Abstract Machine learning-based image super-resolution (SR) has garnered increasing research interest in recent years. However, there are two issues that have not been adequately addressed. The first issue is that existing SR methods often overlook the importance of improving the quality of the training dataset, which is a crucial factor in determining SR performance, regardless of the training method employed. The second issue is that while some studies report high numerical metrics, the visual results remain unsatisfactory. To address the first problem, we propose a new image down-sampling method to obtain higher-quality training datasets. To tackle the second problem, we present a new image super-resolution model based on a large-size convolution kernel and a multi-path algorithm. Specifically, we use an adaptive large-size convolutional kernel to extract features from the image based on the size of the input image, and a residual network to generate a deeper model to retain more details of the original input image. Experimental results demonstrate that the proposed multilayer downsampling method (MDM) can significantly improve the visual quality compared to traditional downsampling methods. Moreover, our proposed method achieves the best peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values compared to several typical SR algorithms. Furthermore, subjective evaluation by human observers reveals that our method retains more details of the original image and produces smoother high-resolution images. Our proposed method effectively addresses the two aforementioned issues, which leads to improved SR performance in terms of both quantitative and qualitative measures.

Active disturbance rejection control (ADRC) is promising for permanent magnet synchronous machine (PMSM) speed regulation system. However, the control performance of ADRC scheme is generally affected by the measurement noise introduced by position sensors. To solve this problem, a high-order nonlinear extended stated observer (NESO) is proposed in this paper to directly estimate the motor speed. The bode diagrams of the high-order NESO based measurement noise suppression system obtained by frequency-sweep approach are illustrated to show its frequency domain characteristics. Taking full advantage of nonlinear control and high-order observer techniques, the proposed strategy can maintain satisfactory noise suppression performance without sacrificing the robustness of PMSM system. Comprehensive experimental results are conducted to verify the superior properties of the proposed control strategy.

In this paper, two efficient robust fuzzy model predictive control algorithms are investigated for discrete nonlinear systems with multiple time delays and bounded disturbances. The famous Takagi-Sugeno (T-S) fuzzy systems are utilized to represent nonlinear systems. Instead of the Lyapunov-Krasovskii functional, the Lyapunov-Razumikhin function is adopted to deal with time delays because it involves invariant sets in the original state space of the system. A sequence of explicit control laws corresponding to a sequence of constraint sets are computed offline so that the online computational burden associated with the classical model predictive control algorithms is significantly reduced. In particular, the set invariance theory behind the Razumikhin approach, which is more complicated than the one for nondelayed systems, is directly observed. Additionally, it is proved that all (delayed) states can enter the terminal set in finite time. Moreover, robust positive invariance and input-to-state stability for time-delay systems concerning disturbances are realized. Additionally, an online optimization algorithm is also provided based on the offline computed ellipsoidal sets. Therefore, the conservatism induced by the Razumikhin approach is relaxed, while the computational cost is not significantly increased.

In this paper, model predictive control (MPC) of discrete T-S fuzzy systems subjected to bounded time-varying delay and persistent disturbances is investigated. The Razumikhin approach is adopted for time-delay systems because it involves a Lyapunov function associated with the original nonaugmented state space of system dynamics when compared to the Krasovskii approach. As such, the Razumikhin approach has a good potential to avoid the inherent complexity of the Krasovskii approach especially in the presence of large delays and disturbances. Based on which, both online and offline MPC approaches for systems with time-varying delay are provided. In addition, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are realized. In the offline approach, a sequence of explicit control laws that correspond to a sequence of robust constraints sets are computed offline. And it is proved that system states including all possibly delayed states can be steered to the terminal constraint set in finite time. Moreover, it allows the exact time delay to be unknown in the proposed two approaches. In particular, for systems with time-varying delay, the special positively invariant set theory and finite-time control theory based on the Razumikhin approach are directly revealed via the proposed offline approach.

Robust fuzzy model predictive control of discrete nonlinear systems is investigated in this paper. A recently developed Takagi-Sugeno (T-S) fuzzy approach which uses nonlinear local models is adopted to approximate the nonlinear systems. A critical issue that restricts the practical application of classical model predictive control is the online computational cost. For model predictive control of T-S fuzzy systems, the online computational burden is even worse. Especially for complex systems with severe nonlinearities, parametric uncertainties, and disturbances, existing model predictive control of T-S fuzzy systems usually leads to a very conservative solution or even no solution in some occasions. However, more relaxed results can be achieved by the proposed fuzzy model predictive control approach which adopts T-S systems with nonlinear local models. Another advantage is that online computational cost of the optimization problem through solving matrix inequalities can be significantly reduced at the same time. Simulations on a numerical example and a two-tank system are presented to verify the effectiveness and advantages of the proposed method. Comparisons among several T-S fuzzy approaches are illustrated and show that the best settling time is achieved via the proposed method.

Robust model predictive control of discrete nonlinear systems with bounded time-varying delay and persistent disturbances is investigated in this paper. The T-S fuzzy systems are utilized to represent nonlinear systems. A Razumikhin-type Lyapunov function is adopted for time-delay systems due to its advantage in reducing the complexity especially for systems with large delays and disturbances. The robust positive invariance set theory for systems subjected to time-varying delay and disturbances is analyzed. In addition, the input-to-state stability is realized due to persistent disturbances. The controller synthesis conditions are derived by solving a sequence of matrix inequalities. Simulation on a continuous stirred-tank reactor (CSTR) is illustrated to verify the effectiveness of the proposed method.

In this paper, model reference tracking control of linear brushless DC motor with dead-zone is investigated. Compared with traditional control methods which requires the nonlinear model of dead-zone and then compensation is executed, the dead-zone is modeled as time-varying input delay systems. Thus the complex nonlinearities of dead-zone can be ignored. As a result, the whole motor motion can be regarded as switched systems comprised of two subsystems, one is a linear time-varying input delay system when motor enters the dead-zone, and the other one is a linear non-delayed system when motor leaves the dead-zone. The tracking control problems for both subsystems are considered. In particular,a stabilization method based on Lyapunov-Razumihkin function is adopted for system with time-varying delay, which leads to easier implementations of controller synthesis and computation when compared to Lyapunov-Krasovskii function. Simulation and experimental results on a linear motor is provided to illustrate the effectiveness of the proposed method.

In this paper, we investigate the problem of stabilization for sampled-data fuzzy systems under variable sampling. A novel Lyapunov-Krasovskii functional (LKF) is defined to capture the characteristic of sampled-data systems, and an improved input delay approach is proposed. By the use of an appropriate enlargement scheme, new stability and stabilization criteria are obtained in terms of linear matrix inequalities (LMIs). Compared with the existing results, the newly obtained ones contain less conservatism. Some illustrative examples are given to show the effectiveness of the proposed method and the significant improvement on the existing results.

This paper studies the problem of stabilization for sampled-data neural-network-based control systems with an optimal guaranteed cost. Unlike previous works, the resulting closed-loop system with variable uncertain sampling cannot simply be regarded as an ordinary continuous-time system with a fast-varying delay in the state. By defining a novel piecewise Lyapunov functional and using a convex combination technique, the characteristic of sampled-data systems is captured. A new delay-dependent stabilization criterion is established in terms of linear matrix inequalities such that the maximal sampling interval and the minimal guaranteed cost control performance can be obtained. It is shown that the newly proposed approach can lead to less conservative and less complex results than the existing ones. Application examples are given to illustrate the effectiveness and the benefits of the proposed method.

This article proposes a unified reset control design method to achieve both track-seeking and track-following performance in hard disk drive servo systems. It involves a discrete Linear Quadratic Regulation (LQR) reset controller and an Hi reset controller. The LQR reset controller is much capable of improving transient response performance which includes fast raise time and little overshoot. The discrete H2reset controller performs a better disturbance rejection performance which is more suitable for track following stage. The unified control makes a soft switch from LQR reset controller to Hi reset controller, which avoids sharp vibrations. Simulation shows that the unified reset controller inherits the merits of both discrete LQR reset controller and discrete H2reset controller, and achieves both swift seeking and accurate following performance.

This paper considers the output feedback control problem for nonlinear discrete-time systems, which are represented by a type of fuzzy systems with local nonlinear models. By using the estimations of the states and nonlinear functions in local models, sufficient conditions for designing observer-based controllers are given for discrete-time nonlinear systems. First, a separation property, i.e., the controller and the observer can be independently designed, is proved for the class of fuzzy systems. Second, a two-step procedure with cone complementarity linearization algorithms is also developed for solving the H( ∞) dynamic output feedback (DOF) control problem. Moreover, for the case where the nonlinear functions in local submodels are measurable, a convex condition for designing H(∞) controllers is given by a new DOF control scheme. In contrast to the existing methods, the new methods can design output feedback controllers with fewer fuzzy rules as well as less computational burden, which is helpful for controller designs and implementations. Lastly, numerical examples are given to illustrate the effectiveness of the proposed methods.

This paper studies the effects of cross-terms in the local polynomial periodograms (LPP) by defining and examining the cross local polynomial periodogram (CLPP) and the time-frequency correlative coefficients (TFCC). Our observations show that the cross-terms of signal components can be ignored if they do not intersect and the window length used in the LPP is sufficient. In addition, the TFCC is useful for the analysis of time-frequency coherency between LFM signals and gives us a parametric measure on the amount of the cross-terms in the LPP.

This paper is concerned with the problem of designing fuzzy controllers for a class of nonlinear dynamic systems. The considered nonlinear systems are described by T-S fuzzy models with nonlinear local models, and the fuzzy models have fewer fuzzy rules than conventional T-S fuzzy models with local linear models. A new fuzzy control scheme with local nonlinear feedbacks is proposed, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). In contrast to the existing methods for fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Moreover, the local nonlinear feedback laws in the new fuzzy controllers are also helpful in achieving good control effects. Numerical examples are given to illustrate the effectiveness of the proposed method.

This paper presents a dynamical control of fuel cell and battery hybrid system to supply a suddenly changing load. Because of the voltage drop when PEM fuel cell current increases, in this hybrid system, the fuel cell is used to supply the rated power to the load, a Lithium battery will supply peak power that the load demands, meanwhile, the fuel cell will charge the battery when the battery voltage is low during the light load. A DC/DC converter is to maintain the overall voltage to constant and serve the load properly. In this system, the main control theory is as follows: (a) when the fuel cell can afford the load, only fuel cell will power the load through DC/DC; (b) when the load goes exceeding fuel cell's power range, the battery will help to power the load automatically; (c) when the load drops again, fuel cell can power the load alone and charge the battery if necessary.

Proton exchange membrane (PEM) fuel cell uninterrupted power supply (UPS) has been getting much attention recently. Fuel cell UPS needs another bridge power to smooth from the utility to the fuel cell because the fuel cell stack takes a certain time to start up. Batteries play the role conventionally. Ultracapacitor has many advantages compared with batteries excepts a problem: ultracapacitor voltage drops sharply while discharging. This paper studied a dynamic discharge structure model for ultracapacitor application in a 3 kW base station UPS. The duration of holding valid voltage by the dynamic discharge structure is more than twice comparing to the static discharge structure. Also the dynamic discharge structure has a better efficiency than the static discharge structure using a DC/DC converter to extend the duration.

Hard disk drives (HDDs) encounter a challenge of large storage capacity demand. A particular problem a HDD faces is that a periodic disturbances caused by different sources. These disturbances which do not change drastically will degrade the performance of voice coil motor actuator based servo system so that significant repeatable run-out is formed in position error signal. This paper shows a method which can cancel most of the components in it. In this method, we suppose to rebuild a signal which can approximately represent the disturbances thus cancel the disturbances by adding the opposite value of this signal. When rebuilding this signal, internal model principle and magnitude/phase adaptive estimation algorithm is used to recreate the disturbance signal. Moreover, a polynomial adaptive fitting method which does not need frequency estimation is proposed to deal with time delay problem which exists in the signal recreation stage. It turns out that the adaptive fitting method is much more effective to deal with multiple frequency disturbances. Simulation results show the cancellation effectiveness for single frequency and multiple frequency disturbances even when the disturbance is time-varying in both frequency and amplitude/phase.

The purpose of this paper is to improve the performance of MEMS parallel plate actuators and RF switches systems. Electrostatic micro actuators are normally driven by static open-loop voltage control schemes. A major problem in this control strategy is that at a distance of two-thirds of the zero-bias capacitive gap, the actuator position becomes unstable and collapses. This phenomenon is known as “snap-through” or “pull-in”. In this paper a new closed-loop feedback control scheme using switching technique will be proposed to solve the problem above to provide the stable and controllable range to full gap and controlling movable plate through whole gap to achieve desired switching on-off time. The other issue to be considered is that the control strategy must be applicable for real time implementation.

This paper analyzed and modeled the major contributors of Track Misregistration (TMR) in HDD Self Servowriting (SSW). Self Servowriting is a process whereby the position and timing burst information are written on the disk surface by using the servo information from previously written tracks as seed tracks for writing the burst information on the remaining tracks. One of the most significant issues in this process is Radial Error Propagation which is built up while successive tracks are written. These written-in disturbances are main source of the Repeatable Runout (RRO) which may greatly degrade the HDD servo performance. In this paper, in order to improve the SSW servo performance with higher density, we designed the servo controller by using loop shaping method which is able to achieve higher bandwidth and better error rejection. To minimize the Radial Error Propagation, we further designed the feed-forward compensator using the identified disturbances models, to reduce the written-in RRO. Simulation results illustrate the capability of the proposed compensators.

This paper investigates a time-dependent phase lead reset control (PLRC) scheme which resets the sub-set of states of the systems at a predefined time. A phase lead reset compensator that has a flat gain characteristic is proposed to be used together with the common feedback control design to enlarge the servo bandwidth by increasing the phase stability margin. We also propose to have a reset time slightly earlier than the instants of input zero-crossing in the usual reset control to solve the impulsive effect in the control signal that leads to control signal saturation. Sinusoidal describing function analysis of the proposed control scheme shows that it can relax the Bode's gain-phase constraint. The application of the proposed PLRC to the hard disk drive (HDD) servo system shows that the performance improvement. Our results also show that the large sharp peaks in the control voltage which may lead to track following controller saturation is also avoided

Narrow-band position error at mid-frequencies around the open-loop crossover frequency can not be effectively reduced using a conventional peak filter, because the attenuation of sensitivity gains has to compromise with the associated decrease of phase margin. This paper presents a general second-order filter that is applicable to reject narrow-band disturbances at any frequency range. The filter zero is designed to minimally degrade the closed-loop system stability and obtain a smooth sensitivity curve around the disturbance frequency. A nonlinear optimization procedure is developed to select the filter parameters such that the statistical position error is minimized. Experimental results of a PZT-actuated head positioning control system on spinstand demonstrate that the add-on filter can further reduce the mid-frequency PES NRRO by 8% and preserve the stability margin of the original control system

Repositioning is required in mechanical systems performing repetitive tasks. The effect of poor accuracy of repositioning on tracking performance of iterative learning control has been fully understood. The iterative learning ensures the system output to follow the desired trajectory with a specified error bound, proportional to the bound on initial condition offsets. In this paper, varying-order learning method is proposed to enhance tracking performance by lowering the size of the error bound. A discrete-time initial rectifying action is introduced, by which the system output achieves the desired trajectory jointed smoothly with a transient trajectory from the starting position. Practical schemes are presented based on the varying-order learning and illustrated by numerical results of a single rigid link example.

Improving the position control of the optical pickup head is important step in increasing the storage capacity of an optical disk drive, especially in the presence of internal and external disturbances. To tackle this problem, a disturbance observer is often used to attenuate the disturbances. However, using this method, the robust stability issue cannot be guaranteed. In this paper, a disturbance-observer-based H/sub /spl infin// controller with internal model principle is proposed to achieve the disturbance rejection and to guarantee the robust stability of the system. The proposed control scheme is applied to a DVD-ROM servo system. Simulation studies how the effectiveness of the proposed scheme.

We focus on the design of robust reduced-order dynamic feedback control for a class of nonlinear systems with uncertainties. A robust nonlinear reduced-order dynamic controller (RNRDC) is proposed. The resulting closed-loop system can be robustly stabilized for all admissible uncertainties via the proposed controller. The application of the proposed RNRDC to a permanent magnet brush DC motor driving a single-link manipulator is also considered. The parameters uncertainties can be up to /spl plusmn/80%. The load can vary ranging from 50% to +100% of the nominal value. Our simulation results show that for the example system the proposed RNRDC can achieve good performance.

This paper tackles a FMS jobshop scheduling problem with alternative routings. The objective function is to minimize the weighted flow time of jobs. We use Lagrangian relaxation method to relax the precedence constraints and propose a two-stage approach to solve the decomposed one-machine multi-operation problems. Three examples are tested and the computational results are compared to the Lagrangian relaxation method proposed by Hoitomt (1993). The comparison studies show that with the minimum weighted flow time of jobs as the objective of the scheduling problem the method proposed in this paper yields better solution with less computational time than the existing Lagrangian method.