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The control of a microgrid is quite challenging thus this work implements a robust and fast control technique based on circular limited cycle oscillator frequency locked loop with inner loop filter (CLO-FLL-WIF) for switching operation between an islanded and the grid-connected modes. This microgrid combing solar PV (Photovoltaic) generation, SyRG (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 solar PV array and has low efficiency, therefore, a MPPT (Maximum Power Point Tracking) namely an INC (Incremental Conductance) method is used. In the implemented system, the interfaced power converter is gated in the grid-connected mode and islanded mode using same enhanced second order generalized integrator with a modified frequency locked loop (ESOGI-MFLL) control method. The comparative results validate that the response of ESOGI-MFLL in comparison to the existing control algorithm, is faster under load and generation perturbations. The implemented control techniques are used for fundamental extraction of load component of voltage and current for assessment of reference currents with their respective phases of synchronization while maintaining the required IEEE power quality standard-519. Simulated results of a microgrid for grid-tied PV-BES-SyRG are obtained in MATLAB/Simulink platform to demonstrate its performance.

The belt-drive control enables the mechanical system to gain the advantages of lightweight, vibration absorption and high torque, while it brings strong nonlinear disturbances due to friction, backlash and flexibility. Therefore an improved disturbance observer control method based on the dual-rate loop structure is proposed in this paper to suppress these disturbances. Only a linear low-frequency device model is required which indicates this method avoids the dependence on accurate identification of the nonlinear uncertain systems. The rate information on motor-side is replaced with the input signal of inner loop due to the high bandwidth of inner motor loop, which greatly simplifies the design of control system. In case of insufficient system bandwidth, the proposed method integrates the favorable information of the load-side and the motor-side under the DOB framework. And the speed-commutation spikes and chatters resulting from disturbances on the flexible transmission chain can be more effectively suppressed by the proposed control structure compared with the conventional methods.

This article deals with the problem of network-based path-tracking control of an underactuated unmanned surface vehicle subject to model uncertainties and unknown disturbances over a wireless network. A two-level network-based control architecture is proposed, including a local inner loop and a remote outer loop. In the remote outer loop, an event-triggered line-of-sight guidance law is designed to achieve path tracking while reducing the network burden for the remote control at the kinematic level. In the local inner loop, an extended state observer is employed to estimate the unknown disturbances due to the model uncertainties and environmental disturbances. Based on the estimated information from the extended state observer, an event-triggered anti-disturbance control law is developed to reduce the execution rate of actuators at the kinetic level. The stability of the closed-loop path-tracking system is proved based on the input-to-state stability and cascade stability theory. The effectiveness of the proposed network-based method for path tracking of the USV is verified via experiments.

Due to the lightly damped resonance and intrinsic nonlinearities, it is difficult for the piezoactuated nanopositioning stage to realize high-bandwidth and high-accuracy control. To handle these limitations, in this work, a dual-loop control scheme based on state-feedback-based modal method is designed to both actively damp and stiffen the resonant mode and to suppress the effects of nonlinearities of the piezoactuated nanopositioning stage. In this scheme, the state-feedback-based modal controller is first designed in the inner loop to enlarge both the damping ratio and natural frequency of the first resonant mode. Then, a proportional-integral (PI) controller is utilized in the outer loop for eliminating the tracking errors caused by other disturbances and nonlinearities including hysteresis and creep. To maximize the control bandwidth of system under the proposed dual-loop scheme, an optimization method is thus proposed for simultaneously tuning the parameters of the inner and the outer loop controllers. Finally, to validate the proposed dual-loop control scheme, comparative experiments are carried out on a piezoactuated nanopositioning stage. Results demonstrate that the proposed control scheme improves the bandwidth of the system from 497 Hz (with PI control) and 1543 Hz (with a commonly used positive acceleration, velocity, and position damping control and a PI controller) to 6546 Hz, which is 664 Hz larger than the first resonant frequency of the original system, validating the effectiveness of the proposed dual-loop scheme on high-bandwidth control.

The microgrid (MG) which effectively utilizes distributed energy resources (DERs) is crucial to the modern power system. However, since the islanded MG with high penetration of DERs inherently lacks sufficient inertial support, the frequency regulation is challenging for the voltage source converter (VSC)-based MG. This paper presents a cascaded model predictive control (MPC) scheme for both the outer and inner loops of primary control, which improves frequency regulation capability with the advantage of satisfactory dynamic response and high tracking accuracy. In the outer loop, to enhance dynamic frequency characteristics, the MPC-based virtual synchronous generator (VSG) method with two control objectives is proposed. According to the movement direction of frequency under different load switching cases, the corresponding frequency response process speeded up or slowed down properly. In the inner loop, an improved finite-set double vector MPC (DV-MPV) is proposed to follow the output reference from the outer loop, which contributes to accurate frequency control by reducing tracking error, and faster dynamic response as well. The simulation and experimental results further demonstrated the effectiveness of the proposed method.

Wheels are vital components on railway vehicles and may encounter various problems during long-term operation, e.g., wear and defects. The existing preventative reprofiling strategy with fixed reprofiling interval and cutting depth causes huge maintenance costs. In order to reduce life cycle cost (LCC), this paper proposes an optimal life cycle reprofiling (LCR) strategy for risk-based preventative maintenance of train wheels. First, the probabilistic models of wheel wear, reprofiling, and defects are built. Then, Markov decision process is constructed to model a complete wheel degradation process in its life cycle with sequential decision-making of reprofiling. Meanwhile, the reward matrices are designed considering LCC and safety-related costs. Further, a two-loop optimization scheme is proposed to pursue the optimal LCR strategy minimizing LCC. In the inner loop, policy iteration is adopted to pursue the optimal reprofiling policy. In the outer loop, gradient descent with momentum is employed to optimize the overhaul limits. This scheme achieves fast convergence and global optimization of maintenance decision-making for both routine and overhaul reprofiling. Finally, the mechanism of optimal reprofiling policy is exemplified, including the principles of prolonging, protecting, and abandoning. The main trade-offs in cutting depth and overhaul limits provide the feasibility for optimization. A case study and field tests demonstrate that through controlling wheel degradation and life span, LCR strategy can not only reduce the LCC of wheel maintenance, but also improve the reliability and safety of wheel operation.

A cable-driven segmented manipulator (CDSM) has superior dexterity for operations in confined space due to its light-slender body and redundant degree of freedoms (DOFs). However, its inverse kinematics resolving and configuration planning are very challenging due to the complex structure and strict constraints. In this article, we propose a two-layer geometric iteration (TLGI) method for inverse kinematics resolving and configuration-constrained Cartesian path planning. The computation efficiency is largely improved and singularities are avoided. First, the end-effector attitude is decomposed into a direction vector and a rotation angle. The former and the end-effector position are combined into state variables of the inner layer, and the latter is treated separately as the state variable of the outer layer. Then, the TLGI method enables to rapidly reach the desired 6-DOF pose by two-layer iterations, i.e., the inner and outer loop iteration. Second, during the inner loop iteration, the CDSM is modeled as an equivalent articulated arm whose end-effector position and direction is the same as that of CDSM, but its links length and joint angles depend on the current configuration of CDSM. Then, the efficient forward and backward reaching inverse kinematics (FABRIKs) method is extended to apply on CDSM so that it can fast reach the inner state variables. During the outer loop iteration, three different rotation cases, i.e., the rotating around the end, root, and both end and root, are designed to switch automatically to reach the outer state variable iteratively. Moreover, by parameterizing geometric constraints of the environment, a TLGI-based configuration-pose simultaneous planning method is also put forward to efficiently achieve additional configuration constraints for operations of CDSM in confined space. Finally, the proposed method is verified by both the simulations and experiments.

This paper presents a differential flatness-based approach to trajectory planning and tracking for helicopters and demonstrates this strategy for time-optimal autonomous helicopter shipboard landing. For planning and outer-loop tracking control, helicopter dynamics are captured by a simplified nonlinear model that is shown to be differentially flat, which enables the transformation of the original nonlinear system dynamics into an equivalent linear form with endogenous nonlinear constraints. For time-optimal trajectory planning, this equivalent model is used to construct a computationally-efficient optimization problem for free end-time end-state problems, where the decision-space is that of the trajectories of the flat outputs and their derivatives. While the attitude dynamics are stabilized by an inner loop controller, for tracking the planned trajectory a flatness-based outer loop control law is designed to stabilize the unstable zero dynamics. Further, analytical guarantees that relate inner loop tracking error to outer loop robust stability and tracking performance are derived. Simulations are performed on a high-fidelity numerical UH-60 helicopter model under ground effect and ship airwake disturbances, together with ship motion profiles collected from a naval environment to validate the proposed approach for realistic landing scenarios.

Secure state estimation (SSE) is a problem to defense false-data injection attacks. This study designs an online distributed SSE of heterogeneous multiagent systems under homologous attack. A triple-loop observer is proposed to estimate the state and attack signal simultaneously. The inner loop keeps the estimations of the attack signal the same using average consensus. The middle loop adjusts the estimations via residual information. The outer loop runs when system measurements change. A sufficient condition that estimations asymptotically converge to the real value is obtained and proved. Finally, the proposed observer has been tested on the global positioning system.

The paper deals with the low-sidelobe shaped-beam pattern synthesis (SBPS) problem under amplitude constraints, including the controllable dynamic range ratio (DRR) and the predetermined excitation amplitude. The formulated problem is nonconvex owning to the existing nonconvex constraints. To this end, a set of auxiliary variables are introduced to reformulate the original problem, and then a double-loop iterative algorithm based on the penalty dual decomposition method (PDD) framework is proposed. The proposed approach solves the reformulated problem by updating primal variables with block successive upper-bound maximization (BSUM) algorithm in the inner loop and updating the dual variables and penalty parameter with the constraint violation strategy in the outer loop. One important advantage of the proposed approach is that all of its subproblems have closed-form solutions, which guarantees that the reformulated problem can be effectively solved. Numerical simulations are carried out to validate the proposed approach’s superiority with both isotropic element pattern and active element pattern. The results show that the proposed approach can obtain a lower sidelobe level (SLL) and a smaller DRR when compared with the state-of-art approaches.

This paper presents design of a robust fractional order proportional-integral (FO-PI) prototype controller for a grid connected photovoltaic (PV) system. The robustness in face of parameter variation is handled by designing a suitable controller considering sensitivity function, gain margin and phase margin for both inner and outer control loops. As FO-PI controller is not yet available in physical form and due to complexity involved in grid-tied PV system, we propose an alternative way of hardware implementation of inner loop FO-PI controller by using fractor through RC ladder network. Firstly, an emulator circuit of the inner current loop comprising the FO-PI controller (including fractor and the proportional gain) along with the transfer function of inverter and filter is developed and the response is verified through a real-time simulator (OPAL-RT). Finally, the FO-PI part of the emulator circuit is interfaced with the PC target to form a hardware-in-loop (HIL) by employing OPAL-RT. The performance of the complete system is verified through OPAL-RT simulator. The result shows the improved robustness by the proposed control design method.

In this paper, a nite-time trajectory tracking control problem is studied for a rodless pneumatic cylinder system with disturbance in an inner-outer loop framework. In the inner loop system, the generalized nonlinear extended state observer and the inner-loop controller are designed to estimate and compensate the matched disturbances, respectively. The generalized nonlinear extended state observer designed in this paper achieves desired convergence performance under relaxed existence conditions. In the outer-loop system, a super-twisting extended state observer and an outer loop controller are designed to estimate and compensate the unmatched disturbances, respectively. The super-twisting extended state observer is designed to realize nite-time stable by using the super-twisting technology. Finally, experiment results are shown that finite-time control performances are obtained by the proposed control strategy.

The current small-signal models of inverters that mimic synchronous generators usually use the steady-state equation of feeder line and load, and ignore the dynamic characteristics of voltage and current controller of inner loop. Thus, some important dynamics of the system will be omitted. To capture the dynamics more accurately, firstly, a small-signal model of a three-phase isolated inverter that mimics a synchronous generator was developed in this paper, and a comparison between the corresponding simulink model and the small-signal model was presented to validate the model correctness. Then, participation factor was calculated, which showed the relation between the state variables and eigenvalues. Based on the model, several root locus plots as a function of the system parameters was obtained. An individual eigenvalue sensitivity analysis of a certain eigenvalue close to the real axis disclosed different parameters’ contribution to the oscillation mode. Finally, a disturbance test with simulink model and the small-signal model under multiple sets of parameters was carried out and the results can accurately reflect the oscillation mode, which verified the correctness of the parameters effect on the conjugate eigenvalues.

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

Wireless charging is an attractive technology that often promises increased mobility for electrical devices. However, commonly-available wireless charging systems are generally intolerant to misalignment between the charger and the receiving device due to the need for inductive coupling between the two, defeating the promise of mobility. Many techniques have been studied to address this issue, including improved electrical circuits, novel charger coil designs, and the use of sensors for detecting misalignment. In this paper, a new arrangement of sensing coils is used to detect lateral misalignment between a moving wireless charger and a mobile robot. Data from the sensing coils are used in a dual-loop control of the robot. The inner loop controls the robot velocities and consists of a linear quadratic regulator with integral action. The outer loop provides reference velocities to the inner loop based on the readings of the sensing coils. Using this method, an experimental mobile robot maintains alignment within 2 cm with respect to a 5-W wireless charger that moves up to 0.145 m/s. This automated alignment method is a low-cost solution that enables mobile systems, such as autonomous vehicles, to wirelessly charge while the charger is moving.

This article proposes new common ground bridgeless power factor correction rectifiers suitable for use in any application which requires a positive or negative dc power supply. The main advantage of the proposed rectifiers, compared to previous works, is the provision of the common ground between input and output, which eliminates electromagnetic interference (EMI) associated with high rates of change of voltage and consequently reduces the need for EMI common mode filtering. The converter also provides step-down and step-up operation, and facilitates positive or negative output voltages with a low number of semiconductor devices operating simultaneously. High power factor, acceptable grid-side current quality, and high efficiency are also achieved. Two variants are presented, referred to as type-I and type-II, which offer common ground positive and negative dc voltages, respectively. Closed-loop control of the converters is provided by a dead-beat current controller in the inner loop. Experimental results are presented for a 500-W prototype, operating from 220 and 110 Vrms input to $\pm$ 48 and $\pm$ 200 Vdc output. The experimental results demonstrate the capability for step-down and step-up ac-to-dc power conversion with a peak efficiency of 96.8% and 96.6% in the positive and the negative outputs, respectively.

The alternating direction method of multipliers (ADMM) is an efficient optimization method for solving equality constrained problems in internet of things (IoT) applications. Recently, several stochastic variance reduced ADMM algorithms (e.g., SVRG-ADMM) have made exciting progress such as linear convergence for strongly convex (SC) problems. However, SVRG-ADMM and its variants have an outer loop where the full gradient at the snapshot is computed, and their outer loop contains an inner loop, in which a large number of variance reduced gradients are estimated from random samples. This loopy design makes these methods more complex to analyze and determine the inner loop length, which must be proportional to the condition number to achieve best convergence, and is often set to O(n) as a suboptimal choice, where n is the number of samples. To tackle these issues, we propose an efficient loopless variance reduced stochastic ADMM algorithm, called LVR-SADMM. In our LVR-SADMM, we remove the outer loop and replace it with a biased coin-flip, in which we update the snapshot with a small probability to trigger the full gradient computation. Moreover, we also theoretically analyze the convergence property of LVR-SADMM, which shows that it enjoys a fast linear convergence rate for SC problems. In particular, we also present an accelerated loopless SVRG-ADMM (LAVR-SADMM) method for both SC and non-SC problems. Various experimental results on many real-world data sets verify that the proposed methods can achieve an average speedup of 2× in the SC case and 5× in the non-SC case over their loopy counterparts, respectively.

Body-in-white (BIW) is a typical frame structure formed by a great many thin-walled structures, and the optimal design of its lightweight and crashworthiness is a typical nonlinear dynamic response optimization problem with multiple design variables. Here, we propose a thickness-based subdomain hybrid cellular automata (T-SHCA) algorithm to solve the lightweight design of BIW under side collision, in which there are two loops: one is the outer loop to conduct crash finite element analysis, calculate output responses, and update internal energy density (IED) and target mass; the other is the inner loop to adjust cell thicknesses according to IEDs of current cell and its neighboring cells to make the actual mass of current cells converge to target mass. The concept of "subdomain cellular automata (SCA)" model was introduced, so that the T-SHCA can solve nonlinear dynamic response optimization in discrete and separated design spaces. The step IED target update rule and the cell thickness update rule based on a PID control strategy were implemented in the inner loop to improve the global optimal solution search capability and the robustness of the proposed algorithm, respectively. The thicknesses optimization of a BIW was carried out by the proposed method and a parallel efficient global optimization algorithm to verify its convergence and efficiency. The results show that the T-SHCA algorithm can be implemented to effectively solve nonlinear dynamic response structural optimization problem with many thickness variables in discrete and separated design spaces.

The goal of this work is to develop the next generation of coordinated optimal planning and control schemes for real-world robotic applications, with cost-effective intelligent robots that can safely and robustly perform the tasks at hand. This article presents a dual-loop optimal hierarchical control scheme for robotic manipulators consisting of outer and inner loops. The outer kinematic control loop in the operational space provides a joint velocity reference signal to the inner one. The kinematic control is formulated as a closed-loop optimal control approach to trajectory generation where the desired target (possibly time-varying) is obtained by acting upon the feedback from the actual state of the robot. The kinematic control is obtained using a closed-form analytic solution of the Hamilton-Jacobi-Bellman (HJB) equation. The proposed methodology defines the task in terms of the integral cost function, which results in a global optimal solution. The inner dynamic control loop consists of a neural network (NN)-based adaptive critic (AC) optimal tracking control scheme. The online NN approximator-based dynamic controller learns the infinite-horizon cost function related to inner loop error dynamics in continuous time and calculates the corresponding optimal control input to minimize the cost function forward in time. The stability and the performance of the proposed control scheme are shown theoretically via the Lyapunov approach and also verified experimentally using a 7-DOF Barrett WAM robot manipulator. The warehousing applications of the proposed dual-loop control scheme have been demonstrated experimentally in an exact warehouse setting.

This paper addresses the attitude reorientation problem under the pointing-constraint zone, angular velocity limits, and actuator constraints. The ERG-based control scheme is designed such that the attitude is stabilized, while the state and control constraints stay in the allowable regions defined by the Lyapunov invariant subset. The disturbance observer-based control law is developed in the inner loop of ERG that enables attitude stabilization in the presence of external disturbance. The navigation layer is utilized to manipulate the reference state to enforcing constraints satisfaction. The effectiveness of the proposed constrained attitude control algorithm is then verified through numerical simulations.

This paper presents a dynamic neural network based adaptive impedance control method for redundant robots under multiple physical constraints. In order to provide optimal contact performance without an accurate environment model, an adaptive impedance learning method is proposed to establish the optimal interaction between robot and environment. In the inner loop, a theoretical framework of constraint optimization is constructed, and then a dynamic neural network is established to compensate the nonlinear dynamics, and compliance to physical limitations is also satisfied. These limitations include joint angle restriction, angular velocity restriction, angular acceleration restriction, and torque restriction. Theoretical analysis proves the stability of the closed loop system. Numerical results show the effectiveness of the proposed control scheme.

Despite the successes of deep reinforcement learning (RL), it is still challenging to obtain safe policies. Formal verifi- cation approaches ensure safety at all times, but usually overly restrict the agent’s behaviors, since they assume adversarial behavior of the environment. Instead of assuming adversarial behavior, we suggest to focus on perceived safety instead, i.e., policies that avoid undesired behaviors while having a desired level of conservativeness. To obtain policies that are perceived as safe, we propose a shield synthesis framework with two distinct loops: (1) an inner loop that trains policies with a set of actions that is constrained by shields whose conservativeness is parameterized, and (2) an outer loop that presents example rollouts of the policy to humans and collects their feedback to update the parameters of the shields in the inner loop. We demonstrate our approach on a RL benchmark of Lunar landing and a scenario in which a mobile robot navigates around humans. For the latter, we conducted two user studies to obtain policies that were perceived as safe. Our results indicate that our framework converges to policies that are perceived as safe, is robust against noisy feedback, and can query feedback for multiple policies at the same time. QC 20211215

This paper proposes a novel concept of flexible dual-polarized multiple-input multiple-output (MIMO) belt strap antenna with triple band. It can be integrated into the wearable biomedical network base station (WBNBS) of intelligent Internet of Medical Things (IoMT), setting up a communication link with smart mobile devices, thereby hosting wireless wearable sensors to track the vital signs of human body. The proposed antenna includes three identical antenna units equidistantly distributed on the belt strap. Each unit is composed of two linearly-polarized dipoles placed orthogonally. The goat horn-shaped outer loop and M-shaped inner loop produce the first and second bands, respectively, and their second harmonic jointly forms the third band. The arc-shaped coupled ring is connected with one dipole arm, which is conducive to tune impedance matching. The effects of belt strap bending and antenna radiation on the body are analyzed. Specific absorption rate (SAR) values are reduced by attaching the absorbing-shielding layer. As a proof-of-concept, the proposed antenna is fabricated for verification. Both measured and simulated results demonstrate the excellent performance with a realized gain of 2.2-3.1 dBi and radiation efficiency of 46.1-80.1% in −10-dB impedance bandwidths of 2.37-2.7 GHz, 2.8-3.22 GHz, and 5.41-6.03 GHz. Finally, a smart belt prototype system is manufactured and tested. A link between wearable sensors and smartphones/computers is set up via WBNBS, where vital signs could be digitally displayed and monitored. It could provide a health risk alarm for a serious pandemic.

In this paper, a resilient ESOGI-FLL-WIF (Enhanced Second Order Generalized Integrator with Frequency Locked Loop and Inner Loop Filter) control algorithm is implemented to enhance the power quality and to regulate the voltage/frequency of a microgrid. This control has a simple structure, fast convergence, and excellent dynamic and steady-state response. The ESOGI-FLL-WIF control algorithm is used for assessing the fundamental constituent from harmonics and DC bias infected current. ESOGI-FLL-WIF control strategy tracks the frequency and tunes to the frequency fluctuations in transient situations. To have a fair comparison, the performance of ESOGI-FLL-WIF control algorithm is compared with already existing controls. This three-phase four-wire islanded system consists of a solar PV (Photovoltaic) array and pico-hydro turbine-driven SyRG (Synchronous Reluctance Generator). As an alternative to conventional generators, SyRG is used because its economical (no permanent magnets in the rotor) and has high efficiency (no electrical losses in the rotor). The four-leg converter compensates neutral current and controls the reactive power injection under unbalance load condition. Whereas, for active power management, BES (Battery Energy Storage) is incorporated in the system. A prototype of the system is developed and tested under different situations of load unbalancing and solar insolation change to confirm the effectiveness and viability of the control strategy.

This article proposes a direct-voltage controller, based on sliding modes, for regulating the output voltage of voltage-source converters (VSCs) equipped with LC filters. Grid-forming VSCs used in microgrids with pulsed loads must provide fast dynamic response and strong rejection of load perturbations. The new voltage control scheme uses a single-stage sliding-mode voltage controller with state feedback. The typical two-loop cascaded topology using a current controller in the inner loop and a voltage controller in the outer loop is not employed. The sliding-mode control (SMC) contains three components: 1) a feedforward effort that sets the ideal operating point, 2) a switching control law based on SMC, and 3) a sliding-mode equivalent control. The sliding variable is defined with proportional–derivative (PD) dynamics, and thus, it provides inherent active damping properties. The SMC improves the robustness with respect to load perturbations and modeling errors, whereas the equivalent control adds damping and reduces the response time to large load transients. The article describes the design and gain selection, and provides a simulation-based sensitivity analysis for this scheme. Two variants are tested in standalone operation: with and without the P term of the PD component. Experimental results show that, in both cases, the response to pulsed loads is very fast (about 1 ms) and accurate.

In order to have a better convergence and accuracy for solving the ion-flow field problem, a novel and general numerical approach is proposed. In the past, the framework of the traditional mesh based method has a dilemma that the Kapzov boundary condition can be imposed properly, and it must have two loops: the “well-posed” problem is solved in the inner loop and the secant based method is applied to impose the Kapzov assumption in the outer loop. In contrast, the proposed method solves the ion flow field problem from the perspective of the inverse problem. The original boundary value problem is transformed into a regularized optimization problem based on the prior information about the smooth ion distribution on the conductors. The objective function is separated into two parts and minimized by the alternating direction iterative method. In contrast to the traditional methods, the proposed method has removed the redundant iterations and the contentious simplifications. Numerical experiments show that the performance of the proposed method is superior to the traditional method and the results obtained by the proposed method agree better with the physical law than the traditional method. the new method presents a general and rigorous way to analysis the ion-flow field problem. National Natural Science Foundation of China under grant 52077111 and partly by Fund of State Grid Corporation of China (State Grid) under Grant SGSNKY00SPJS2000031.

Untethered magnetic miniature robots have attracted much attention in recent years, due to their minimally invasive features in biomedicine and highly flexible traits in micromanipulation. Autonomous control is one of the essential issues for magnetic microrobots, however, which remains challenging, especially for complex and large-workspace environments. To this end, this paper proposes a novel navigation framework, using a home-designed eye-in-hand mobile-coil system. A three-step motion planner is designed to know entire surroundings and generate optimal paths, which contains environment reconstruction, position registration, and modified A* path searching. Moreover, a double-loop motion controller is designed to make the microrobot accurately follow the computed path and intelligently avoid collisions: the inner loop performs mechanism following; the outer loop decides appropriate magnetic fields for actuation, which includes a disturbance observer (DOB), a fuzzy logic modifier (FLM), and a model predictive controller (MPC). Experiments demonstrate the effectiveness of the proposed strategy: feasible trajectories in a maze-like environment of diameter 230 mm can be planned, and autonomous navigation of microrobot is realized with both global accessibility and local adaptability.

Aiming at the low-frequency oscillation (LFO) phenomenon in high-speed railways, this paper proposes a feedback linearized virtual inertia control (VIC) strategy of the traction converter based on a sliding mode observer (SMO) to improve the robustness of the system and suppress the occurrence of LFO. First, to optimize the total harmonic distortion of the traction grid-side current, the inner loop controller is designed as the feedback linearization control. Then, the voltage outer loop is designed as a combination of the VIC designed in this paper and SMO, which enhances the system inertia and reduces the impact of external interference on the system, while reducing the controller's demand for electrical power on the DC-side. Furthermore, a small-signal model is established to analyze the stability of the single-phase vehicle-grid system under the proposed control. The influence of the control parameters on stability is discussed in detail. Finally, the comparison of software simulation and hardware-in-the-loop experimental results between traditional PI control, feedback linearization control, feedback+SMO, feedback+VIC and the proposed control method shows that the proposed control has better dynamic and static characteristics, and can effectively inhibit the occurrence of LFO.

This paper addresses the robust attitude control problem for quadrotors subject to model mismatch and disturbances. A dynamic inversion based attitude control scheme is proposed, which consists of an outer loop attitude controller and an inner loop angular acceleration controller. The attitude controller is designed based on the Nonlinear Dynamic Inversion (NDI) to precisely linearize the nonlinear dynamics between the angular acceleration and the attitude. An onboard actuator model-based Incremental Nonlinear Dynamic Inversion (INDI) controller is designed in the angular acceleration control loop to improve the robustness against the model mismatch and disturbances. Meanwhile, the onboard actuator model with a modified structure eliminates the oscillation phenomenon when the sampling rate of the controller is higher than that of the actuator. Numerical simulations and flight tests demonstrate the effectiveness and robustness of the proposed controller in comparison with the PID controller.

There is growing awareness that errors in the model equations cannot be ignored in data assimilation methods such as four-dimensional variational assimilation (4D-Var). If allowed for, more information can be extracted from observations, longer time windows are possible, and the minimization process is easier, at least in principle. Weak constraint 4D-Var estimates the model error and minimizes a series of linear least-squares cost functions using the conjugate gradient (CG) method; minimising each cost function is called an inner loop. CG needs preconditioning to improve its performance. In previous work, limited memory preconditioners (LMPs) have been constructed using approximations of the eigenvalues and eigenvectors of the Hessian in the previous inner loop. If the Hessian changes signicantly in consecutive inner loops, the LMP may be of limited usefulness. To circumvent this, we propose using randomised methods for low rank eigenvalue decomposition and use these approximations to cheaply construct LMPs using information from the current inner loop. Three randomised methods are compared. Numerical experiments in idealized systems show that the resulting LMPs perform better than the existing LMPs. Using these methods may allow more efficient and robust implementations of incremental weak constraint 4D-Var.

Purpose This paper aims to investigate the distributed formation control problem for a multi-quadrotor unmanned aerial vehicle system without linear velocity feedbacks. Design/methodology/approach A nonlinear controller is proposed based on the orthogonal group SE(3) to obviate singularities and ambiguities of the traditional parameterized attitude representations. A cascade structure is applied in the distributed controller design. The inner loop is responsible for attitude control, and the outer loop is responsible for translational dynamics. To ensure a linear-velocity-free characteristic, some auxiliary variables are introduced to construct virtual signals in distributed controller design. The stability analysis of the proposed distributed control method by the Lyapunov function is provided as well. Findings A group of four quadrotors with constant reference linear velocity and a group of six quadrotors with varying reference linear velocity are adopted to verify the effectiveness of the proposed strategy. Originality/value This is a new innovation for multi-robot formation control method to improve assembly automation.

This paper aims to investigate a coupled orbit-attitude control strategy for a kind of novel spacecraft, solar sail, to track the given orbit in Earth-Moon 3-Body dynamic environment in presence of the matched and mismatched disturbances, attitude control saturation, orbital modeling error and parametric uncertainties. A cascaded triple-loop control structure is proposed to deal with the strong couplings between the orbit and attitude systems. The inner loop focusing on the orbital effects on attitude dynamics, an adaptive saturation controller is proposed to achieve attitude angular tracking, where the uncertain inertia, unknown matched disturbance and saturated attitude control torque are compensated by combining the unknown knowledge. The middle loop is to handle the orbit effects on attitude kinematics facing the mismatched disturbance. In the outer loop, the effects of attitude system on orbit dynamics are deal with, where an adaptive orbit controller is designed considering the uncertain optical parameter and orbital modeling error. The proposed control structure efficiently simplifies the coupled orbit-attitude control design for solar sail. In contrast to traditional coupled controllers for solar sail, the proposed control laws do not require exact knowledge of parametric uncertainties, disturbances and orbit modeling errors. The combination of unknown information reduces the number of estimated parameters as well. The numerical simulation results demonstrate the effectiveness of the proposed control strategy.

In this article, a novel robust observer-based nonlinear control approach is proposed for quadrotors with unmodeled dynamics and external disturbances, wherein the tracking errors are restricted effectively. Two nonlinear disturbance observers are proposed to deal with uncertainties in the inner and outer loops, respectively. On this basis, a robust observer-based nonlinear control approach is put forward for quadrotor systems. Specifically, in the outer loop subsystem, a robust observer-based control scheme is proposed, which can reduce unexpected tracking errors for quadrotors effectively. Subsequently, based upon the geometric methods, the coordinate-free attitude controller and the nonlinear disturbance observer are integrated as a robust controller in the inner loop subsystem. Based upon full nonlinear dynamics, the solutions of the closed-loop system are proven to be uniformly ultimately bounded by means of rigorous Lyapunov analysis. A series of comparative experiments consisting of the implementation of a nonlinear proportional-integral-derivative (PID)-type controller, an observer-based sliding mode controller, and the proposed controller are conducted to demonstrate the remarkable performance of the proposed method in terms of higher tracking accuracy and stronger robustness.

Direct power control (DPC) is widely used in grid-connected inverters. First, considering the effects of phase-locked loop (PLL), voltage outer loop, power inner loop, control delay, and frequency coupling, a broadband admittance model of the grid-connected inverter with DPC is established. The admittance characteristics of grid-connected inverters with DPC and closed-loop current control (CCC) are compared and analyzed. Due to the different inner loop control objects, the components produced by the power calculation in DPC and the Park's transformation in CCC cause the admittance differences between the grid-connected inverters with DPC and CCC. Then, the effects of grid impedance, output power, and control parameters on system stability are analyzed. It is revealed that the stability of the grid-connected inverter with DPC is very sensitive to grid impedance and easily affected by output power and inner loop bandwidth, while the grid-connected inverter with CCC has a better adaptability to weak grid, high output power, and inner loop bandwidth. Moreover, the PLL bandwidth has little effect on the stability of the grid-connected inverter with DPC, but has a great impact on the stability of the grid-connected inverter with CCC when the inverter is connected to weak grid. Finally, the correctness of the analysis is verified by experiments.

In this paper, a grid interface current control strategy is presented for a DC microgrid, which aims to reduce the disturbance from PV generation and the load variation to the main grid without a grid interface converter. The grid interface current is directly controlled by a battery DC-DC converter within the DC microgrid. Based on a comprehensive analysis of the battery DCDC converter and interface current control, the control system has been mathematically modelled. This enabled two transfer functions to be derived that reflect the dynamic response of the inductor current to the duty cycle variation (inner loop), and the dynamic response of the grid interface current to the inductor current variation (outer loop). Experimental study has been done to assess the effectiveness of the proposed control strategy. The experimental results indicate that the proposed control strategy has a good performance to control the grid interface current without an interface converter, regardless the variations of both PV and the load conditions.

To coordinate the contradiction between artillery launching performance indexes under parameter uncertainty, this paper proposes an artillery structural dynamic responses optimization method based on robust Nash game theory. First, a multi-flexible body dynamic model for a 155 mm caliber artillery is established, which coupling the interior ballistic model, recoil force model, and balance mechanism model. Secondly, the live firing experiment is carried out to verify the accuracy of the established multi-flexible model. Then the muzzle vibration and maximum chamber pressure are selected as the players in the game. Because these two indexes can represent the most critical contradictory indexes of artillery, namely the firing accuracy and power. Afterward, to reduce the computational time, the BP neural network surrogate model is constructed to replace the original multi-flexible body dynamic model. Finally, the double-loop approach is adopted to search for the robust Nash equilibrium. The inner loop optimization is used to determine the worst-case scenario caused by the parameter uncertainty. The outer loop optimization is referred to as the robust Nash equilibrium solution process. The results show that the artillery structural dynamics responses have been significantly improved.

In the past, PI control algorithm and hysteresis control algorithm are not accurate in islanding detection, which leads to poor control effect. In view of this problem, a single-phase inverter grid connected control method based on wireless sensor network is proposed. According to the wireless sensor network architecture, the current loop closed-loop structure is designed, and the grid connected control model based on wireless sensor network is constructed. Under the support of equivalent diagram, the control target of grid connection is determined and the output current of inverter is adjusted to realize the adjustment of inverter output power. According to the circuit vector diagram of grid connected operation, the current tracking control strategy is designed to determine the vector relationship between the voltage on the inductance of the inverter output side and the feed forward voltage of the grid. The control strategy of the outer and inner loop of the single-phase inverter is corrected by constructing the mathematical model of the controlled object of the single-phase grid connected inverter. The experimental results show that the method has high accuracy, the maximum error of grid connected voltage is 0.14 kV, the maximum grid connected current is 0.11 kA, which is basically in accordance with the actual value, and has good control effect.

The design of a cascade controller is demonstrated for a buck–boost converter that is combined with two control loops consisting of inner and outer controllers. The outer loop is implemented by a fractional‐order proportional‐integrated‐derivative (FO‐PID) controller that works as a voltage controller and generates a reference current for the inner control loop. To provide faster dynamic performance for inner loop, a self‐tuning regulator adaptive controller, which tries to regulates the current with the help of a novel improved exponential regressive least square identification in an online technique, is designed. Moreover, in the outer loop, to tune the gains of the FO‐PID controller, a novel algorithm of antlion optimizer algorithm is used that offers many benefits in comparison with other algorithms. The system provided by the boost mode is a non‐minimum phase system, which creates challenges for designing a stable controller. In addition, a single loop controller is proposed based on a PID controller tuned by a particle swarm optimization algorithm to be compared with the cascade controller. Cascade loop can present significant benefits to the controller such as better disturbance rejection. Finally, the strength of the presented cascade control scheme is verified in different performing situations by real‐time experiments.

With the developments of intelligent and autonomous robotic technology, robots are usually designed to confront sophisticated tasks such as automated assembly, which requires both high-speed positioning capabilities and compliance with unknown contact environments. As we know, a high-performance motion tracking control in free space can achieve efficient and accurate positioning, while impedance or force control shows superior performance in terms of sensitive force and compliance with unknown contact environments. However, it is still challenging to achieve both high-performance motion tracking and compliance within one single control framework, especially in unknown contact environments. To this end, in this article, a unified motion/force/impedance approach for unknown contact environments is proposed by robust model-reaching control with dynamic trajectory adaptation. Specifically, the overall control scheme includes two loops: the outer loop replans the trajectories of motion and force in real time to meet the environmental constraints, which are estimated by the recursive least squares estimation law; in the inner loop, the robust model-reaching control law is proposed to realize a target model, which is designed to establish a dynamic relationship between the motion and force tracking errors of the replanned trajectories. Then, by changing the matrices of the target model, the compromise between motion tracking and force tracking can be achieved, as well as different control objectives. Experiments are conducted on a seven-degree-of-freedom manipulator to validate the advantages of the proposed scheme.

Multiloop constant on-time (COT) controlled buck converter uses two weight resistors for sensing inductor current and detecting output voltage in its inner loop. When an output capacitor with a small equivalent series resistance (ESR) is employed, the converter will suffer the risk of instability due to different weight settings of inductor current and output voltage using a proportional–integral (PI) compensation gain in the outer voltage loop. However, no attention has been paid for their stability effects, which are important for designing the inner loop and outer voltage loop. To address these issues, a piecewise linear model is established for multiloop COT-controlled buck converter. Circuit parameter-related bifurcation behaviors and control weight-related dynamical distributions are explored to demonstrate these stability effects. Furthermore, by deriving an approximate discrete model, a control weight-related stability criterion is expressed explicitly, from which stability boundaries for dividing the normally stable and abnormally unstable operation parameter regions are thereby yielded. Besides, control weight-related transient performances under different circuit parameter settings are analyzed. Finally, PSIM circuit simulations and hardware circuit experiments verify the theoretical analysis well.

In this article, a practical fixed-time disturbance rejection controller is investigated for quadrotor attitude tracking. The disturbance rejection is achieved by applying fixed-time disturbance observers (FxTDOs), and the practically fixed-time convergence is proved via homogeneous theory and Lyapunov method. Then, a dual-loop fixed-time controller is designed for quadrotor attitude tracking, and the disturbances are estimated by FxTDO in the inner loop. Finally, the efficiency and robustness of the proposed method are illustrated by simulations and experiments in real flights.

The growth process of Czochralski (Cz) silicon single crystal is a dynamic time-varying system with nonlinearity, strong coupling, large hysteresis, and uncertain model. Traditional model-based control methods are difficult to achieve satisfactory crystal growth control effects, and it is difficult to ensure that the crystal quality meets the actual process requirements. Therefore, from the perspective of data-driven modeling and control, this paper proposes a model predictive control method for the crystal growth process with a V/G soft-sensing model for measuring crystal quality. First, because the V/G value to measure crystal quality is difficult to obtain directly, a hybrid variable weighted stacked autoencoder random forest (HVW-SAE-RF) soft-sensing model based on data-driven V/G value is established. Here, the HVW-SAE is used to extract the deep quality-related features of the measurable process data, and the RF is used for the regression prediction of the SAE output layer; Second, using the dual closed-loop control strategy, the inner loop is based on the HVW-SAE-RF soft-sensing model of the V/G value, and the predictive PI control method is used to control the V/G value closely related to the crystal quality within a reasonable range, and the outer loop based on real-time estimation of V/G values to achieve nonlinear model predictive control (NMPC) of crystal diameter; finally, the effectiveness of the proposed method is verified based on the industrial production process data of silicon single crystal.

This paper deals with the controller design and closed-loop asymptotic stability analysis of a DC–DC boost converter based on the singular perturbation theory. Due to the nature of two-time scales with fast and slow dynamics in this converter, the cascade control structure is used to control it. This control system has two control loops: an outer loop to regulate the output voltage based on a proportional-integral (PI) controller and an inner loop to regulate the inductor current based on a sliding mode controller. These controllers within each of the loops are designed based on the perturbation theory to satisfy the constraints considered for the converter operation and to guarantee the asymptotic stability of the closed-loop system over a wide range of the converter initial state conditions. The numerical results show that with a proper selection of the outer loop PI controller parameters, the asymptotic stability and optimal performance of the closed-loop system are satisfied. Moreover, the robustness of the proposed control method is shown by the numerical experiments under step parameter uncertainties and disturbances.