Performance Of Closed-loop System Research Articles

First Report on Real-World Outcomes with Evoked Compound Action Potential (ECAP)-Controlled Closed-Loop Spinal Cord Stimulation for Treatment of Chronic Pain.

A novel closed-loop spinal cord stimulation (SCS) system has recently been approved for use which records evoked compound action potentials (ECAPs) from the spinal cord and utilizes these recordings to automatically adjust the stimulation strength in real time. It automatically compensates for fluctuations in distance between the epidural leads and the spinal cord by maintaining the neural response (ECAP) at a determined target level. This data collection was principally designed to evaluate the performance of this first closed-loop SCS system in a 'real-world' setting under normal conditions of use in a single European center. In this prospective, single-center observational data collection, 22 patients were recruited at the outpatient pain clinic of the St. Antonius Hospital. All candidates were suffering from chronic pain in the trunk and/or limbs due to PSPS type 2 (persistent spinal pain syndrome). As standard of care, follow-up visits were completed at 3months, 6months, and 12months post-device activation. Patient-reported outcome data (pain intensity, patient satisfaction) and electrophysiological and device data (ECAP amplitude, conduction velocity, current output, pulse width, frequency, usage), and patient interaction with their controller were collected at baseline and during standard of care follow-up visits. Significant decreases in pain intensity for overall back or leg pain scores (verbal numerical rating score=VNRS) were observed between baseline [mean±SEM (standard error of the mean); n=22; 8.4±0.2)], 3months (n=12; 1.9±0.5), 6months (n=16; 2.6±0.5), and 12months (n=20; 2.0±0.5), with 85.0% of the patients being satisfied at 12months. Additionally, no significant differences in average pain relief at 3months and 12months between the real-world data (77.2%; 76.8%) and the AVALON (71.2%; 73.6%) and EVOKE (78.1%; 76.7%) studies were observed. These initial 'real-world' data on ECAP-controlled, closed-loop SCS in a real-world clinical setting appear to be promising, as they provide novel insights of the beneficial effect of ECAP-controlled, closed-loop SCS in a real-world setting. The presented results demonstrate a noteworthy maintenance of pain relief over 12months and corroborate the outcomes observed in the AVALON prospective, multicenter, single-arm study and the EVOKE double-blind, multicenter, randomized controlled trial. The data collection is registered on the International Clinical Trials Registry Platform (Trial NL7889).

Real-World Evidence Analysis of a Hybrid Closed-Loop System.

We analyzed real-world evidence to assess the performance of the mylife CamAPS FX hybrid closed-loop system. Users from 15 countries across different age groups who used the system between May 9, 2022, and December 3, 2022, and who had ≥30 days of continuous glucose monitor data, and ≥30% of closed-loop usage were included in the current analysis (N = 1805). Time in range (3.9-10 mmol/L) was 72.6 ± 11.5% (mean ± SD) for all users and increased by age from 66.9 ± 11.7% for users ≤6 years old to 81.8 ± 8.7% for users ≥65 years. Time spent in hypoglycemia (<3.9 mmol/L) was 2.3% [1.3, 3.6] (median [interquartile range]). Mean glucose and glucose management indicator were 8.4 ± 1.1 mmol/L and 6.9%, respectively. Time using closed-loop was high at 94.7% [90.0, 96.9]. Glycemic outcomes from the present real-world evidence are comparable to results obtained from previous randomized controlled studies and confirm the efficacy of this hybrid closed-loop system in real-world settings.

Predictor-Based Neural Dynamic Surface Control for Strict-Feedback Nonlinear Systems With Unknown Control Gains.

Neural dynamic surface control (NDSC) is an effective technique for the tracking control of nonlinear systems. The objective of this article is to improve closed-loop transient performance and reduce the number of learning parameters for a strict-feedback nonlinear system with unknown control gains. For this purpose, a predictor-based NDSC (PNDSC) approach is presented. It introduces Nussbaum functions and predictors into the traditional NDSC for nonlinear systems with unknown control gains. Unlike NDSC that uses surface errors to update the learning parameters of neural networks (NNs), the PNDSC employs prediction errors for the same purpose, leading to improved transient performance of closed-loop control systems. To reduce the number of learning parameters, the PNDSC is further embedded with the technique of the minimal number of learning parameters (MNLPs). This avoids the problem of the ``explosion of learning parameters'' as the order of the system increases. A Lyapunov-based stability analysis shows that all signals are bounded in the closed-loop systems under PNDSC embedded with MNLPs. Simulations are conducted to demonstrate the effectiveness of the PNDSC approach presented in this article.

Data-Driven Multimodel Predictive Control for Multirate Sampled-Data Nonlinear Systems

This paper is concerned with the asynchronous control problem of multi-rate sampled-data nonlinear systems. To solve this problem, the data-driven multi-model predictive control (DMMPC) strategy is proposed. First, a multi-model predictive control structure is designed such that each state variable of the multi-rate sampled-data nonlinear system can be controlled synchronously at all sampling instants. In this structure, the fuzzy neural network (FNN) is introduced to build the multi-model. Then, the prediction outputs of each state variable at all sampling instants are obtained to provide control information for the controller. Specially, the objective function with adaptive weight matrix (AWM) is designed to reduce the influence of the prediction error caused by nonlinear fitting on control performance. Then, the optimal control laws are calculated to improve the control precision. Finally, the convergence and stability of DMMPC are proved in detail. The numerical example and industrial application reveal that the proposed DMMPC can obtain considerable control performance for the multi-rate sampled-data nonlinear systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The asynchronous control problem of the multi-rate sampled-data nonlinear system (MRSNS) may degrade the operation performance of closed-loop system. In this paper, a data-driven multi-model predictive control (DMMPC) strategy is designed for MRSNS without mechanism model. The strategy mainly consists of three parts: First, a multi-model prediction structure based on fuzzy neural network (FNN) is established to obtain the prediction output of all variables at each sampling point. The parameters of FNNs are corrected at each sampling point to ensure prediction accuracy. Second, an objective function with an adaptive weight matrix (AWM) is designed to compute the control law, in which AWM is used to reduce the influence of prediction error on control performance. Third, the effectiveness of DMMPC is verified by a numerical example and an industrial application of the wastewater treatment process. The experimental results show that DMMPC can achieve satisfied operation performance in control accuracy.

An Incremental Broad-Learning-System-Based Approach for Tremor Attenuation for Robot Tele-Operation.

The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiological tremor has been an important yet challenging research topic in the tele-operation robot field. Some scholars propose using deep learning algorithms to solve this problem, but a large number of hyperparameters lead to a slow training speed. Later, the support-vector-machine-based methods have been applied to solve the problem, thereby effectively canceling tremors. However, these methods may lose the prediction accuracy, because learning energy cannot be accurately assigned. Therefore, in this paper, we propose a broad-learning-system-based tremor filter, which integrates a series of incremental learning algorithms to achieve fast remodeling and reach the desired performance. Note that the broad-learning-system-based filter has a fast learning rate while ensuring the accuracy due to its simple and novel network structure. Unlike other algorithms, it uses incremental learning algorithms to constantly update network parameters during training, and it stops learning when the error converges to zero. By focusing on the control performance of the slave robot, a sliding mode control approach has been used to improve the performance of closed-loop systems. In simulation experiments, the results demonstrated the feasibility of our proposed method.

Convex Optimization-Based Adaptive Fuzzy Control for Uncertain Nonlinear Systems With Input Saturation Using Command Filtered Backstepping

This paper presents a modified command filter backstepping tracking control strategy for a class of uncertain nonlinear systems with input saturation based on the convex optimization method and the adaptive fuzzy logic system control technique. Firstly, the effect of complex uncertainties is eliminated by introducing n command filters and a single fuzzy logic system (FLS). And then, the update laws of FLS weights are designed based on the convex optimization technique. Next, a new piece-wise continuous function is employed to deal with the input saturation problem. The closed-loop system performance is also analyzed using the Lyapunov stability theorem and the Lasalle invariant principle. Finally, simulation and experimental results are presented to show the effectiveness of our controller.

Photonics Large-Survey Telescope Internal Motion Metrology System

Large survey telescopes are vital for mapping dark energy and dark matter in the deep universe. This study presents a fiber-linked internal motion metrology system that aligns the mirrors and large lenses in the telescopes to enhance alignment accuracy by improving the image quality at a lower weight, volume, power, and cost. The internal motion system comprises a photonic laser beam projector capable of projecting multiple Gaussian beams onto the detector of the telescope. The specific spatial frequency aberration component is determined by combining Gaussian beam location and the geometry model of the telescope. Furthermore, integrating the proposed system with the curvature-sensing wavefront system enables more precise alignment and camera sensing. In the experimental tests, the location precision was within 10 μm, and the rotation precision improved to 5 arcsecs, fulfilling the alignment and motion monitoring requirements of large survey telescopes. The results of this study can be used as a reference to improve the performance of closed-loop bandwidth systems and active camera optics.

Comparative thermo-economic analysis of co-axial closed-loop geothermal systems using CO2 and water as working fluids

Co-axial closed-loop geothermal heating systems coupling abandoned oil/gas wells face more grievous corrosion issues than the shallow ground source heat pumps due to the higher temperature and pressure conditions. Instead of water, using CO2 as a working fluid would extract more power and eliminate the corrosion issue; however, these advantages might be offset by the energy and capital cost of high operating pressure. To better guide the working fluid selection for the co-axial closed-loop geothermal heating systems, the effects of using CO2 as a working fluid on the system thermo-economic performance as well as the effects of geothermal gradients and tube diameters are evaluated. We first established a 2D thermodynamic model to simulate the heat extraction from a vertical co-axial closed loop geothermal well and the heat conduction in host rocks and then applied the key parameters into an economic model for the calculation of thermo-economic indicators. It is found that the wellbore set with smaller tube diameters can profit the thermo-economic performance of the co-axial closed-loop geothermal heating system by substantially lowering the total annual cost, promoting the energy efficiency, and slowing down the growth of cost and the descent of energy efficiency within the lifetime of the system. Besides, CO2 will be a better working fluid for small-scale co-axial closed-loop geothermal heating systems leading to lower cost per net power compared to water, and it also can slow down the descent of energy efficiency year by year.

Anti-unwinding adaptive super-twisting attitude control for spacecraft

This article addresses the attitude maneuver control without unwinding based on variable gain(i.e. adaptive) super-twisting algorithm and the modified Rodrigues parameters(MRPs). First, a selection logic for the MRP set and its shadow set is designed in order to construct an attitude control system model for rigid spacecraft. Second, a nonlinear sliding function is presented, and on the sliding phase, finite-time convergence and anti-unwinding performance are guaranteed. As a result, a first order sliding mode controller is created to ensure finite-time convergence and anti-unwinding performance on the reaching phase. To alleviate the chattering and improve convergence speed, an adaptive super-twisting algorithm is presented, and the finite-time convergence and anti-unwinding performance are guaranteed on the reaching phase. Furthermore, the adaptive gains are designed based on dual-layer adaptive law, which allows the gains to be increased and decreased as appropriate. It is further demonstrated that when the controller gains are time-varying according to adaptive law, the convergence property and anti-unwinding performance are guaranteed. Numerical simulations are carried out to illustrate the closed-loop system's performance under the proposed control scheme.

Predictive control for networked high-order fully actuated systems subject to communication delays and external disturbances

This research is concerned with an output tracking problem for networked high-order fully actuated (NHOFA) systems subject to communication delays and external disturbances, where communication delays occur in sensor to network node and network node to actuator. A HOFA system model, as a novel system representation, is applied to establish the dynamics of networked control systems (NCSs). Accordingly, a disturbance observer based HOFA predictive control approach is proposed to address this problem. In the proposed approach, a disturbance observer is utilized to cope with the external disturbances, and then a local HOFA feedback with disturbance compensation is designed to adjust the closed-loop system performances. Further, a Diophantine Equation is applied to establish an incremental HOFA (IHOFA) prediction model to substitute a reduced-order prediction model, such that multi-step ahead predictions are derived to minimize a cost function involving the optimization of tracking performance and the compensation of network-induced communication delays. A necessary and sufficient criterion is given to discuss the stability and tracking performance of closed-loop NHOFA systems, it is simple to use in system analysis and extend in practice. The availability of the proposed approach is demonstrated via simulated and experimental results for tracking control of air-bearing spacecraft (ABS) simulator.

Robust Control of an Electromagnetic Suspension System

The electromagnetic suspension system is a motivating and inspiring project for students to investigate the primary principles of electrical engineering, such as control theory, including robust performance and modeling uncertainty. This paper studies robust control methods for an electromagnetic suspension system with H_2, H_infinity and mu-synthesis control approaches to provide robust system performance in the presence of uncertainties and disturbances. System uncertainties are critical parts of the interconnected system modeling, affecting its performance. The control methods tackle the system uncertainties, including unmodeled dynamics, parametric uncertainty, and linearization error. Robust performance analysis for all proposed control methods is provided, and closed-loop system performance is illustrated. The performance robustness is validated using illustrative simulation results.

One-year real-world performance of the DBLG1 closed-loop system: Data from 3706 adult users with type 1 diabetes in Germany.

The Diabeloop Generation 1 (DBLG1) system is an interoperable hybrid closed-loop solution that was commercialized in Germany in March 2021. We report the longitudinal glycaemic outcomes among the first 3706 users in a real-world setting. We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Germany with the DBLG1 system before 30 April 2022, and with a minimum 14 days of closed-loop usage. In total, 3706 users (41% women, age 45.1 ± 14.5 years) met the inclusion criteria, reaching a mean follow-up of 131.0 ± 85.1 days, an overall 485 600 days of continuous glucose monitoring data, and a median time spent in closed-loop of 95.0% (IQR 89.1-97.4). The median percentage time in range (70-180 mg/dl) was 72.1% (IQR 65.0-78.9); the time below 70 mg/dl was 0.9% (0.5-1.7), the time below 54 mg/dl was 0.1% (0.1-0.3), and the median Glucose Management Index was 7.0% (6.8-7.3). Exploratory analysis of a subset of 2460 patients in whom baseline glycated haemoglobin (HbA1c) was available [7.4% (IQR 6.9-8.0)] showed that the achieved mean time in range was influenced by baseline HbA1c, ranging from 65.8 ± 9.9% (A1c ≥8.5%) to 81.3 ± 6.8% (A1c <6.5%). This large real-world analysis confirms the relevance of the DBLG1 automated insulin delivery solution for the achievement of standards of care in adult patients with type 1 diabetes.

Novel anti-disturbance fast terminal sliding mode control with improved quick reaching law for DC-DC buck converter

In order to improve the performance and suppress the chattering problem of variable structure control system, a nonlinear novel quick reaching law with dynamic power term is suggested, which could enhance the convergence rate whenever the system state position is near to or far from the manifold. Furthermore, a novel fast terminal sliding surface with additional nonlinear terms is developed and designed. The combination of these structure propositions can minimize both the reaching time and sliding time convergence. The finite-time stability of the proposed control paradigm is demonstrated via the Lyapunov criteria method. Moreover, in order to see the superior performance of the compound control, a disturbed DC-DC buck converter is selected as plant in this work. In addition, the extended state observer is designed and added in feedforward path of control law of sliding mode structure. In order to estimate the unknown uncertainties, the results demonstrate that the nonlinear novel fast terminal sliding mode with variable quick reaching law brings the system at equilibrium point more rapidly than the traditional manifolds and reaching laws. More than that, the closed-loop system performance has better transient response, lower steady state error and lesser sensitivity to disturbances.

Performance Improvement of an NMP Mini Segway Using Sample and Hold Inputs

Early analytical work showed that it is possible to discretize a continuous-time non-minimum phase (NMP) system using special sample and hold inputs (SHIs) and obtain a discrete-time minimum phase (MP) system with a higher sampling rate than that of conventional zero-order sample and hold. As a result, high-gain discrete-time control law can be used to further improve closed-loop system performance since the resulting discrete-time system is MP. In this paper, this technique is adopted for performance improvement on a mini-Segway (MS), a developed robot equipped with an extremely low-cost microcontroller (Arduino). A dual-loop control tuning method was developed to optimize the overall closed-loop system performance. The system performance improvement is demonstrated by the reduction of the mini-Segway oscillation magnitude under idle conditions. The experimental results show that the low-cost microcontroller can be used for the dual-loop SHI control scheme, and the MS cart displacement oscillation magnitude is significantly reduced by more than 65% over the baseline controller. The main contribution of this paper is implementing the proposed sample and hold input (SHI) scheme into an extremely low-cost micro-controller (Arduino) and demonstrating that low-cost microcontrollers can effectively utilize the proposed SHI.

An improved model-free adaptive integral sliding mode control for a class of nonlinear discrete-time systems

For a class of uncertain single-input single-output (SISO) nonlinear discrete-time systems, an improved model-free adaptive integral sliding mode control (MFA-ISMC) algorithm is proposed based on a disturbance observer. The pseudo-partial derivative (PPD) estimation algorithm used to describe the system dynamics is modified and the stability of the closed-loop system is guaranteed. It is shown that the corresponding closed-loop system performance is improved and the effectiveness of the proposed method is demonstrated by a simulation example.

Resilient Load Frequency Control of Multi-Area Power Systems Under DoS Attacks

Cyber security of modern power systems has become increasingly significant due to their open communication architecture and expanding network connectivity exposed to malicious cyber attacks. Resilient control represents an effective means to preserve the survivability of the power system under cyber attacks. In this paper, we address the resilient load frequency control (LFC) design for multi-area power systems under a new class of time-constrained denial-of-service (DoS) attacks. First, different from the widely-explored duration- and frequency-constrained DoS attack models, we consider a general time-constrained DoS attack model where only the attack durations are confined into some bounds, which represents less a priori knowledge of an attacker’s actions. Second, instead of using a traditional yet conservative time-invariant Lyapunov function (TILF), we develop an attack-parameter-dependent time-varying Lyapunov function (TVLF) approach to enable a resilient LFC design without jeopardizing the desired closed-loop system stability and performance. Furthermore, we provide a formal stability and performance analysis condition as well as a design criterion for the desired DoS-resilient output feedback LFC controller. We also show that the minimum allowable sleeping period and the maximum allowable active period of the attacked LFC system can be explicitly disclosed. Finally, we present two simulation case studies on a two-area LFC system and a three-area LFC system to demonstrate the effectiveness of the obtained results.

A Non-Cooperative Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Interconnected Systems

This paper deals with a new non-cooperative distributed controller for linear large-scale systems based on designing multiple local Model Predictive Control (MPC) algorithms using Laguerre functions to enhance the global performance of the overall closed-loop system. In this distributed control scheme, that does not require a coordinator, local MPC algorithms might transmit and receive information from other sub-controllers by means of the communication network to perform their control decisions independently on each other. Thanks to the exchanged information, the sub-controllers have in this way the ability to work together in a collaborative manner towards achieving a good overall system performance. To decrease drastically the computational load in the small-size optimization problem with a short prediction horizon, discrete-time Laguerre functions are used to tightly approximate the optimal control sequence. For evaluating the proposed distribution control framework, a simulation example is proposed to show the effectiveness of the proposed scheme and its applicability for large-scale interconnected systems. The obtained simulation results are provided to demonstrate clearly that the proposed Non-Cooperative Distributed MPC (NC-DMPC) outperforms Decentralized MPC (De-MPC) and achieves performance comparable to centralized MPC with a reduced computing time. The system performance of the proposed distributed model predictive control is given.

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

In this paper, the problem of trajectory tracking for robotic manipulator with input deadzone and time-varying disturbances is investigated using a continuous non-singular terminal sliding mode control (SMC). The robotic manipulator plays a vital role in industrial as well as many other applications. However, it faces numerous problems for accurate and fast trajectory tracking response in the presence of unknown uncertainties and external disturbances, that always have adverse affects on system closed-loop performance. This paper proposes a valid control design scheme to address the aforementioned practical issue. First, a non-singular terminal sliding mode surface is designed. Then, a robust control law is developed to guarantee the finite-time converges of system states to the origin. In addition, the sign function is substituted by saturation function, which can effectively reduce the chattering driven by the high-frequency switching item in the traditional SMC method. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The efficiency of the designed control scheme is validated with numerical simulations.

Communication Times Reconstruction in a Telecontrolled Client–Server Scheme: An Approach by Kalman Filter Applied to a Proprietary Real-Time Operating System and TCP/IP Protocol

Nowadays, various systems were developed in the telecommunications field which make use of technologies for the transmission and reception of information. One of these technologies is the Internet, which was developed in tandem with scientific growth. Therefore, its application in the control of various industrial processes has a notable influence. In this context, there are industrial processes that, due to the potential danger they represent to human beings, must be controlled by means of a remote control system. Such systems can be implemented through client–server communication schemes, which form a network of computers to exchange information. In the exchange of information, delay times are generated. These inactivity times have a close relationship with the latency in the communication network and have a negative impact on the performance of closed-loop control systems. In this sense, for physical implementation, it is essential to measure and mathematically characterize their magnitudes in order to know their variability and thus be able to design control strategies that compensate for their effects. Hence, this research paper presents the reconstruction of the communication times measured from a telecontrol system, where it is assumed that only one subsystem acts as the controller and the other one acts as the controlled. In other words, this paper addresses a control scheme type of single-input-single-output system (SISO). This reconstruction is based on the Kalman filter, which estimates the communication times that are measured on an experimental test bench with a client–server communication scheme. Communication times are characterized as stochastic processes. So, in order to validate the reconstruction presented, the level of dependence between the random processes is evaluated by analyzing their moments of probability as well as their covariance moments. Finally, an analysis based on the mean square error is presented, through which it can be concluded that the reconstruction technique used allows one to know the dynamics of the communication times generated by the remote control process presented in this research.

Nonlinear Model Predictive Control with aggregated constraints

This paper presents a constraint aggregation approach for Nonlinear Model Predictive Control (NMPC). Constraint aggregation functions provide an approximation of the feasible region with a reduced number of nonlinear constraints. The effect of the aggregation on the closed-loop system performance and stability is studied using tools from sensitivity analysis. Numerical results for the control of a 20th order flexible aircraft model show that significant computational savings can be achieved. The proposed method can facilitate the implementation of NMPC solutions for large-scale systems.