Differential Feedback Control Research Articles

A Novel Laser Lithotripsy System with Automatic Real-Time Urinary Stone Recognition: Computer Controlled Ex Vivo Lithotripsy is Feasible and Reproducible in Endoscopic Stone Fragmentation.

Urinary stone treatment has been strongly influenced by advances in technology. Nevertheless, the photonic characteristics of stones as the treatment target have been neglected. Monitoring fluorescence spectra is sufficient for automatic target differentiation and laser feedback control as previously described. We investigated the characteristics of fluorescence signals and the clinical practicability of real-time laser feedback control during lithotripsy. Fluorescence excitation light was superimposed on a holmium laser beam into the treatment fiber. Spectra were recorded and signal amplitude changes were analyzed during increases in distance between the fiber tip and the stone to identify the optimal threshold level for stone recognition. Ho:YAG lithotripsy was performed under in vitro surgical conditions in porcine tissue while our feedback system autonomously controlled the laser impulse release during lithotripsy. The tissue was then endoscopically and macroscopically examined for laser induced lesions. Mean ± SD autofluorescence signal amplitudes from urinary stone samples varied between 142 ± 29 and 1,521 ± 152 ADU while tissue and endoscope coating emission was negligible. Signal amplitude decreased rapidly at distances larger than 1 to 2 mm. Clinically reliable threshold values for target recognition could be set to prevent laser pulse emission if the stone was out of range or urothelial tissue might be harmed by laser irradiation. We observed no incorrectly released laser pulse or injury to tissue during autonomously controlled holmium laser lithotripsy. Our laboratory study strengthens the evidence that tracking real-time autofluorescence spectra during endoscopic stone surgery via automatic feedback control of the laser impulse release may become a potentially useful clinical tool for surgeons who navigate in the upper urinary tract.

Nonlinear delayed feedback control of synchronization in an excitatory–inhibitory coupled neuronal network

We explore nonlinear time-delayed feedback as a technique for the control of synchronization in excitatory–inhibitory coupled neuronal networks. The feedback signal can be expressed in two forms: differential feedback control and direct feedback control, to suppress bursting synchronization. In the two-dimensional plane of feedback strength and time delay, domains of effective synchronization suppression are described. For these two different types of nonlinear feedback, numerical simulations yield completely different results: The differential feedback is noninvasive, but it has a fragile robust performance. However, the direct feedback is robust against stimulation parameter variations, but it is invasive. Furthermore, electromagnetic induction is introduced by calculating the magnetic flux on membrane, and how it affects nonlinear time-delayed feedback control to suppress bursting synchronization is explored. We show that desynchronization effect of the differential feedback can be enhanced by appropriate electromagnetic induction. And desynchronization effect of the direct feedback is maintained by a small electromagnetic induction intensity, and no longer works once it exceeds a critical value. These presented results are expected to facilitate us to control abnormal brain synchrony, and promote the applications of the nonlinear feedback control scheme in practical clinical treatment.

Suppressing bursting synchronization in a modular neuronal network with synaptic plasticity.

Excessive synchronization of neurons in cerebral cortex is believed to play a crucial role in the emergence of neuropsychological disorders such as Parkinson's disease, epilepsy and essential tremor. This study, by constructing a modular neuronal network with modified Oja's learning rule, explores how to eliminate the pathological synchronized rhythm of interacted busting neurons numerically. When all neurons in the modular neuronal network are strongly synchronous within a specific range of coupling strength, the result reveals that synaptic plasticity with large learning rate can suppress bursting synchronization effectively. For the relative small learning rate not capable of suppressing synchronization, the technique of nonlinear delayed feedback control including differential feedback control and direct feedback control is further proposed to reduce the synchronized bursting state of coupled neurons. It is demonstrated that the two kinds of nonlinear feedback control can eliminate bursting synchronization significantly when the control parameters of feedback strength and feedback delay are appropriately tuned. For the former control technique, the control domain of effective synchronization suppression is similar to a semi-elliptical domain in the simulated parameter space of feedback strength and feedback delay, while for the latter one, the effective control domain is similar to a fan-shaped domain in the simulated parameter space.

Experimental evaluation of human renal kidney stone spectra for intraoperative stone-tissue-instrument analysis using autofluorescence

The precision and safety of laser lithotripsy in the upper urinary tract is solely controlled by the surgeon. To our knowledge laser systems with integrated real-time analysis of target tissues are not available. An intelligent laser system with automated target differentiation and laser feedback control would provide tremendous improvement in patient safety and surgical precision. We evaluated the technical, medical and physical conditions for real-time analysis using fluorescence for future development of a new laser for lithotripsy. We collected data on the fluorescence spectra of 82 native human calculi covering the 8 most relevant subtypes compared to the spectra of endoscope components, the organic porcine urinary tract and pure samples of urinary stone compositions. Data analysis was performed to determine differences in sample signal intensities and emission wavelengths. All native stones showed a significant fluorescence signal compared to porcine urinary tract tissue or endoscope components. The amplitude of the fluorescence signal varied by a factor of 75. The weakest signal of stone material was 3.6-fold larger than the strongest signal of pig kidney tissue (mean ± SD 0.038 ± 0.043 vs 0.00058 ± 0.00058 arbitrary units). No fluorescence signal was observed for endoscope components. Fluorescence amplitude and spectral curve form were found to be unrelated to stone type. Our study provides essential information on the spectral differentiation of tissue, urinary stones and relevant endoscope components. The measurements indicate that differentiation by fluorescence is possible for all relevant stone types.

Agile Coordination and Assistive Collision Avoidance for Quadrotor Swarms Using Virtual Structures

This paper presents a method for controlling a swarm of quadrotors to perform agile interleaved maneuvers while holding a fixed relative formation, or transitioning between different formations. The method prevents collisions within the swarm, as well as between the quadrotors and static obstacles in the environment. The method is built upon the existing notion of a virtual structure, which serves as a framework with which to plan and execute complex interleaved trajectories, and also gives a simple, intuitive interface for a single human operator to control an arbitrarily large aerial swarm in real time. The virtual structure concept is integrated with differential flatness-based feedback control to give an end-to-end integrated swarm teleoperation system. Collision avoidance is achieved by using multiple layered potential fields. Our method is demonstrated in hardware experiments with groups of 3–5 quadrotors teleoperated by a single human operator, and simulations of 200 quadrotors teleoperated by a single human operator.

Singular-Perturbation-Based Nonlinear Hybrid Control of Redundant Parallel Robot

High-speed robots are being applied more and more widely in the scenarios of aviation manufacturing, electronic packaging, and three-dimensional printing, etc. To achieve high speed, the robots are typically designed to be lightweight, hence suffer from undesired vibration, so that the dynamic performance would deteriorate. In light of this, this paper considers the dynamic modeling and active control of an emerging redundant parallel robot. First, the recursively dynamic model is modularly established by means of Lagrangian multipliers method. For completeness, the model of permanent magnet synchronous motor is further integrated with the model of mechanical system to formulate the electromechanically coupled dynamic model (ECDM). To facilitate dynamic control, the ECDM is cleverly projected into the null space of constraint Jacobian matrix eliminating the need to compute Lagrangian multipliers explicitly. Based on singular perturbation approach and Tikhonov theorem, the ECDM is decomposed into two subsystems in different timescales. On this basis, a nonlinear hybrid control scheme is proposed for both trajectory tracking and vibration suppression. Numerical simulations suggest that the nonlinear hybrid control exhibits overwhelming performance than the conventional joint-based proportional and differential feedback control.

Pinning synchronization of fractional-order complex networks by a single controller

We investigate the state feedback pinning synchronization of fractional-order complex networks. Based on the stability theory of fractional-order differential systems and state feedback control by a single controller, synchronization conditions for fractional-order complex networks are given. We assume that the coupling matrix is irreducible, and provide a numerical example to illustrate the validity of the proposed conclusions.

Trajectory and Vibration Control of a Single-Link Flexible-Joint Manipulator Using a Distributed Higher-Order Differential Feedback Controller

The trajectory tracking in the flexible-joint manipulator (FJM) system becomes complicated since the flexibility of the joint of the FJM superimposes vibrations and nonminimum phase characteristics. In this paper, a distributed higher-order differential feedback controller (DHODFC) using the link and joint position measurement was developed to reduce joint vibration in step input response and to improve tracking behavior in reference trajectory tracking control. In contrast to the classical higher-order differential (HOD), the dynamics of the joint and link are considered separately in DHODFC. In order to validate the performance of the DHODFC, step input, trajectory tracking, and disturbance rejection experiments are conducted. In order to illustrate the differences between classical HOD and DHODFC, the performance of these controllers is compared based on tracking errors and energy of control signal in the tracking experiments and fundamental dynamic characteristics in the step response experiments. DHODFC produces better tracking errors with almost same control effort in the reference tracking experiments and a faster settling time, less or no overshoot, and higher robustness in the step input experiments. Dynamic behavior of DHODFC is examined in continuous and discontinues inputs. The experimental results showed that the DHODFC is successful in the elimination of the nonminimum phase dynamics, reducing overshoots in the tracking of such discontinuous input trajectories as step and square waveforms and the rapid damping of joint vibrations.

PINNING SYNCHRONIZATION OF FRACTIONAL-ORDER COMPLEX NETWORKS BY A SINGLE CONTROLLER

We investigate the state feedback pinning synchronization of fractional-order complex networks. Based on the stability theory of fractional-order differential systems and state feedback control by a single controller, synchronization conditions for fractional-order complex networks are given. We assume that the coupling matrix is irreducible, and provide a numerical example to illustrate the validity of the proposed conclusions.

Distributed plantwide control based on differential dissipativity

SummaryModern chemical plants are becoming very complex, often consisting of a number of nonlinear process units (subsystems) with strong interactions due to material recycle and energy integration. The operation setpoint may need to be adjusted from time to time based on the market demand. To address the aforementioned challenges, a plantwide distributed nonlinear control scheme based on differential dissipativity is proposed in this paper, which can ensure plantwide incremental exponential stability and achieve bounded incremental L2 gain performance. As a non‐unique property, the differential dissipativity of individual subsystem is shaped by a setpoint‐independent control structure – differential state feedback control. The dissipativity properties of subsystems and individual controllers are determined simultaneously as a large‐scale feasibility problem to ensure the plantwide stability and performance. It is converted into an LMI condition for plantwide supply rate planning and small‐scale sum‐of‐squares programming problems for individual subsystem dissipativity shaping, by using the alternating direction method of multipliers method. The proposed approach is illustrated using a chemical reactor network with a recycle stream. Copyright © 2016 John Wiley & Sons, Ltd.

Finite-time Mittag-Leffler synchronization of fractional-order memristive BAM neural networks with time delays

This paper investigates the finite-time synchronization of delayed fractional-order memristive bidirectional associative memory neural networks (FMBAMNNs). Based on Lyapunov theory, fractional-order differential inequalities, norm properties and linear feedback controller, the separated criteria are obtained to ensure the finite-time synchronization of studied FMBAMNNs with different fractional order α, respectively. Moreover, the derived criteria are easily checked and they contribute to the reduction of amount of calculation. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results.

Differential Flatness Properties and Feedback Control of the Fokker–Planck PDE

The Fokker–Planck PDE describes the dynamics of several biochemical and biological processes, as well as of phenomena at molecular scale. In this paper, the problem of feedback control of the Fokker–Planck PDE is studied. It is shown that the procedure for numerical solution of Fokker–Planck PDE results into a set of nonlinear ordinary differential equations (ODEs) and an associated state equations model. For the local subsystems, into which a Fokker–Planck PDE is decomposed, it becomes possible to apply boundary-based feedback control. The controller design proceeds by showing that the state-space model of the Fokker–Planck PDE stands for a differentially flat system. Next, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem’s dynamics and can eliminate the subsystem’s tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the Fokker–Planck PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the Fokker–Planck PDE system so as to assure that all its state variables will converge to the desirable setpoints.

How time-delayed coupling influences differential feedback control of bursting synchronization in modular neuronal network

The nontrivial influence of time-delayed coupling on differential feedback control of bursting synchronization is explored in modular neuronal network. Numerical simulations show that collective rhythm of bursting synchronization, irrespective of the numbers of subnetworks, can appear when the considered modular network is forced by large coupling. Then, a control domain of effective synchronization suppression is detected when the proposed differential feedback control is applied to suppress bursting synchronization. Furthermore, time delay in the coupling is introduced, and how it affects differential feedback control to inhibit bursting synchronization is explored. The result declares that small time delay can enhance differential feedback control to suppress bursting synchronization, and there exists an optimal time delay such that this enhancement can obtain its maximum. This presented result is expected to facilitate us to suppress bursting synchronization in realistic neuronal networks.

Asymptotic Tracking of Periodic Operation Based on Control Contraction Metrics

Periodic operations can be beneficial to chemical and biological systems, leading to better efficiency and plant economy compared to steady state operation. In this paper, the tracking control of desired periodic operation based on control contraction metrics is considered. A relaxation method is proposed to convert the nonconvex control synthesis problem into a convex sum-of-squares programming. Finally, an application to a Lotka-Volterra system of sustained oscillating chemical reactions is presented for illustration. The main advantages of this approach include: (1) the feedback information is geodesics, which is a general deviation considering local nonlinearity; (2) the differential state feedback control law is reference trajectory independent; (3) the control design can be carried out via numerical optimization.

Nonlinear Differential Equations and Feedback Control Design for the Urban–Rural Resident Pension Insurance in China

Facing many problems of the urban–rural resident pension insurance system in China, one should firstly make sure that this system can be optimized. This paper, based on the modern control theory, sets up differential equations as models to describe the urban–rural resident pension insurance system, and discusses the globally asymptotic stability in the sense of Liapunov for the urban–rural resident pension insurance system in the new equilibrium point. This research sets the stage for our further discussion, and it is theoretically important and convenient for optimizing the urban–rural resident pension insurance system.

Dynamic MIPS Rate Stabilization for Complex Processors

Modern microprocessor cores reach their high performance levels with the help of high clock rates, parallel and speculative execution of a large number of instructions, and vast cache hierarchies. Modern cores also have adaptive features to regulate power and temperature and avoid thermal emergencies. All of these features contribute to highly unpredictable execution times. In this article, we demonstrate that the execution time of in-order (IO), out-of-order (OoO), and OoO simultaneous multithreaded processors can be stable and predictable by stabilizing their mega instructions executed per second (MIPS) rate via a proportional, integral, and differential (PID) gain feedback controller and dynamic voltage and frequency scaling (DVFS). Processor cores in idle cycles are continuously consuming power, which is highly undesirable in systems, especially in real-time systems. In addition to meeting deadlines in real-time systems, our MIPS rate stabilization framework can be applied on top of it to reduce power and energy by avoiding idle cycles. If processors are equipped with MIPS rate stabilization, the execution time can be predicted. Because the MIPS rate remains steady, a stabilized processor meets deadlines on time in real-time systems or in systems with quality-of-service execution latency requirements at the lowest possible frequency. To demonstrate and evaluate this capability, we have selected a subset of the MiBench benchmarks with the widest execution rate variations. We stabilize their MIPS rate on a 1GHz Pentium III--like OoO single-thread microarchitecture, a 1.32GHz StrongARM-like IO microarchitecture, and the 1GHz OoO processor augmented with two-way and four-way simultaneous multithreading. Both IO and OoO cores can take advantage of the stabilization framework, but the energy per instruction of the stabilized OoO core is less because it runs at a lower frequency to meet the same deadlines. The MIPS rate stabilization of complex processors using a PID feedback control loop is a general technique applicable to environments in which lower power or energy coupled with steady, predictable performance are desirable, although we target more specifically real-time systems in this article.

A Control Method to Suppress Resonance and Improve Current Quality for Inverter System with LCL Filter

The LCL filter is easy to resonate, especially in the new energy power system with multiple inverters. The LCL filter resonant formulas are derived in this paper and a novel double closed loop control strategy combing with the structure of the LCL filter was proposed at the base on feedback of capacitor current as a result. In the control system, the inner loop feedback the first differential voltage of filter inductor at the grid side to damp the resonance and nature of first differential voltage of filter inductor is explained; the grid current outer loop achieves direct control of the grid current for high power factor and utilization efficiency. The simulation results show that the grid-side inductor voltage differential feedback control have a better current control effect both in the condition of the normal power grid voltage and distorted grid voltage, and the fluctuations of power and dc voltage are reduced.

Bio-inspired neurodynamics-based cascade tracking control for automated guided vehicles

Nowadays, the automated guided vehicles (AGV) have been widely used with increasing missions in a variety of fields such as the industry, military, and research. Due to the nonholonomic constraints, fulfilling satisfactory control of the AGV becomes a big challenge. In the present work, a bio-inspired neurodynamics-based cascade tracking control strategy was proposed. Specifically, the bio-inspired neurodynamics module was utilized to generate smooth forward velocities for overcoming the sharp velocity jump. Moreover, with the cascade tracking approach, the nonholonomic system was transformed into a chained system. Additionally, a state differential feedback controller was applied to improve the tracking accuracy. Finally, simulation investigations based on Matlab codes with various parameter settings were carried out to verify the effectiveness of the proposed strategy. The simulation results showed that the proposed strategy in the present work is able to produce accurate, smooth, robust, and globally stable control for the AGV.

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.

Hyper-chaos encryption using convolutional masking and model free unmasking

In this paper, during the masking process the encrypted message is convolved and embedded into a Qi hyper-chaotic system characterizing a high disorder degree. The masking scheme was tested using both Qi hyper-chaos and Lorenz chaos and indicated that Qi hyper-chaos based masking can resist attacks of the filtering and power spectrum analysis, while the Lorenz based scheme fails for high amplitude data. To unmask the message at the receiving end, two methods are proposed. In the first method, a model-free synchronizer, i.e. a multivariable higher-order differential feedback controller between the transmitter and receiver is employed to de-convolve the message embedded in the receiving signal. In the second method, no synchronization is required since the message is de-convolved using the information of the estimated derivative.