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The carry-over effect is a common phenomenon in the semiconductor manufacturing process, giving the process a dynamic nature. Dynamic models are more accurate but with a consequent increase in uncertainty. Therefore, it is very important to eliminate the uncertainty and disturbance at the same time. To this end, a run-to-run (RtR) control scheme based on discrete active disturbance rejection control (DADRC) is proposed in this work. The process recipe is calculated using the state error feedback law, relying on the extended state observer (ESO) to effectively suppress the total disturbance synthesized by model uncertainty and external disturbance. Considering that tool aging often leads to drift disturbances in semiconductor manufacturing processes, a model-assisted ESO with two extended states is designed to estimate process states and total disturbance. Then an optimal observer gain is derived to minimize the estimation error. Finally, the numerical and industrial cases provide compelling evidence of the effectiveness of the proposed control scheme in suppressing tool-aging drift disturbance and a remarkable degree of tolerance towards uncertainties in the system model’s order and parameters. [ABSTRACT FROM AUTHOR]

This article investigates the multirate disturbance rejection control problem for linear control systems with mismatched disturbance and measurement delay using predictor-based extended state observer. A new extended state observer together with output predictor is first designed to obtain the estimation values of system state and mismatched disturbance, where output predictor is used to compensate the influences of measurement delay and sampling of output. To attenuate the undesirable influence of mismatched disturbance, we then design a new sampled-data robust controller with disturbance compensation, and the updating rate of the proposed controller is allowed to be different from that of the sensor. Thanks to prediction and disturbance/uncertainty estimation and attenuation techniques, the disturbance rejection property of the resultant closed-loop control systems is enhanced despite the multirate and measurement delay. Some sufficient conditions are presented to ensure the stability property of the resultant control systems. We finally consider the application of the visual servoing control system for an inertially stabilized platform, and the experiment results verify superiorities of the predictor-based disturbance rejection control method proposed in the article. [ABSTRACT FROM AUTHOR]

In this article, a novel reinforcement learning (RL)-based robust control approach is proposed for quadrotors, which guarantees efficient learning and satisfactory tracking performance by simultaneously evaluating the RL and the baseline method in training. Different from existing works, the key novelty is to design a practice-reliable RL control framework for quadrotors in a two-part cooperative manner. In the first part, based on the hierarchical property, a new robust integral of the signum of the error (RISE) design is proposed to ensure asymptotic convergence, which includes the nonlinear and the disturbance rejection terms. In the second part, a one-actor-dual-critic (OADC) learning framework is proposed, where the designed switching logic in the first part works as a benchmark to guide the learning. Specifically, the two critics independently evaluate the RL policy and the switching logic simultaneously, which are utilized for policy update, only when both are positive, corresponding to the remarkable actor-better exploration actions. The asymptotic RISE controller, together with the two critics in OADC learning framework, guarantees accurate judgment on every exploration. On this basis, the satisfactory performance of the RL policy is guaranteed by the actor-better exploration based learning while the chattering problem arisen from the switching logic is addressed completely. Plenty of comparative experimental tests are presented to illustrate the superior performance of the proposed RL controller in terms of tracking accuracy and robustness. [ABSTRACT FROM AUTHOR]

For high-precision contouring tasks in a typical Cartesian motion system, multiaxis cooperation is a long-standing challenging issue. Inevitably, various factors pose substantial difficulty in the multiaxis cooperation leading to degraded contouring performance, such as the strong coupling effect between different axes, nonlinearity, the unknown dynamics due to the friction, and the difficulties in accurate system identification. To enhance the contouring performance of a flexure-joint dual-drive gantry system against the aforementioned issues, this article presents a hybrid active–passive robust control framework leveraging a model-free architecture. In this control scheme, all the coupling effects, nonlinearity, disturbance, and unknown dynamics are considered as “lumped uncertainty”. Then, a super-twisting sliding mode control method with a signum-type iterative learning law is proposed to passively suppress the lumped uncertainty during iterations; and an extended state observer is deployed to actively compensate the lumped uncertainty and ensure the establishment of sliding motion in the time domain. As supported by theoretical analysis, the proposed controller is shown to exhibit several important properties. First, the establishment of the sliding motion is guaranteed globally, in both the time domain and the iteration domain. Second, the properties of short establishment time of the sliding motion, fast convergence during the iterations, and low chattering are achieved. Moreover, a series of comparative experiments are conducted, and the proposed method is shown to be rather effective in achieving excellent contouring performance in the high-speed and complex-curvature contouring tasks, without relying on the system model. [ABSTRACT FROM AUTHOR]

Spherical motors capable of omnidirectional rotations in a ball joint provide a highly dexterous actuator system yet also present great challenges in developing a high-performance regulation and tracking controller due to the complex rotor dynamics. This article presents a complementary H2-H∞(C-H2-H∞) control method for precisely controlling the multidegree of freedom (DOF) orientation of spherical motors in presence of external disturbances as well as model uncertainties and inaccuracies. In order to deal with the tradeoff between the robustness and control performance resulted from conventional control methods, the proposed controller featured with a 2-DOF structure presents an H2 controller for a nominal plant and then complements it with an additional regulator designed in H∞sense to assure robustness. Unlike traditional H∞ or mixed H2and H∞ controller, the H∞regulation is conducted online in accordance with the monitored modeling mismatch in a way that does not adversely degrade the performance delivered by H2 control, hence, improving the conservativeness of the total control performance and leading to significant improvements of tracking accuracy and response time at same time. Both numerical simulations and experiments on a spherical motor testbed are conducted to validate the superior performance of the proposed controller versus conventional control methods. [ABSTRACT FROM AUTHOR]

Human intelligence, cognition, and skills can be applied to robots to accomplish challenging tasks through cooperative control between humans and robots. In this article, we propose two control frameworks for the human teleoperation of multiple robots under undirected and directed graphs. The two control laws are considered for two cases: 1) when both the relative positions and velocities of the robots are available, and 2) when only the relative positions are available. In this article, we first propose a controller that integrates adaptive neural networks, task-space synchronization, and robust control to address several practical issues related to uncertainty in robotic systems, (e.g., model inaccuracy, time delay, and disturbance). Second, another control algorithm using only the relative position information is addressed by using distributed observers on the remote side. As a result, the system is proven to be stable, and the tracking errors of position and velocity are uniformly and ultimately bounded. Finally, the effectiveness of the proposed control algorithms is verified by experimental demonstration. [ABSTRACT FROM AUTHOR]

Lower-limb exoskeletons are promising applications of robotic rehabilitation for people with motor impairment. As current studies have tailored the design of gait trajectories for the target users, realizing a high-precision motion control is a critical issue for safe and effective assistance. The walking assistance involves unique characteristic phases that embody different physical constraints and requirements for assistance. Conventional methods often utilized gain-switching control for time-varying adaptation. However, despite their intuitiveness as well as simplicity, the control performance was unsatisfying due to unmodeled responses by human behavior and continuous interaction with the external environment. To tackle these challenges, this study proposes a hybrid control method applied to the disturbance observer that can provide robust robotic rehabilitation. The proposed method adaptively identifies the exoskeletal system as a hybrid nominal model and online exchanges model-based tracking controllers parallelly to the gait phase of a user. Furthermore, a unique filter named allowance filter is introduced to compensate for the plant dynamics, preventing instability of the inverted plant and realizing digital implementation. In this article, a practical user with complete paraplegia participated in the experiments for verification of the proposed methods. [ABSTRACT FROM AUTHOR]

To cope with future goal of efficiency, next generation of more electrical aircraft is likely to embed reconfigurable microgrid for the primary electrical distribution. The stability analysis of such time-varying structure is challenging. Conventional linear and nonlinear approaches failed to produce proper answers from a practical point of view. Under conditions, passivity property of each equipment is sufficient to ensure stability of the overall microgrid. But with improper passivity-based control (PBC) design, the passivity property may not be propagated. The main contribution of this article is to propose a modified IDA-PBC procedure that will ensure the passivity properties of the system regarding its electrical inputs and outputs involved in the interconnection. Thus, for electrical microgrids resulting from the interconnection of controllable equipment, input/output filters, and electrical lines, if all the equipment embed the proposed control, the whole microgrid have passive properties and the global stability of the system is ensured. Moreover, this article will also present how to achieve the proposed control with high transient and robust performances thanks to nonlinear parameters’ estimator preserving the passivity proper. Experimental validation of the proposed control in representative electrical conditions is provided. [ABSTRACT FROM AUTHOR]

Tendon–sheath artificial muscle (TSAM) is a type of artificial muscle, which is widely used in robotic flexible endoscopies. The characteristics of the tendon force/position transmission were studied recently, but the control issue of the TSAM twist is still barely concerned. In this article, an effective robust control scheme is proposed for the TSAM twist, which achieves satisfactory tracking performance. First, a rigorous model was developed for the TSAM twist with consideration of parameters uncertainty and actuator input constraints, moreover, the disturbance of the discontinuous friction (Coulomb friction) was analyzed. Next, by applying some signal operation methods, a nonlinear robust controller was designed. In addition, the stability of the control strategy was proven theoretically by utilizing Lyapunov-based theory. Finally, a series of hardware experiments were conducted on a robotic flexible ureteroscope test-bed. The feasibility of the proposed model, the effectiveness and robustness of the designed control method were extensively validated. [ABSTRACT FROM AUTHOR]

Wound field synchronous motors (WFSMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To ensure high control performance of the EV-WFSMs, this article proposes a robust maximum torque per ampere (MTPA) control strategy based on a series of new sliding mode (SM) inductance observers. First, after establishing the model of a WFSM based on capacitive coupling, the current control scheme is designed for the MTPA control. Second, to eliminate the impacts of the inductance uncertainties on the MTPA control, the SM mutual inductance observer, q-axis inductance observer, and d-axis inductance observer are designed, which need to be implemented one by one. Then, the Lyapunov stability criterion is used to analyze the stability conditions for the observers. Moreover, considering that the observers are achieved by using the offline inductance information provided by the suppliers, the robustness against parameter mismatch is innovatively discussed at length, and an analytical method that can avoid estimation errors is developed. The proposed inductance identification techniques and MTPA control method are verified by both simulation and experiments, which are conducted on a 580-W three-phase WFSM drive. [ABSTRACT FROM AUTHOR]

A dual-loop robust control scheme and its property of performance separation are presented in this article. The dual-loop control scheme consists of two degrees of freedom for nominal and robust performances, with the nominal controller being any stabilizing controller in the observer-based state-feedback form and robust controller being a standard $H_{\infty }$ controller. When there is model error and/or disturbance, the robust controller is activated to compensate the nominal controller; otherwise, the dual-loop control returns to a single-loop nominal controller. We also show that the nominal and robust performances of the dual-loop control are independent of one another. As a result, the nominal and robust controllers can be designed separately offline, and then, online coordinated in the dual-loop control. Furthermore, the state-space realization and controller implementation are also provided. Finally, a two-wheeled robot with varying slip effect is considered as an illustrative example. Both simulation and experimental results show that the dual-loop control outperforms the classical robust control methods. [ABSTRACT FROM AUTHOR]

Steer-by-wire (SBW) system has been introduced to reduce the number of parts in a vehicle by eliminating the mechanical link between the steering wheel and the road wheel. Even though intensive research has been conducted on the SBW system, the performance of the actuator system that provides the desired steering feel still needs improvement. Therefore, this article presents a compact size torque controllable steering wheel module for the SBW system, where the actuator that provides the steering feel to the driver is modularized with the gear mechanism and the spring. The steering torque sensing mechanism is compactly integrated by placing the spring inside the gear mechanism. Thanks to the advantage in the torque measurement and control ability by the spring mechanism, high gear-ratio transmission can be employed in the proposed module, which can also address the low inherent mechanical impedance problem that may cause fail-safety issues in the SBW system. To enhance the impedance rendering performance of the proposed module, an internal model compensator which is composed of inertia and friction compensation is applied. The kinematics, statics, and dynamics of the proposed module are theoretically analyzed, and a model-based control algorithm is proposed based on this analysis result. Finally, the increase in the inherent impedance and the torque control performance of the proposed module are verified through experiments. [ABSTRACT FROM AUTHOR]

In this letter, a quasi-resonant extended state observer-based predictive current control (QRESO-based PCC) strategy is proposed for three-phase pulse width modulation (PWM) rectifier. Specifically, the QRESO is utilized to estimate the predictive value of system total disturbance and grid current at time instant k+1 in the stationary αβ frame, and the required control input at time instant k+1 is then calculated based on the principle of deadbeat predictive control. The proposed control scheme can achieve robust control against to the electrical parameter variation, accommodate the measurement noise, and also assure the satisfactory steady-state and dynamic performance for the PWM rectifier. Besides, the stability analysis of QRESO and the whole current close-loop control system with the consideration of QRESO in the discrete-time domain are presented. Finally, several hardware-in-the-loop test results of the proposed control strategy are provided and compared with the model free predictive current control based on linear extended state observer and the generalized integrator-extended state observer-based PCC strategy, which validate the effectiveness and superiority of proposed QRESO-based PCC strategy. [ABSTRACT FROM AUTHOR]

In this article, the geometric adaptive robust hierarchical control strategy is proposed for underactuated quadrotor, in which the negative effects of nonlinearities, uncertainties, and coupling are thoroughly considered and effectively attenuated. For handling nonlinearities, geometric control is adopted, i.e., the attitude error is directly defined on tangent space of rotation group, thus better attitude tracking performance can be achieved. To deal with uncertainties, a comprehensive dynamic model is established with compound damping terms representing the main aerodynamic effects and all inertia parameters including mass, inertia tensor, and center of gravity. Then, integrated direct/indirect adaptive robust control is developed with adaptive compensation of aerodynamic damping and complete inertia, which is beneficial to improve tracking performance. As for the coupling, hierarchical control architecture is utilized to relax the strong coupling between attitude and translational subsystems. Moreover, the stability of the overall closed-loop hierarchical system, together with transient performance and final tracking accuracy, has been rigorously analyzed without time-scale separation assumption. Finally, comparative experiments demonstrate the better performance of the proposed method in terms of tracking accuracy and robustness. [ABSTRACT FROM AUTHOR]

Starting from Fliess's model-free control (MFC) technique developed 15 years ago, this article aims to provide a systematic framework for characterizing, benchmarking, and generalizing this emerging control technique, with a particular focus on power electronics (PE). It examines the performance of MFC in terms of dynamic response, stability, and robustness, using the classical control theory as a basic tool. A theoretical comparison is conducted with the conventional linear control techniques on dynamic response and performance robustness. A generalized MFC theory and means to enhance its robustness performance are also highlighted. This article suggests that MFC, in contrast to the conventional understanding based on model-independent error dynamics, is practically a model-based control technique. Such model dependence characteristics under MFC become more severe for PE systems. However, following a new design principle, MFC is found possible to possess extraordinarily robust performance against model variations as compared to most existing model-based control methods. On top of that, stability margin is found to be the key bottleneck hindering the performance robustness of the existing MFC techniques. A new MFC with greater stability margin and performance robustness is proposed in this article. Comprehensive Pareto fronts analysis, simulations, and experiments are conducted on a buck converter system to verify the new understandings and conclusions drawn from the framework. With the new design principle and the new MFC, the system is able to demonstrate an almost constant dynamic response despite 25-fold circuit parameter (R, C, and L) variations and 1.85-fold input voltage variations. [ABSTRACT FROM AUTHOR]

Interior permanent magnet synchronous motor (IPMSM) with back electromotive-force (EMF) based sensorless field-oriented control (FOC) is widely used in medium-high speed applications. Unfortunately, the back EMF is too small to be estimated accurately during low speed operation. Hence, the current-frequency (I–f) control with controllable current vector is used for startup. However, the conventional I–f control is hard to realize the tradeoff between startup speed and load capacity, suffering high-acceleration and high-load startup failure. In this article, a current vector angle controller for sensorless IPMSM startup is proposed to adjust speed-up-stage current acceleration and constant-speed-stage current amplitude with load automatically. The proposed angle controller guarantees the current vector angle close to the maximum torque per ampere angle, which contributes to large startup torque, high operating efficiency and smooth transition from I–f startup to sensorless FOC. Furthermore, the robustness against parameters variation of angle controller is evaluated quantitatively. These theoretical advantages are verified in experiments with a 380 V, 1.5 kW IPMSM. In particular, the experimental results demonstrate that the proposed I–f control realizes full-load startup even with ${\rm{ \pm }}$ 50% flux linkage and ${\rm{ \pm }}$ 30% inductance variation. Furthermore, the startup acceleration auto-adjusts with load to reconcile startup speed and load capacity. [ABSTRACT FROM AUTHOR]

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs. Method: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Experimental results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications. [ABSTRACT FROM AUTHOR]

An analysis and controller design of double-input dc–dc converter (DIDDC) is introduced in this article. This converter is integrated with buck and boost converters at the front-end and a charge pump on the back-end. Salient features of this topology are as follows: (i) ripple content is low in source currents, (ii) both the dc sources are connected through an inductor, which gives rise to a smooth source current waveform, (iii) both the sources supply power to load either in standalone or simultaneously together, and (iv) two control–loops (one for source current regulation, while the second one for load voltage regulation) are sufficient enough to ensure power transfer/management from dc sources to load. To ensure power management on the input dc sources while keeping a stable closed-loop converter system, a multivariable diagonal controller is designed. The first diagonal controller performs the load voltage regulation task, while the second controller regulates input of source-2 dc current. From the control point of view, the double-input single-output converter becomes a two-input two-output control problem and exhibits control–loop interactions due to the power flow from two different sources to load through common elements. The impact of off-diagonal elements’ interactions is accounted by using decouplers. The H∞ robust control theory and linear matrix inequality (LMI) principles are adopted to design reliable controllers. The structured controller of fixed order (PID) is designed by enforcing the necessary type LMIs. To demonstrate the feasibility and effectiveness of multivariable controllers of the DIDDC, a 60/24 V to 48 V, 150 W, 50 kHz topology prototype is constructed, and the power management aspects are investigated experimentally. Load voltage regulation, source current regulation, load sharing on the input dc sources, and power management aspects of LMI-based controllers were tested extensively for all possible disturbances. In all these test cases, the closed-loop double-input converter system is stable and robust in rejecting a variety of disturbances created. [ABSTRACT FROM AUTHOR]

The control of a microgrid is quite challenging thus this work implements a robust and fast control technique based on an enhanced second-order generalized integrator with a modified frequency locked loop (ESOGI-MFLL) for switching operation between an islanded and the grid-connected mode. For phase angle estimation, a circular limited cycle oscillator frequency locked loop with an inner loop filter (CLO-FLL-WIF) is implemented instead of a conventional phase-locked loop technique to provide better synchronization performance for polluted grid voltages. This microgrid is combing a solar photovoltaic (PV) generation, a synchronous reluctance generator for pico-hydro generation, and the battery, which works in both standalone and grid-connected modes while feeding local loads uninterruptedly. The output power conversion of a PV array is influenced by irradiation of the solar PV array; therefore, a maximum power point tracking namely an incremental conductance method is used. The implemented control techniques are used for fundamental extraction of load components of voltage and currents for assessment of reference currents with their respective phases of synchronization while maintaining the required IEEE power quality standard-519. In addition, the comparative results validate the faster response of ESOGI-MFLL control algorithm to the existing control algorithm. Simulated and test results of a microgrid for islanded, switching mode, and grid-connected mode demonstrate its performance under load unbalance and generation perturbation. [ABSTRACT FROM AUTHOR]

In this article, a sliding mode control (SMC) is applied to regulate the delivered power of a symmetrical six-phase induction generator. For this, the SMC strategy associated to the field-oriented control strategy is used in order to adjust the inner current loops of a squirrel cage six-phase induction generator (SC6PIG). In this way, the control algorithm switches between positive and negative feedback according to the position of the operating point in the phase plane. This technique guarantees the robustness of the SC6PIG in case of the loss of one to three stator phases or machine converter legs. The switching line slope and the initial operating point have been set so that the system starts with a negative feedback, then it continuously switches back and forth to positive feedback in order to maintain the desired trajectory of the system. The simulation and experimental results demonstrate the effectiveness and the robustness of this technique since the delivered power of the SC6PIG presents the same dynamics in healthy or faulty modes with one to two phase losses. [ABSTRACT FROM AUTHOR]

Four-wheeled steerable mobile robots have been widely used in industrial fields. However, robust tracking control is hard to achieve with a fixed maneuver mode. Based on a novel internal coupled sliding mode control (ICSMC) method, this study handles the above challenge by constructing a mode-dependent switched mobile robot (MSMR), and then designing an asynchronous $H_{\infty}$ continuous stabilization control scheme. This method governs the system behaviors to perform adaptive-mode allocations asynchronously and smoothly. First, a unified expression of MSMR is formulated, which regards the maneuver modes as optional subsystems. Then, with the coupled sliding surfaces and modified reaching law, an enhanced chattering-free ICSMC technique is established to optimize continuous control inputs for each subsystem. Moreover, using a surveillant criterion that depicts the energy decaying rate, an evaluation rule is built to distinguish the mismatched subsystems and then realize autonomous mode switching for the concerned MSMR. Utilizing piecewise Lyapunov functional technique and mode-dependent average dwell time, new sufficient conditions are derived for global exponential stability and $H_{\infty}$ performance. Comparative experiments implemented on the developed MSMR are carried out to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Control inputs of fully constrained cable-driven parallel robots (CDPRs) are constrained by the positiveness of the cables tension, as cables merely apply tensile forces. The positive tension distribution (PTD) in CDPRs is usually guaranteed with iterative optimization techniques, utilizing the redundant actuation of the CDPR. The iterative nature of the conventional PTD limits their real-time application since the worst-case computation time of the iterative methods is not predictable. In addition, optimization methods are prone to model uncertainties. This article addresses the PTD problem in the fully constrained CDPRs with a control viewpoint. In the proposed approach, the PTD algorithm is an integral part of the controller, which explicitly generates positive values for the cables tension. To this aim, a saturation-type function is coupled with the controller, and its effect is compensated using a nonlinear disturbance observer. The stability of the proposed control scheme is also investigated in detail through Lyapunov’s second method, considering a nonsingular terminal sliding mode controller. Furthermore, the performance of the proposed methodology is compared with the conventional method for a six-degrees-of-freedom CDPR, in the presence of uncertainties. Finally, the effectiveness of the proposed control scheme is investigated through experiments. [ABSTRACT FROM AUTHOR]

H∞ control can guarantee the performance of a nominal ICPT system. However, the dynamic performance may suffer degradation when the parameter deviates from its nominal value. Thus, to enhance the overall dynamic performance of the ICPT system under parameter perturbations, an H∞ control method with a selected weighting function considering parameter perturbations is proposed in this article. First, a generalized state-space model is established to analyze the influence of parameter perturbations on the open-loop system in detail. After that, an uncertain model is established via frequency-domain analysis, generating a numerical solution of a weighting function used to describe the parameter perturbations. Then, the generalized plant with uncertainty is obtained and transferred into a standard H∞ optimal problem via small gain theory. Based on the proposed controller, the dynamic performance and robustness of the closed-loop system are analyzed and verified theoretically. Finally, simulation and experimental results further verify that the proposed H∞ control method can make the closed-loop system obtain good dynamic performance and strong robustness against parameter perturbations. The settling time is less than 6.8 ms without any overshoot when the system suffers parameter perturbations. Besides, the controller can maintain good dynamic performance in the startup and reference tracking experiments. [ABSTRACT FROM AUTHOR]

With the aim of reducing the number of sensors, this article investigates the single-sensor grid-following control for the LCL-filtered inverter system, and proposes an intuitive robust stability analysis method in the linear framework to handle power control and grid voltage variations in engineering practice. Specifically, through the use of a state-and-disturbance observer, the lumped-disturbance compensated suppression and nominal system full-state feedback technologies are equivalently realized. And the lumped-disturbance estimation carrying voltage phase information at the point of common coupling is further used as synchronization target of the phase-locked loop (PLL) to achieve grid-following control, and the resulting phase-shift is further corrected by accurate phase compensation. In addition, due to the nonlinear nature of the PLL and the power control and grid voltage variations, the system analysis is faced with the dilemma of complex nonlinear analysis or small-signal analysis without a fixed equilibrium point. In this case, a PLL small-signal model with bounded parameter perturbation characterizing power control and grid voltage variations is developed, and further incorporated into the output impedance modeling of the single-sensor grid-following controlled inverter. According to the edge theorem, the system stability in the studied uncertain region is analyzed via four edge output impedances, and appropriate control parameters are selected accordingly. Finally, the experimental results from a downscaled prototype are provided to verify the effectiveness of the proposed single-sensor grid-following control scheme. [ABSTRACT FROM AUTHOR]

This study presents the design of a robust control based on the sliding-mode theory to solve both; the stabilization and the trajectory tracking problems of nonlinear systems subjected to a class of full-state restrictions. The selected nonlinear system satisfies a standard Lagrangian structure affected by nonparametric uncertainties. A barrier Lyapunov function is used to ensure the state constraints by designing a time-varying gain, which guarantees the fulfillment of the predefined state constraints even under external perturbations. The proposed design methodology for the barrier sliding-mode control (BSMC) ensures the convergence of the sliding surface in finite time to the origin. Consequently, the asymptotic convergence of the states to the corresponding equilibrium point is achieved. The finite-time stability of the origin in the closed-loop system with the proposed controller has been demonstrated using the second Lyapunov stability method. The suggested controller was evaluated on a two-link robotic manipulator. Then, the obtained results showed better stabilization and tracking performances (while the restrictions are satisfied) than the traditional first-order sliding-mode or linear state feedback controllers. [ABSTRACT FROM AUTHOR]

Mechanical systems with (possibly fast) time-varying but bounded uncertainty are considered. The exact value of the boundary is unknown. All the designer can get is that the boundary values are in a (known) fuzzy set. On the basis, a robust control method is proposed, which can ensure the uniformly bounded and uniformly ultimately bounded of the controlled mechanical system. The control contains two flexibly selectable design parameters. We seek to choose the optimal parameters. For a superior performance, two fuzzy-set-based performance indices are proposed to reflect the transient performance as well as the steady-state performance. Each performance index can be influenced by two design parameters. However, influences are nonconciliatory. This poses a design dilemma: the increase of a parameter may harness one performance while inflict the other. Therefore, the “optimal” choice of the design parameters is not intuitively clear. To resolve this dilemma, the two-player Nash-based noncooperative game theory is adopted, which is a notable feature of this article. Once the problem is formulated, we show that there is always a Nash-equilibrium solution to the two-player problem. We also show how to find it. The approach is very general, which also can be extended to $n$ -player Nash game for future research. The control is applied to a compressor powered by permanent magnet synchronous motor (PMSM) as a demonstration. The resulting performance shows that this Nash-based robust control design is both practical and effective. [ABSTRACT FROM AUTHOR]

To optimize the current-control performance of the linear permanent magnet synchronous motor (LPMSM) drive system, a robust continuous time model-based predictive control for the LPMSM drive systems is proposed in this article. Since the current regulation technology is fundamental for obtaining the precise thrust of the LPMSM, a continuous time model-based predictive control is utilized in the current loop to achieve a fast dynamic response and accurate tracking accuracy. However, the predictive control is a model-based scheme, thus, the model uncertainties and parameter variations will influence the accuracy of stator current. To solve this problem, an augmented internal model disturbance observer is proposed to estimate the lumped disturbances and then compensate to the forward channel. First, the internal model equation of the current loop is constructed by the internal model control principle. Then, a new augmented matrix is constructed by introducing the error variables of the actual current and the internal model current. The observer control law is designed by the state feedback theory, and the observer parameters are optimized by pole assignment method. Finally, the experimental results demonstrate the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article addresses the synchronization control problem for dual-linear-motors-driven systems with model uncertainties and disturbances. An approximation-free robust synchronization control scheme based on cross-coupled control (CCC) frame is proposed to achieve high-precision tracking and synchronization performance, along with excellent uncertainties and disturbances rejection ability. More specifically, the CCC frame is designed to handle the asynchronous motion of two parallel motors. The main advantage is that the proposed method does not require the explicit system model, and any approximations utilized to handle the model uncertainties, such as estimation, identification, and online learning, are not required. Therefore, the computational burden and complexity of the controller are significantly reduced. Considering the importance of the transient and steady-state response, the concept and technology of prescribed performance are adopted to guarantee the control effect and state constraints. In addition, none of the high-order derivatives of desired trajectory, difficult to obtain directly in many applications, are used in the proposed controller. Furthermore, the stability and convergence performance of the closed-loop system are rigorously demonstrated. Finally, comparative experiments show the effectiveness of this study via a dual-driven H-type gantry. [ABSTRACT FROM AUTHOR]

The article proposes an adaptive robust controller with synchronization compensation for the Maglev wind turbine yaw system to achieve stable nacelle suspension and pitching suppression. First, two-degree-of-freedom suspension model is constructed with consideration of magnetic coupling and pitching interference, and then deduces a two-terminal suspension control model based on the inverse system. Second, a nominal suspension controller is designed based on the composite error; and then an adaptive synchronization controller is proposed to adjust coupling parameters based on reconstructed synchronous function; an adaptive tracking controller is used to online update dominant control input parameters. The robust controller is to compensate for the influence of the approximation errors during the parameters convergence process. It is proved that the proposed strategy can guarantee the globally uniformly ultimately bounded of all signals, and suspension tracking error and synchronization error can converge to the desired smaller neighborhood of zero. Finally, simulation and experimental results show that the system with the proposed strategy has the faster dynamic response, smaller steady error and synchronization error, and stronger robustness than other two strategies. [ABSTRACT FROM AUTHOR]

In order to improve the motion accuracy and robustness of the clutch mechanical leg for driving robot (CMLDR), and to improve the shift performance of the driving robot vehicle (DRV) for the engagement operation of CMLDR, a neural network adaptive robust control method of shift process for CMLDR with clutch transmission torque compensation is proposed. First, the dynamics model of the driving robot is established considering an external interference force and dynamic modeling error of the mechanical leg. Second, the upper clutch controller including a two-parameter shift module, a switch controller, and a finite-time linear quadratic regulator of the clutch transmission torque is designed. Third, the estimator of clutch engagement torque through a Kalman filter in the process of shift is constructed. Then, the neural network adaptive robust (NNAR) controller of the mechanical leg is designed. Finally, the proof of stability analysis for NNAR controller is conducted. Experiment and simulation results show that the designed controller of the mechanical leg has a strong antiinterference ability. And the DRV accurately tracks the target speed after compensating for the transmission torque. [ABSTRACT FROM AUTHOR]

In this article, we propose a sliding fractional-order (FO) control strategy to provide robustness feature for integrating a multilevel converter into the power grid under the system parameter uncertainties, nonlinear load alterations, and grid voltage sag. All dynamics achieved from the input capacitors and output inductances of the multilevel converter are utilized to design the proposed sliding fractional surfaces (SFSs) such that a robust operation of the grid-tied converter-based system is achieved. Then, an SFS-based Lyapunov function is built to assess the proposed controller stability. A thorough analysis of the Lyapunov function is carried out for distinguishing suitable boundaries of the controller coefficients when the error components of the system state variables are changed, accordingly. Using the derivative of Lyapunov function and SFS, a comprehensive evaluation is executed to detail the regulation procedure for the robustness and fractional PI controller coefficients. Finally, a closed-loop system using the converter current dynamics aims to discern the differences between different values of FO. Experimental tests using dSPACE-1202 along with MATLAB/Simulink simulations are employed to verify the effectiveness of the proposed control technique for providing proper harmonic compensation, enhanced power quality, unity power factor, voltage sag tolerance, and very low total harmonic distortion grid current. [ABSTRACT FROM AUTHOR]

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly applied to surveillance tasks, thanks to their excellent mobility and flexibility. Different from existing works using UAVs for video surveillance, this paper employs a UAV team to carry out collaborative radio surveillance on ground moving nodes and disguise the purpose of surveillance. We consider two aspects of disguise. The first is that the UAVs do not communicate with each other (or the ground nodes can notice), and each UAV plans its trajectory in a decentralized way. The other aspect of disguise is that the UAVs avoid being noticed by the nodes for which a metric quantifying the disguising performance is adopted. We present a new decentralized method for the online trajectory planning of the UAVs, which maximizes the disguising metric while maintaining uninterrupted surveillance and avoiding UAV collisions. Based on the model predictive control (MPC) technique, our method allows each UAV to separately estimate the locations of the UAVs and the ground nodes, and decide its trajectory accordingly. The impact of potential estimation errors is mitigated by incorporating the error bounds into the online trajectory planning, hence achieving a robust control of the trajectories. Computer-based simulation results demonstrate that the developed strategy ensures the surveillance requirement without losing disguising performance, and outperforms existing alternatives. Note to Practitioners—The paper is motivated by the covertness requirement in the radio surveillance (also called eavesdropping) by UAVs. In some situations, the UAV user (such as the police department) wishes to disguise the surveillance intention from the targets, and the trajectories of UAVs play a significant role in the disguising. However, the typical UAV trajectories such as standoff tracking and orbiting can easily be noticed by the targets. Considering this gap, we focus on how to plan the UAVs’ trajectories so that they are less noticeable while conducting effective eavesdropping. We formulate a path planning problem aiming at maximizing a disguising metric, which measures the magnitude of the relative position change between a UAV and a target. A decentralized method is proposed for the online trajectory planning of the UAVs based on MPC, and its robust version is also presented to account for the uncertainty in the estimation and prediction of the nodes’ states. [ABSTRACT FROM AUTHOR]

Navigation control of microrobots in vivo has great potential in precision medicine and has attracted considerable attention in recent years. The control performance of the existing methods is considerably affected by hysteresis nonlinearity. This article presents a robust control method that can overcome hysteresis influence in navigating a microrobot actuated by an electromagnetic coil system. A motion planner that combines the breadth-first search (BFS) method and genetic algorithm (GA) is used to plan a reliable and flexible trajectory for the microrobot navigation. To compensate for hysteresis nonlinearity existing in the system, the Prandtl–Ishlinskii (PI) model is introduced. A robust controller that integrates adaptive sliding mode control (ASMC) and nonlinear disturbance observer is designed to guarantee the stability and accuracy of the microrobot in motion. Experiments have been performed to demonstrate the effectiveness of the proposed approach. The success of this research will advance the microrobot navigation for in vivo applications. Note to Practitioners—The motivation of this article is to eliminate the effect of hysteresis caused by the electromagnetic system on microrobot motion. Existing research on hysteresis compensation mostly uses inverse model or online model identification, which is not suitable for real-time control of the microrobot. In addition, the disturbance and uncertainty caused by the microfluidic environment at different flow rates will also affect the control performance of the microrobot. To solve the above problems, an adaptive robust control method is developed in this article. Experimental results have shown that the proposed method can successfully control the microrobot in the in vitro and in vivo environment. [ABSTRACT FROM AUTHOR]

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness. Note to Practitioners—This article addresses the collaborative formation problem of multiagent systems that has potential applications in transportation and disaster relief. The design of robust and energy-saving coordination strategies is challenging in heterogeneous multivehicle systems. The proposed distributed method is suitable for large-scale uncertain heterogeneous systems, and the use of the dual adaptive event-triggered strategy reduces the data transmission rate. [ABSTRACT FROM AUTHOR]

Most of the existing classification systems assume that the data used is high-quality labeled. However, the labeling process in real-world may inevitably introduce corruptions into labels which can confuse the performances of classifiers. In this paper, based on Broad Learning System (BLS), we propose a novel label noise tolerant method to classify the pattern with corrupted labels. The standard BLS has shown promising efficiency and accuracy in general classification, but its learning process is prone to be affected by the noisy labels. Here, by detailed probabilistic analysis, we first give the reason for lacks of robustness in standard BLS. Then a maximum likelihood estimation-based objective function is derived for robust classification. In addition, a manifold regularization term is integrated to preserve the local geometry of data, which makes the model to be more robust and flexible to learn the output weights. Given some basic assumptions on the approximation errors, the obtained model can be transformed to a graph regularized reweighted BLS problem. The negative effects of noisy labels in data can be inhibited adaptively by assigning reasonable weights. Theoretical analysis and extensive experiments are provided to demonstrate the robustness and effectiveness of the proposed robust BLS model, especially for the case of large amounts of noisy labels. [ABSTRACT FROM AUTHOR]

High speed becomes an important development direction of flywheel energy storage system (FESS) for higher energy storage density. However, the high speed leads to a wide-range and rapid speed variation (tens of thousands of revolutions in seconds) and a limited frequency modulation index, both of which aggravate the current harmonics and deteriorate the robustness of the discharge control, and even lead to instability. To address this issue, this article proposes a robust and practical discharge control strategy for high-speed FESS with RLC filter, which realizes speed adaptation and harmonic suppression. In this scheme, the linear extended state observer and two-degree-of-freedom internal model control (TDOF-IMC) are applied to FESS in the framework of linear active disturbance rejection control (LADRC). Besides, considering that high-speed FESS usually require sensorless control due to magnetic bearings, a reduced-order Luenberger observer for the investigated FESS is proposed. The salient feature of this strategy is to introduce the TDOF-IMC containing FESS dynamic information into LADRC, so that the discharge control is both practical and robust. Experiments and simulations verify the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

In this article, islanding detection, control, grid synchronization, and power share techniques have been considered for the seamless operation of ac microgrid in grid-connected mode, islanded mode, and during the transition between both modes. A master–slave organization coordinates the power distribution among microgrid sources. The control scheme of each source is designed with a nested loop robust linear quadratic regulator and mixed $H_{2}/H_{\infty }$ optimal controller. The islanding detection algorithm is developed with synchro-extraction transform and optimal support vector machine (OSVM). An optimal linear Kalman filter-based synchronous reference frame phase locked loop (OLKF-SRFPLL) method is considered for the reconnecting of the microgrid to the utility grid. For optimal parameter tuning in the OSVM and LKF-SRFPLL methods, a particle swarm optimization technique is used. The seamless operation of the proposed ac microgrid system is verified in MATLAB/SIMULINK, and the practical feasibility is verified with an experimental photo-type setup and dSPACE 1202 control kit. Simulation and experimental results are presented to illustrate the seamless operation of the proposed ac microgrid system. [ABSTRACT FROM AUTHOR]

To realize the robust control of multirotor unmanned aerial vehicle (UAV) systems, adaptive multilayer neural dynamics (AMND) controllers are proposed and analyzed. The proposed AMND controllers with the strong anti-perturbation property can drive multirotor UAVs to track time-varying tasks and deal with parameter uncertainty problems. First, the design method of the general multilayer neural dynamics (MLND) controllers is introduced and analyzed. Second, based on the design method, the attitude angles, height, and position controllers of a UAV system are designed. Third, according to the adaptive control theory, a novel AMND controller is designed, which can self-tune the parameters of the UAV. Finally, the proposed AMND method applies to a real-world hexrotor UAV system to illustrate its reliability. Mathematical analysis, computer simulations, and experiments verify the reliability, stability, and effectiveness of the proposed controllers which are used to track time-varying tasks. [ABSTRACT FROM AUTHOR]

This article is dealt with the problem of trajectory tracking with prescribed performance for a family of underactuated surface vessels (USVs) under model uncertainties and disturbances. The prescribed performance means that the USV tracks a given trajectory with the arbitrarily predefined speed of response and accuracy. The existing prescribed performance control (PPC) solutions and the traditional robust control approaches for USVs may have the singularity issue or cause a discontinuous control signal. Thereby, a new-type adaptive PPC strategy is put forward in this article. The adaptive technique is devoted to tackling model imperfections as usual, whereas the constraint-handling technique is adopted in a novel way. Herein, we first construct an auxiliary variable instead of using the approach angle or azimuth angle. Then, we impose constraints on the position error, not the tracking error, and the auxiliary variable, simultaneously. In this way, the predefined performance is achieved by moreover a singularity-free continuous control action. These theoretical findings are illustrated via a comparative simulation study. [ABSTRACT FROM AUTHOR]

Connecting permanent magnet synchronous machines (PMSMs) in parallel to a two-level three-leg inverter is a simple but effective way to reduce the required components in a multimachine system. The parallel PMSM system is an affine nonlinear system with external disturbances; therefore, designing a robust controller for it is a challenging issue, especially when machine parameters are not the same. In this article, to deal with the nonlinearity of the system, a Takagi–Sugeno (T–S) fuzzy model is constructed for it first. Then, a nonlinear feedforward controller is introduced to increase the speed tracking performance of the system as well as reduce the complexity of the constructed T–S fuzzy model. To cope with the external disturbances, guaranteed cost control (GCC) techniques and the H∞ stability analysis method are combined to achieve zero-error speed tracking and external load rejection. In this way, the optimization of GCC is derived, and an H∞ stability criterion is analyzed based on the T–S fuzzy model instead of the original system. Finally, the feasibility and the performance of the proposed method are tested through an experiment. The experiment results also demonstrated that, compared with the traditional strategy, the proposed method can make the parallel PMSM system consisting of different machines free of unstable risk. [ABSTRACT FROM AUTHOR]

Microgrid's voltage regulation is of particular importance during both grid-connected and islanded modes of operation. Especially, during the islanded mode, when the support from the upstream grid is lost, stable voltage regulation is vital for the reliable operation of critical loads. This paper proposes a robust and data-driven control approach for secondary voltage control of AC microgrids in the presence of uncertainties. To this end, unfalsified adaptive control (UAC) is utilized to select the best stabilizing controller from a set of pre-designed controllers with the minimum knowledge required from the microgrid. Two microgrid test systems are simulated in MATLAB to verify the effectiveness of the proposed method under different scenarios like load change and communication link failure. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of vehicle model parameters, modeling errors and external disturbances, the performance of path tracking control system is poor, especially under high velocity and large curvature extreme conditions. To address this issue, this paper presents a novel gain-scheduled robust control strategy based on linear parameter varying system with model predictive control (MPC) and H∞. Firstly, fully considering the influence of the time-varying characteristics of vehicle velocity and tire cornering stiffness on the path tracking system model, a novel linear parameter varying system model is built for path tracking control of autonomous vehicle. Then, a path tracking robust controller is designed based on gain-scheduled approach, and the linear matrix inequality (LMI) is applied to solve optimization problem, in which MPC and H∞ robust control theory are applied to the controller design process. Finally, the simulation experiments have verified that the proposed novel robust control strategy can improve the path tracking accuracy and ensure the vehicle lateral and roll stability, especially under high velocity and large curvature extreme conditions. Meanwhile, the proposed robust controller shows superiority to suppress the parameters uncertainty, modeling error and external disturbance. [ABSTRACT FROM AUTHOR]

State-of-charge (SoC) imbalance and bus voltage deviation are two of the main problems in autonomous dc microgrids. Based on this concern, this paper presents an improved dual-quadrant SoC weighted control strategy and a distributed optimization control method to achieve SoC balance, ensuring accurate power-sharing and bus voltage recovery. Firstly, this paper couples the injected/released power with the current SoC and observed average SoC value to weight the droop coefficient, which is based on the charge/discharge mode for the energy storage system. Then a secondary controller is designed based on distributed optimal control to eliminate the bus voltage deviation caused by the line impedance difference. The proposed optimal control method optimizes the average bus voltage to the nominal value and achieve accurate power-sharing by constructing the correlated variables and voltage independent intermediate variables exchanged among bulk energy storage units (ESUs). Since the voltage observer cannot accurately observe the true average bus voltage under the communication delay, the proposed distributed optimal control method without the voltage observer can ensure that the average bus voltage is optimized to the nominal value, thus improving the robustness of the control system. Finally, the correctness and effectiveness of the proposed method are verified in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

This study has the aim of introducing a new type of trajectory tracking robust controllers for a class of rehabilitation robotic system considering the articulations restrictions. The robotic device consists of a suspended biped configuration. The suggested robust control considers the application of state depending gains which provide finite-time convergence for the tracking deviation. The state restrictions are fulfilled by the implementation of controller gains estimated by a class of the controlled tangent barrier Lyapunov function. Stability analysis for the tracking error yields the explicit design of the state dependent gains. The rate of convergence for the controller design is enhanced using a matrix inequality convex optimization method. Based on the forward complete characteristic of the suggested rehabilitation device, it is allowed using a finite-time convergent super-twisting-based differentiator to concrete an output feedback realization of the proposed controller. A computerized model of the tendered rehabilitation robot provides a reliable testing platform to the suggested roust controller. Numerical evaluations appear to serve as an indirect confirmation for the tracking error convergence, satisfying the articulation restrictions, and the effect of the gain optimization design. For comparison purposes, the regular state feedback control design is considered as benchmark. The faster convergence of the mean square estimation of the tracking error justifies the design of the proposed control design as well as the state feedback structure justifies the origin is a fixed-time stable equilibrium point for the space of tracking error at the same time that state space restrictions remain satisfied. The experimental evaluations of the proposed controller justifies the barrier controller which, in spite of the modeling uncertainties and the implementation issues, tracked the reference trajectories. [ABSTRACT FROM AUTHOR]

The four-wheel omnidirectional mobile robot usually suffers disturbed or unstable lateral motion under harsh terrain conditions (such as uneven or oiled ground). Generally for such a challenging situation, the lumped disturbances and interconnected states render available coupling solutions difficult to achieve demand-satisfied performance. This article proposes a novel decoupled fractional supertwisting sliding mode control (FST-SMC) method by i) constructing an inverse system-based decoupling to form a pseudolinear composition system; ii) presenting an enhanced nominal sliding law for chattering mitigation, and iii) designing an unbiased multilayer fuzzy estimator with gain-learning capacity to compensate for the lumped disturbances actively. Given that the identified disturbances can be directly reflected in the FST-SMC law, this method guarantees an accurate and robust control without causing gain overestimation. Theoretical analysis is offered to verify the asymptotic stability. Under harsh terrain conditions, experimental results validate the effectiveness of the proposed FST-SMC method. [ABSTRACT FROM AUTHOR]

Unknown multiple tonal noise and band-limited random noise can be both generated in ductwork systems. This article has constructed a LQG controller with an augmented Youla (Q) parameterized adaptive regulation approach to minimize the residual error at the end of the duct segment where both types of noise signals are supposed to be attenuated. The construction approach of the Youla parameterized controller based on a LQG controller is first presented. Then, the recursive least squares adaptive algorithm is developed, and the convergence of the algorithm and the robustness of the closed-loop control system are analyzed. The effectiveness of the proposed adaptive control approach is experimentally evaluated in a duct noise attenuation testbench platform. The results illustrate that the proposed adaptive noise control method can effectively attenuate the unknown multiple tonal noise and band-limited random noise simultaneously in the ductwork system. [ABSTRACT FROM AUTHOR]

As one of the core components of high-end manufacturing equipment, the linear motor (LM) plays an important role in high efficiency and high quality production of the equipment. In this article, a high-precision robust control method with periodic dynamic compensation is proposed for the most common repetitive trajectory tracking task in the manufacturing industry. To overcome the influence of unknown periodic dynamics on the control accuracy, a B-spline wavelet neural network observer is designed to estimate the uncertainties. Moreover, the control framework in the form of robust control is employed to ensure the stability of the closed-loop system under other unknown aperiodic disturbance. Finally, the high-precision control for LM-driven stage in high-speed environment is realized, and the comparative experiments show that the control accuracy of the proposed method is significantly improved at least 28.9% compared with the other three existing control methods. [ABSTRACT FROM AUTHOR]

Policy search and model predictive control (MPC) are two different paradigms for robot control: policy search has the strength of automatically learning complex policies using experienced data, and MPC can offer optimal control performance using models and trajectory optimization. An open research question is how to leverage and combine the advantages of both approaches. In this article, we provide an answer by using policy search for automatically choosing high-level decision variables for MPC, which leads to a novel policy-search-for-model-predictive-control framework. Specifically, we formulate the MPC as a parameterized controller, where the hard-to-optimize decision variables are represented as high-level policies. Such a formulation allows optimizing policies in a self-supervised fashion. We validate this framework by focusing on a challenging problem in agile drone flight: flying a quadrotor through fast-moving gates. Experiments show that our controller achieves robust and real-time control performance in both simulation and the real world. The proposed framework offers a new perspective for merging learning and control. [ABSTRACT FROM AUTHOR]

Direct model predictive control (DMPC) has become an effective control alternative for the grid-tied modular multilevel converter. However, classical DMPC of the grid-tied MMC relies on accurate system model. System parameter variations will deteriorate the control performances of power, grid current, and circulating current. Besides, the inherent fewer switching actions of DMPC will also lead to poor circulating current performance. To solve these issues, this work proposes a robust DMPC solution for the grid-tied MMC. Novelties lie in two aspects. First, a virtual-flux (VF) based online inductance estimator is proposed, which utilizes the VF estimation error caused by inductance deviation. It achieves fast and accurate estimation of AC equivalent inductance, and reduces the dependency on system parameters. Thereafter, we propose a hybrid predictive control frame with a simple proportional-resonant (PR) controller, which considerably suppresses the circulating current. Finally, experimental results verify the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]