design strategy (i.e., the worst case study), aiming to ensure stable operation of the system with the presence of large-signal disturbances. However it will inevitably result in degraded nominal control performance. To this end, a compromise between dynamic performance and system stability appears and how to reconcile it is a challenging task. In this context, we propose a novel adaptive control strategy aiming to pursue a balance between the above two properties. Firstly, a higher-order sliding mode observation technique is employed to estimate the disturbance variations within a finite time. Secondly, an adaptive gain regulation mechanism is designed in an easy-implementable fashion. Finally, by combing the feedforward decoupling procedure and the adaptive feedback scheme, the control performance of the DC microgrids can be adjusted adaptively in different working conditions. Through the above design produces, the proposed controller will bring the following new features: 1) The robustness redundancy of existing robust control strategies can be essentially avoided. 2) The balance between dynamic performance and system stability is achieved. 3) The self-tuning regulation facilitates the process of gain selection and the concise adaptive structure can be easily utilized in practical implementation. The efficacy of the proposed controller is verified by both simulation and experiment results. [ABSTRACT FROM AUTHOR]

Different harmonic mitigation techniques have been utilized in grid-connected inverters to suppress the effect of grid voltage distortion on the output current of these inverters. In practice, to scale up the injected current into the grid, a set of parallel grid-connected inverters are often utilized rather than a single high-power inverter in solar Photovoltaic or motor drive systems. Conventional Point of Common Coupling (PCC) feedforward approaches have been used for harmonic rejection of multi-parallel grid-connected inverters. However, these methods need to implement a separate feedforward scheme for each individual inverter. In this paper, a novel full PCC voltage feedforward strategy is proposed to reduce the cost investment, which is implemented only in one of the grid-connected inverters. The proposed strategy is based on cancelling the total parallel admittance of the system by adding a virtual negative admittance to the target inverter. It is shown that the proposed scheme causes no instability issue on the system since it does not affect the system phase margin. To verify the efficiency of the proposed strategy against emitted harmonics from the grid, simulations and experiments on a system with two parallel single-phase grid-tied inverters are performed. [ABSTRACT FROM AUTHOR]

This article proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with incremental encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro-actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below $\pm \text{1}\ \mu$ m when subject to a sensor quantization level of 5 $\mu$ m. [ABSTRACT FROM AUTHOR]

High dynamic performance is an essential requirement for the dual-active-bridge (DAB) dc/dc converters. As dc voltage sources, they should maintain the desired output voltage instantly under all working conditions. However, the previous literature mainly focus on the dynamic control of the voltage-fed DAB converters, and the existing control schemes for the current-fed DAB converters achieve limited dynamic performance. Aiming at improving the dynamic performance, a direct actual-power control (DAPC) scheme based on virtual impedance estimation (VIE) is proposed for the current-fed DAB converters in this article. The proposed DAPC scheme is based on a parallel structure instead of the series structure of existing control schemes, and it realizes fast dynamic control through combining actual power control with the VIE method. The proposed DAPC scheme can obtain the fastest transient response for the output voltage without voltage overshoot in transient conditions, such as load step change, input voltage fluctuation, and the desired output voltage step change. Besides, a leakage inductor precharging method is integrated into the DAPC scheme to avoid the current mismatching. Finally, the proposed DAPC schemes are compared with two existing control schemes and tested in a scale-down experimental prototype. Experimental results verify the effectiveness of the proposed DAPC scheme. [ABSTRACT FROM AUTHOR]

In this article, we develop a predictive geometry control framework for jet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model’s parameters are obtained by training the network through back propagation using input–output data from a small number of layers. Moreover, we demonstrate that the model can be dually expressed such that the layer droplet input pattern for (each layer of) the part to be fabricated now becomes the network parameter to be learned by back-propagation. This approach is applied for feedforward predictive control in which the network parameters are learned offline from previous data and the control input pattern for all layers to be printed is synthesized. Sufficient conditions for the predictive controller’s stability are then shown. Furthermore, we design an algorithm for efficiently implementing feedback predictive control in which the network parameters and input patterns (for the receding horizon) are learned online with no added lead time for computation. The feedforward control scheme is shown experimentally to improve the RMS reference tracking error by more than $\boldsymbol{30\%}$ over the state of the art. We also experimentally demonstrate that process uncertainties are compensated by the online learning and feedback control. [ABSTRACT FROM AUTHOR]

Feedforward active vibration control techniques aim at canceling vibrations occurring in a system by means of introducing intentional forces that generate destructively interfering vibrations. These forces are typically controlled using an adaptive filtering algorithm that tracks the system state in real time and seeks to minimize overall vibration. One important often-overlooked issue in such systems is related with the vibrations introduced by the adaptive control algorithm itself either during its transient learning phase or due to its nonideal characteristic. These vibrations may become very significant in several mechanical structures due to resonance phenomena. In this context, this work is dedicated to the development of an effective scheme for feedforward active vibration control that is capable of dealing with undesirable vibrations introduced by the adaptive controller. A particular version of the filtered-x normalized least-mean-squares algorithm tailored to the proposed scheme is also developed. Results obtained from numerical simulation as well as from an experimental device are presented, aiming to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

To accommodate the exponential growth of network services in the five-generation (5G) and beyond wireless system, 50 Gb/s/λ passive optical network (PON) is developed for mobile xhaul applications. Intensity modulation and direct detection (IMDD) technology together with digital signal procession (DSP) is being considered as the promising solution for 50 Gb/s/λ PON due to its low cost, low power consumption, and compact footprint. Different DSP algorithms with varied structures are proposed for linear and nonlinear impairments compensation in the high-speed PON, while the performance and complexity analysis of these algorithms is still missing. To find the most efficient equalizers, in this paper, four conventional equalizers, including feed-forward equalizer, decision feedback equalizer (DFE), Volterra equalizer (Vol) and Volterra DFE equalizer (Vol-DFE), together with two deep learning equalizers namely fully-connected neural network, and long short-term memory equalizer are experimentally compared in a 10G optics based 50G-PON system in terms of the equalization performance, computation complexity, optimization difficulty, and generalization ability. After the evaluation of our proposed fair comparison algorithm, we consider Vol-DFE is the most efficient one considering both performance and complexity. Attributes to the strong and efficient equalization capability of Vol-DFE, C-band 50 Gb/s PAM-4 signal transmission can be supported in a 10G optics based IMDD system with a 3 dB bandwidth of 6.11 GHz, and a power budget up to 38 dB can be achieved. [ABSTRACT FROM AUTHOR]

This article proposes a new data-driven optimization of integrated control for flexible systems to achieve high-performance automatic control. The integrated control that is composed of feedback control, feedforward control, and disturbance observer is adopted in this article as the control framework that can effectively address the control problems of the flexible system. However, it is difficult to optimize all the parameters of the integrated control, because the number of the parameters to be optimized is larger than the conventional feedback control, which complicates the optimization procedure. In this article, the optimization procedure of the integrated control as well as the mathematical background of it is proposed. At first, the closed-loop characteristics of the integrated control are analyzed and its convexity with respect to control parameters is theoretically investigated. The proposed optimization method is designed taking into consideration the convexity of the control configuration to guarantee the global optimality of the obtained parameters. Moreover, the proposed method can simultaneously optimize all the parameters of the integrated controller based on the experimental data. The effectiveness of the proposed algorithm is experimentally confirmed using a flexible system under the following two conditions: first, change of initial parameters and second, change of plant conditions. [ABSTRACT FROM AUTHOR]

This article presents results from an experiment in which 44 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into four groups. All groups interact with the same dynamic system, but each group performs a different sequence of command-following tasks. All reference commands have frequency content between 0 and 0.5 Hz. We use a subsystem identification algorithm to estimate the control strategy (feedback and feedforward) that each subject uses on each trial. The experimental and identification results are used to examine the impact of the command-following tasks on the subjects’ performance and the control strategies that the subjects learn. Results demonstrate that certain reference commands (e.g., a sum of sinusoids) are more difficult for subjects to learn to follow than others (e.g., a chirp), and the difference in difficulty is related to the subjects’ ability to match the phase of the reference command. In addition, the identification results show that differences in command-following performance for different tasks can be attributed to three aspects of the subjects’ identified controllers: 1) compensating for time delay in feedforward; 2) using a comparatively accurate approximation of the inverse dynamics in feedforward; and 3) using a feedback controller with comparatively high gain. Results also demonstrate that subjects generalize their control strategy when the command changes. Specifically, when the command changes, subjects maintain relatively high gain in feedback and retain their feedforward internal model of the inverse dynamics. Finally, we provide evidence that subjects use prediction of the command (if possible) to improve performance but that subjects can learn to improve performance without prediction. Specifically, subjects learn to use feedback controllers with comparatively high gain to improve performance even though the command is unpredictable. [ABSTRACT FROM AUTHOR]

In this paper, a novel lane keeping control method for automated vehicles based on human-simulated intelligent control (HSIC) is proposed, which is inspired by human expert drivers’ steering characteristics including good foresight, precise execution and notable intermittency. The novelty of the paper is to introduce the HSIC concept into lateral control of vehicles, which is a multi-mode control scheme implemented by the combination of the feedforward control for curve tracking and act-and-wait control for intermittent error correction. Theoretically, the stabilization problem of the HSIC method is investigated based on the switched system related method. Experiments on the joint simulation platform of PreScan and CarSim show that the newly presented HSIC scheme has better matching performance to the expert driver and good robustness. For automated lane keeping systems, the HSIC method could provide human-like qualities, which may be one of the essential points to determine whether the driver is comfortable or not when the driver hands over the steering authority, improve the transition smoothness in the scenario of human vehicle co-piloting, and eliminate the potential conflicts between manual driving and automated driving vehicles in the future mixed traffic flow. [ABSTRACT FROM AUTHOR]

Hybrid energy storage microgrid represents the future direction of distributed energy development. However, under the influence of interference, the dynamic performance of the output side interface will be severely deteriorated. In this article, a linear active disturbance rejection controller with reference differential feedforward and cascade linear extended state observer (ADRC-RDCO) is proposed to deal with unknown modeling error and the unknown dynamics of the interface converter. In this strategy, a cascaded linear extended state observer is introduced to cope with the steady-state offset in the disturbance estimation error, so as to implement the accurate reconstruction of the disturbance state. Furthermore, a control law with RDF is applied to offset a portion of the tracking error of the given signal to achieve the purpose of optimizing the tracking accuracy of the given signal. Then, the tracking performance and stability of ADRC-RDCO are analyzed theoretically. On the basis of discussing the relationship between dynamic characteristics and control parameters, a parameter configuration method is proposed. Finally, the proposed ADRC-RDCO technology was verified on the 40 kW microgrid platform. [ABSTRACT FROM AUTHOR]

Benefitting from some significant advantages, the dual active bridge (DAB) dc–dc converter has become one of the most promising candidates for dc–dc power conversion. In recent years, some strategies have been proposed to boost the dynamic performance of DAB dc–dc converter under the disturbance of input voltage and load condition. According to the relationship between the compensation part and the model-based part, these existing schemes can be divided into two structures including the parallel structure and series structure. In the parallel control structure, the compensation part is added to the model-based part. In contrast, the compensation part is multiplied with the model-based part in the series control structure. By adopting proper feedback control, both control structures can provide excellent dynamic performance for DAB dc–dc converter easily. Hence, the modified parallel-structure fast-dynamic control scheme and the modified series-structure fast-dynamic control scheme are both proposed in this article. Then, using these two proposed schemes as examples, the merits and the demerits of both structures are analyzed, and the corresponding compensation methods are also presented. Moreover, a general PI design principle of the model-based control scheme for the DAB dc–dc converter is provided, which is different from the traditional concept for designing the PI parameters. In addition, the control delay of these two proposed schemes is analyzed, and a compensation method is also proposed. Finally, simulation results and experimental results are obtained to verify the analysis in this article and the excellent performance of the proposed methods. [ABSTRACT FROM AUTHOR]

We present results from an experiment in which 33 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into three groups. For each interaction, a subject performs a command-following task, where the reference command is the same for all trials and all subjects. However, each group interacts with a different dynamic system, which is represented by a transfer function. The transfer functions have the same poles but different zeros. One has a minimum-phase zero $z_{\textrm {m}} < 0$ , another has a nonminimum-phase zero $z_{\textrm {n}} = - z_{\textrm {m}} > 0$ , and the last has a slower (i.e., closer to the imaginary axis) nonminimum-phase zero $z_{\textrm {sn}} \in (0,z_{\textrm {n}})$. The experimental results show that nonminimum-phase zeros tend to make dynamic systems more difficult for humans to learn to control. We use a subsystem identification algorithm to identify the control strategy that each subject uses on each trial. The identification results show that the identified feedforward controllers approximate the inverse dynamics of the system with which the subjects interact better on the last trial than on the first trial. However, the subjects interacting with the minimum-phase system are able to approximate the inverse dynamics in feedforward more accurately than the subjects interacting with the nonminimum-phase system. This observation suggests that nonminimum-phase zeros are an impediment to approximating inverse dynamics in feedforward. Finally, we provide evidence that humans rely on feedforward-step-like-control strategies with systems (e.g., nonminimum-phase systems) for which it is difficult to approximate the inverse dynamics in feedforward. [ABSTRACT FROM AUTHOR]

High control bandwidth is usually restricted in a photoelectric tracking system (PTS) based on a Charge-Couple Device(CCD) with time delay, which hinders a good tracking performance. Generally, a model-based delay-compensation controller called Smith predictor (SP) can help increase the controller gain to promote the bandwidth by separating delay from the control loop. However, the performance promotion is insufficient because the delay still stays in the forward channel which causes errors between output and input. And the increase of the controller gain is still limited due to the effect of model mismatch on stability. In this paper, to solve the problems, a delay-compound-compensation control (DCCC) based on improved SP by trajectory prediction and velocity feedforward is proposed. The additional trajectory prediction is used to further eliminate the effect of delay existing in the forward channel. The additional velocity feedforward is used to further reform the transfer characteristics limited by the controller gain. A Kalman filter-based design method of trajectory prediction is presented and the optimal design principle of feedback and feedforward controllers is given in the face of model mismatch. Experiments demonstrate that the DCCC is valid and could greatly promote the tracking performance in the low frequency. [ABSTRACT FROM AUTHOR]

This paper investigates the robust stabilization problem of a class of uncertain nonlinear systems with input matching uncertainty under linear feedforward growth conditions. Smooth robust controllers are obtained by state feedback and output feedback, respectively. In order to deal with input matching uncertainty, extra dynamics are designed inspired by the integral control and disturbance estimation. The proposed controllers guarantee that the states of controlled systems asymptotically converge to zero and all closed-loop signals are globally bounded. Finally, simulation examples are given to illustrate the usefulness of the state-feedback and output-feedback controllers proposed.

The parallel connection of multiple distributed energy resources with a common DC-link structure is typically used in grid-connected applications which enables flexible operation maximizing power production of the inverter system under various operation conditions. However, it has brought drawbacks for DC-link power decoupling with the requirement of a larger capacitor bank, faster voltage regulation, etc., to maintain a constant DC-link voltage which increases the overall size and cost. In this paper, a DC-link decoupling technique using a nonlinear control algorithm is proposed to perform rapid DC-link voltage regulation for multi-input grid-connected inverters. With the implementation of a nonlinear observer, the power fed into the DC-link from multiple inputs is estimated by the proposed control algorithm and can be rapidly compensated by the inverter minimizing the DC-link voltage fluctuation. The effectiveness of the proposed nonlinear power decoupling control algorithm is verified by comparing the DC-link performance with a conventional control algorithm through both simulation results on a MATLAB platform and experimental verification on a grid-connected inverter prototype.

Nanopositioning stages driven by piezoelectric actuator have been employed across a wide spectrum of applications. However, the challenging issues they are suffering include hysteresis and creep effects, along with lightly damped mechanical resonances. In view of these issues, this article presents a novel hierarchical antidisturbance control solution. The proposed strategy seamlessly integrates three antidisturbance components, i.e., a feedforward-type disturbance observer (DO), a feedback-type DO, and an error-based active disturbance rejection controller (EB-ADRC) into a single framework. Such a strategy loosens the bandwidth constraint of DO imposed by mechanical resonance and speeds up the response time of typical ADRC without relying on high-order derivatives of reference input. The inclusion of these antidisturbance components does not complicate the controller tuning procedure. In fact, it is shown that only one parameter needs to be tuned in practice. Moreover, the stability analysis of EB-ADRC is provided within DO framework. The superior control performance of the proposed strategy is confirmed via extensive comparative studies with integral resonant control, positive position feedback, and linear active disturbance rejection control strategies. [ABSTRACT FROM AUTHOR]

Most electrical power system loads are actively controlled power electronics converters behaving as constant power loads. These loads resemble a small-signal negative impedance that can directly degrade the system stability margin and performance. The active damping approach is widely used for instability mitigation. However, if the mitigation is applied at the load side, it can affect the load performance. Therefore, this article presents a new design method for instability mitigation by the artificial intelligence method called the adaptive tabu search algorithm. After applying the proposed design method, the power system can maintain stable operation until achieving the rated power. Moreover, the load performance is better when the proposed design technique is applied compared to that from the conventional design. Good agreement among theoretical, simulation, and experimental results is also achieved. [ABSTRACT FROM AUTHOR]

In this article, an improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to solve the optimization problem of controller parameters in the design of a permanent magnet synchronous motor (PMSM) servo system. The proposed algorithm presents the directional mutation operation to the particles, which fixes the position of some particular particles so as to enhance the searching ability to some remote regions. In order to cooperate with directional mutation operation, the updating formula of particles velocity is ulteriorly improved. Then, the proposed IHPSO algorithm is adopted to optimize parameters of the designed controller. A simulation and an experimental platform of the PMSM servo system are designed using a biological intelligence controller based on hormone regulation for the speed control and feedforward compensation for the position controller, where IHPSO is applied to the parameter optimization for speed and position controllers, which validate the effectiveness of the IHPSO algorithm and the designed control system. [ABSTRACT FROM AUTHOR]

To improve the usability of cooperative adaptive cruise control (CACC) in the mixed traffic, a CACC algorithm with unconnected vehicle in the loop (CACCu) is proposed. Unlike the traditional CACC that requires a connected preceding vehicle or otherwise degrades to adaptive cruise control (ACC), CACCu aims to closely follow an unconnected preceding vehicle utilizing the information from the further (connected) preceding vehicle. Moreover, CACCu can robustly maintain string stability given various behaviors of unconnected preceding vehicles, without requiring identification process or extra information on the unconnected vehicles. For the sake of simplicity, this paper starts with CACCu in the three-vehicle sandwich scenario (i.e., one unconnected vehicle is in between of two connected vehicles), but derivatively, this control design is extended and evaluated in multiple-unconnected-vehicle cases. It is proven that by attaching a filter of “virtual preceding vehicle” to the original feedforward filter, the CACCu vehicle can stay string-stable at a gap significantly shorter than that required by ACC, given almost all kinds of car-following behaviors of the unconnected vehicle. At last, the favorable properties of CACCu are validated in high-fidelity simulations using real vehicle trajectory data and a physics-based vehicle dynamics model. The results show that CACCu outperforms existing ACC and acceleration-based connected cruise control (CCC) in string stability, ride comfort, safety maintenance, and fuel consumption. [ABSTRACT FROM AUTHOR]

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

In this work, the problem of global adaptive stabilization is solved by output feedback for a family of feedforward nonlinear time-delay systems with uncertain output equation and unknown growth rate. To deal with system uncertainty, a low-gain observer incorporating with novel adaptive gain is first proposed. Next, via output feedback, an adaptive delay-free controller is constructed by combining scaling change with backstepping algorithm. Compared with existing results, the controller proposed is capable of handling both uncertain output equation and unknown delay. With the aid of Lyapunov-Krasovskii functional and Barbălat's lemma, it is shown that the state and its estimate converge asymptotically to zero, and the adaptive gain is bounded. Furthermore, by numerical simulations, the usefulness of the control scheme is illustrated. [ABSTRACT FROM AUTHOR]

The cascaded H-bridge (CHB) low-capacitance static compensator (LC-StatCom) has a limited inductive region compared to a conventional StatCom, both in balanced and unbalanced conditions, due to the inherent large oscillations on the capacitor voltages. This article presents a detailed analysis of the viable operating region in these converters that takes into account the inherent large oscillations. The analysis facilitates sizing of the dc-link capacitors to ensure that the StatCom is able to inject a given range of positive- and negative-sequence current. Though the analysis is generic and applicable to the CHB StatComs with relatively large capacitance values, it is more relevant in the LC-StatComs. In addition, the article derives reference steady-state signals that can be used as feedforward terms in LC-StatCom control loops to enhance the control performance. The article presents simulation and experimental results, in balanced and unbalanced conditions to corroborate the proposed analysis. [ABSTRACT FROM AUTHOR]

For current-controlled grid-connected inverters with LCL filter, the essence of grid voltage full feedback is to improve the output impedance of grid-connected inverters to infinity, which can eliminate the influences of grid voltage distortion and the non-negligible grid impedance. However, existing full feedforward strategies are complex and control delay cannot be compensated precisely, which leads to the instability of system when the full feedforward of the voltage at the point of common coupling under the weak grid is adopted. This article first proposes a simplified full feedforward strategy based on grid-side inductor volt-age differential feedback active damping (GIVD-AD). Second, repetitive prediction (RP) is used to compensate for the control delay. Then, a virtual impedance branch is added and an adaptive algorithm for modifying virtual impedance based on the difference between predicted grid voltages by Newton interpolation prediction (NIP) and RP is proposed. With the proposed method, grid-connected inverters can work stably when grid impedance changes suddenly and exhibit strong rejection ability against grid-voltage harmonics. Finally, simulative and experimental results from a 3-kW single-phase grid-connected inverter are provided to prove the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The finite-time output regulation (FTOR) problem for linear heterogeneous multi-agent systems (MAS) is investigated in this brief. The objective of this brief is to design controllers such that each agent can accurately track the reference signal and reject the disturbance. The disturbance and reference signal are generated by an exosystem. It is assumed that only a part of agents can obtain the state and system matrix of the exosystem. Then, distributed fixed-time observers are firstly proposed to estimate them, which can ensure the fixed-time convergence of the estimation errors. Next, we design both state feedback controller and output feedback controller to solve the FTOR problem using feedforward method. By virtue of Lyapunov theory and output regulation technique, it is shown that the proposed control strategy can solve the FTOR problem. Finally, a numerical example is included for illustration. [ABSTRACT FROM AUTHOR]

This article proposes a low-complexity frequency equalization technique for pulsewidth modulation (PWM)-residual-aliasing reduction in closed-loop Class-D audio amplifiers to achieve both low total harmonic distortion plus noise (THD+N) and low quiescent current. Based on the comprehensive analysis on the phase shift delay between the audio input and the loop filter’s output for the prior PWM-residual-aliasing reduction technique, the proposed technique minimizes the non-idealities via a frequency-equalization path to enhance the cancellation ability of high-frequency PWM switching components. In this way, the PWM-residual-aliasing distortion is greatly suppressed, thereby permitting Class-D audio amplifiers to achieve a further-reduced THD+N for the entire audio band while operating at a low switching frequency ($f_{\mathrm {SW}}$), leading to less quiescent current consumption. Moreover, due to the minimized phase shift delay, the required bias current of the loop filter’s first operational amplifier for the target THD+N can be reduced, resulting in even lower quiescent current. Compared with other state of the arts, this work’s high-fidelity second-order Class-D audio amplifier, fabricated with cost-efficient 0.5- $\mu \text{m}$ CMOS technology, not only achieves a competitive minimal THD+N of 0.00091% for a 1-kHz input as well as the lowest maximal THD+N of 0.0027% over the entire audio band while operating at a low $f_{\mathrm {SW}}$ of 168 kHz but also features both the highest figure of merit (FOM) of 2536 and the lowest quiescent current of 0.4 mA, surpassing the prior arts by a factor of 2.3. [ABSTRACT FROM AUTHOR]

An important issue in wireline receivers (RX) is minimizing the area and power consumption while overcoming the channel attenuation with an equalizer. The greater the compensation for channel loss at the analog front end (AFE) of the RX, the lower the number of decision feedback equalizer (DFE) taps. Power dissipation and area can be reduced by reducing the number of DFE taps. This brief presents a technology that compensates for the channel loss with the proposed AFE based on a two-stage continuous-time linear equalizer (CTLE), low and high bandwidth amplifiers, and a gain controller. It sufficiently reduces the DC gain and increases the peak gain of the AFE by using a feedforward equalizer (FFEQ) and feedback equalizer (FBEQ). These equalizers result in an increase in the difference between the peak and DC gains and the gain difference at the fundamental frequency (f0) and 2nd subharmonic frequency (f1/2). The IC is fabricated in a 28 nm CMOS process, and the proposed architecture yields a BER less than 10−12 at 25.8 dB channel attenuation. At 25 Gb/s, the area and power efficiency of the proposed AFE are 1.19 pJ/bit and 0.01 mm2, respectively. [ABSTRACT FROM AUTHOR]

This article describes a method for the active control of the deformations on a magnetically levitated moving-magnet planar motor. The planar motor under consideration is comprised of a stator on a double coil array configuration and a mover with permanent magnets, and it is designed to perform positioning tasks with nanometer level of accuracy. Due to the spatially asymmetric, nonuniform force distribution on the magnet plate, mechanical deformations are induced to the mover, which can severely hinder the desired positioning accuracy. The proposed method overcomes this challenge by properly shaping the force distribution on the moving magnet plate, which is enabled by the presence of multiple coils interacting with the mover, corresponding to an “overactuation” scheme. As a consequence, the independent control of elementary deformation shapes (modes) is achieved. The proposed overactuation scheme is experimentally validated on a planar motor prototype, proving the efficiency of the proposed method during both standstill and motion. [ABSTRACT FROM AUTHOR]

This article presents a multispatial filtering module cascaded system (MSFMCS) for hyperspectral image classification (HSIC), which can serve as a paradigm to improve spectral–spatial classification. It includes multiple spatial filtering modules (SFMs) that are cascaded to particularly capture spatial information from the classification maps generated from the preceding modules. As a result, any spectral classifier (SC) can be used as an input to an initial/input module (IM). Through MSFMCS, its classification performance keeps improving as more SFMs are processed. To terminate MSFMCS, an automatic stopping rule is particularly designed by support vector machine (SVM) which is used not only as a classifier but also as a decision-maker. So, once an SC cannot be further improved, MSFMCS is terminated. One major benefit resulting from MSFMCS is its framework which can implement any arbitrary SC as its initial classifier in IM. Another is its ability in capturing additional spatial classification information module by module as the process progresses. A third one is no weights connected between modules so that no training phase is required like a feedforward neural network. Finally, the number of modules used in MSFMCS can be automatically determined by its stopping rule not predetermined empirically. To illustrate full advantages of MSFMCS in HSIC, three types of heterogeneous classifiers, pure-pixel-based SVM, mixed-pixel-based constrained energy minimization (CEM), and feature-extraction-based classifier—orthogonal total variation component analysis (OTVCA)—are used for experiments to demonstrate how MSFMCS can improve their classification performance. [ABSTRACT FROM AUTHOR]

In this article, to guarantee the good tracking performance of the precision motion system for various tracking tasks, an online iterative learning compensation method is proposed for closed-loop motion control systems. The prediction model is based on the closed-loop model of the linear second-order system with a proportional-integral-derivative controller, and an estimation term is added to deal with the influence of slow-varying uncertain disturbances. On the basis of the accurate state prediction, the dynamical feedforward compensation can be obtained, which suppresses the tracking error caused by the dynamical lag. Furthermore, in order to simultaneously compensate the errors caused by nonlinear factors such as uncertain disturbances and to guarantee the smoothness of the compensated trajectory, the optimal compensation gain is determined through online iterative calculation. The online iterative approach is similar to iterative learning control, but does not require several offline iterations of a repeating trajectory. Comparative experiments are carried out on an industrial motion stage. Various experimental results consistently demonstrate that the proposed compensation scheme can achieve the tracking accuracy comparable to iterative learning, while maintaining the robustness to trajectory changes and uncertain disturbances without reoffline iteration. [ABSTRACT FROM AUTHOR]