Controller Output Signal Research Articles

Dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for nonlinear multi-agent systems

This paper is concerned with the problem of dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for multi-agent systems subject to actuator saturation, in which state variables are unmeasurable and nonlinear functions are totally unknown. A fuzzy state observer is constructed to estimate unmeasurable states. Meanwhile, fuzzy logic systems are used to approximate unknown nonlinear functions. To effectively save the usage of communication resources, this paper designs both sensor output signal and control signal triggering mechanisms, respectively. Unfortunately, the output triggering can lead to a problem that virtual control laws are non-differentiable. To solve this problem, we first utilize observer output signals to construct virtual control laws to ensure the first-order differentiation of virtual control laws. Then, a dynamic filtering technology is introduced to avoid the repeated differentiation of virtual control laws. Furthermore, an improved first-order auxiliary system is designed to compensate for the impact of actuator saturation. It is shown that the designed controller can guarantee tracking errors steer to a preset accuracy within a prescribed settling time, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results verify the effectiveness of the developed control scheme.

A Novel Linear-Based Closed-Loop Control and Analysis of Solid-State Transformer

In this paper, a new linear-based closed-loop control method for a Solid-State Transformer (SST) has been proposed. In this new control method, individual current and voltage loops for each of the power conversion stages (AC-DC, DC-DC, DC-AC) are implemented. The feedback between the input and output control signals for each loop is achieved through the voltage on the DC link capacitors and the current transferred between the converters. This enables the SST to be controlled easily in a linear-based closed-loop manner without the need for complex computations. In order to evaluate the performance analysis of the proposed control system, a simulation of an SST with approximately 10 kVA apparent power was performed. Based on the obtained simulation results, the response time of the proposed control method for dynamic load variations was proved to be in the range of 40 milliseconds, and it has been observed that this method allows electrical power to be transferred from the load to the grid. The power factor value of SST under inductive load is measured to be approximately 99%, and the overall system efficiency is 96% and above, indicating that this proposed new control method has very high performance.

Differential Entropy-Based Fault-Detection Mechanism for Power-Constrained Networked Control Systems.

In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach, we then discuss the finite-time approximation of the power constraints for the relevant control loop signals. The network under study is formed by two additive white Gaussian noise (AWGN) channels located on the direct and feedback paths of the closed control loop. The finite-time approximation of the controller output signal allows us to estimate its differential entropy, which is used in our proposed fault-detection mechanism. After fault detection, we propose a fault-identification mechanism that is capable of correctly discriminating faults. Finally, we discuss the extension of the contributions developed here to future research directions, such as fault recovery and control resilience.

Comprehensive analysis of the magnetic field closed-loop control system in SERF atomic magnetometer

Benefiting from the capability to measure and suppress magnetic field interferences in situ, the closed-loop magnetometer system has garnered significant attention. In this paper, the low-pass filtering link is analyzed theoretically and experimentally for the first time within the framework of the single-beam spin exchange relaxation free atomic magnetometer system (the object to be controlled). The analysis not only elucidates its impact on the closed-loop system but also establishes optimal operating parameters through a comprehensive consideration of factors such as steady-state error, bandwidth, and other metrics. A comparison and analysis of the direct output signal of the system with the controller output signal reveal the necessity of using the controller output as the closed-loop system’s output, supported by the amplitude and frequency response characteristics of the output signal. The paper introduces evaluation indicators essential for comparing the long-term measurement stability of open- and closed-loop systems, and it explores the correlation between the output signals of the open- and closed-loop systems and the cardio-magnetic input signals. Finally, the calibration methods for multi-channel closed-loop magnetometers are discussed.

Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors.

This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefined-time convergence property guarantees that the regulation task can be finished by robotic manipulators in a preset time, in spite of the initial state of manipulators. This feature will facilitate the scheduling of a series of tasks in industrial applications. To this end, a varying-parameter predefined-time convergent zeroing neural dynamics (ZND) model is first proposed and employed to solve the regulation problem. As well as the primary task, a conventional ZND model is utilized to achieve the avoidance of obstacle. The stability of the proposed controller is analyzed based on the Lyapunov stability theory. For the sake of dealing with the unknown kinematic model of robotic manipulators, gradient neural dynamics (GND) models are exploited to adapt the Jacobian matrices just relying on the control signal and sensory output, which enables us to control robotic manipulators in a model-free manner. Finally, the efficacy and merits of the proposed control method are verified by simulations and experiments, including a comparison with the existing method.

Action Mapping: A Reinforcement Learning Method for Constrained-Input Systems.

Existing approaches to constrained-input optimal control problems mainly focus on systems with input saturation, whereas other constraints, such as combined inequality constraints and state-dependent constraints, are seldom discussed. In this article, a reinforcement learning (RL)-based algorithm is developed for constrained-input optimal control of discrete-time (DT) systems. The deterministic policy gradient (DPG) is introduced to iteratively search the optimal solution to the Hamilton-Jacobi-Bellman (HJB) equation. To deal with input constraints, an action mapping (AM) mechanism is proposed. The objective of this mechanism is to transform the exploration space from the subspace generated by the given inequality constraints to the standard Cartesian product space, which can be searched effectively by existing algorithms. By using the proposed architecture, the learned policy can output control signals satisfying the given constraints, and the original reward function can be kept unchanged. In our study, the convergence analysis is given. It is shown that the iterative algorithm is convergent to the optimal solution of the HJB equation. In addition, the continuity of the iterative estimated Q-function is investigated. Two numerical examples are provided to demonstrate the effectiveness of our approach.

Model-Free Adaptive Positioning Control of the Bidirectional Stick-Slip Piezoelectric Actuator with Coupled Asymmetric Flexure-Hinge Mechanisms.

A model-free adaptive positioning control strategy for piezoelectric stick-slip actuators (PSSAs) with uncertain disturbance is proposed. The designed controller consists of a data-driven self-learning feedforward controller and a model-free adaptive feedback controller with a radial basis function neural network (RBFNN)-based observer. Unlike the traditional model-based control methods, the model-free adaptive control (MFAC) strategy avoids the complicated modeling process. First, the nonlinear system of the PSSA is dynamically linearized into a data model. Then, the model-free adaptive feedback controller based on a data model is designed to avoid the complicated modeling process and enhance the robustness of the control system. Simultaneously, the data-driven self-learning feedforward controller is improved to realize the high-precision control performance. Additionally, the convergence of the tracking error and the boundedness of the control output signal are proved. Finally, the experimentally obtained results illustrate the advantages and effectiveness of the developed control methodology on the bidirectional stick-slip piezoelectric actuator with coupled asymmetric flexure-hinge mechanisms. The positioning error through the proposed controller reaches 30 nm under the low-frequency condition and 200 nm under the high-frequency condition when the target position is set to 100 μm. In addition, the target position can be accurately tracked in less than 0.5 s in the presence of a 100 Hz frequency.

Proposed sub‐synchronous resonance damping controller for large‐scale wind farms

AbstractThis paper deals with the mitigation of sub‐synchronous resonance (SSR) in doubly‐fed induction generator (DFIG)‐based wind farms using a sub‐synchronous resonance damping controller (SSRDC). The performance of the SSRDC depends on its input control signal and the location of its output control signals. Hence, this paper presents an algorithm to select the best location for applying the SSRDC. The DFIG parameters are used as the inputs of this algorithm. Also, the participation factors analysis is employed as this algorithm's main core. The output of this algorithm determines that the control signal of SSRDC can be applied either in the grid‐side converter (GSC) and/or in the rotor‐side converter (RSC). The best input location in the GSC is the DC‐link voltage and the best input location in the RSC is the q‐component of the rotor voltage. The accuracy of this algorithm was evaluated by investigating the effect of various input signal locations on the SSR using the eigenvalue analysis. This analysis indicated that the dc‐link voltage and the q‐components of the rotor voltage are the most effective signals on the sub‐synchronous oscillatory modes. Moreover, this paper introduces a new SSRDC using these two signals. The performance of this controller is validated through the eigenvalue analysis and a time domain simulation.

A surface-enhanced Raman scattering aptasensor for output-signal detection of aflatoxin B1 based on peroxidase-like Cu2O@Au hybrid nanozyme

Aflatoxin B1 (AFB1) as toxic secondary metabolite significantly threatens human health. Hence, we designed a reliable “target control signal output” surface-enhanced Raman scattering (SERS) aptasensor based on catalytic reaction and signal amplification induced by bifunctional semiconductor-bimetallic plasmonic assemblies to monitor AFB1 contamination. SH-modified AFB1 aptamers were coupled to hollow Au–Ag NPs with enhanced SERS features, enabling rapid identification of AFB1. As artificial nanoenzymes, self-assembled Cu2O@Au NCs with peroxidase-like activity produced synergistic catalytic enhancement effects. Complementary chain-modified Cu2O@Au NCs promoted the construction of bifunctional composites. Besides, the preferred binding of the aptamer to AFB1 triggered the dissociation of Au–Ag NPs, causing the reduced SERS effect. Then, in the 3,3′,5,5′-tetramethylbenzidine (TMB) reaction mediated by nanoenzymes, oxidation products (ox TMB) with intrinsic Raman signals appeared, which were ideal quantitative signal. Compared with the labeled detection, the novel post-output mode of SERS signal reduced the signal fluctuation in the sensor construction and improved the accuracy of the results. Thus, the aptasensor exhibited a strong negative linear correlation (R2 = 0.991) in the AFB1 concentration ranging from 0.001 ng/mL to 100 ng/mL, and the detection limit (LOD) was 0.7 pg/mL, indicating that the integration of SERS and enzymatic reactions has potential application prospects in test technology.

Design of a robust and practicable SSI damping controller using H[formula omitted] technique for series compensated DFIG-based wind farms

This paper designs a robust and practicable subsynchronous interaction (SSI) damping controller using H∞ technique for the safe operation of series capacitor compensated wind farms (WFs) with doubly-fed induction generator (DFIG) wind turbines (WTs). Mixed sensitivity control design together with pole placement are formulated into a set of linear matrix inequalities (LMIs) to obtain the controller parameters. The LMI technique allows to include both desirable frequency and time domain specifications. The proposed damping controller is integrated into the WT controller (WTC) and receives the DFIG converter currents as inputs. The implementation of the proposed controller does not require any communication links between the WTs and WF secondary control layer. The controller output signals are applied to the inner control loops of DFIG converters and are dynamically limited for the desired fault-ride-through (FRT) performance. The effectiveness of the damping controller is verified through detailed electromagnetic transient (EMT) simulations. In these simulations, the complete medium-voltage (MV) collector grid is modeled with all details, and it is assumed that the wind speed at the location of each turbine follows a Gaussian distribution. The collected results confirm the accuracy of the modeling of the entire WF as an aggregated WT with the average wind speed.

고속철도차량 해치시스템 동작 특성을 고려한 제어기 설계

A fore section of a high-speed train best expresses the characteristics of the vehicle and is functional by reducing air resistance. The controller of the existing hatch system is designed as a PLC, so it is possible to modify the control circuit without physically changing wiring or relays. However, it is not standardized as each foreign manufacturer provides software in different languages. Therefore, when a failure of the PLC itself occurs, it is impossible to identify the cause, so there is a disadvantage in that response to system failure is delayed.BRIn this paper, the requirements for the controller were derived through the analysis of the operation characteristics and functions of the existing high-speed train hatch system controller. In addition, the hatch system controller was designed by analyzing the operation sequence of the hatch system. And considering the future improvement of the hatch system, the controller was designed as a PCB type that is easy to expand. The normal operation of the controller was tested by virtually applying the hatch operation input signal and the sensor input signal. As a result of the experiment, it was confirmed that the hatch system status output signal and the pneumatic valve control output signal were controlled according to the hatch system operation sequence.

Closed-Loop Microbial Fuel Cell Control System Designed for Online Monitoring of TOC Dynamic Characteristics in Public Swimming Pool.

Total organic carbon (TOC) in the water of public swimming pools (PSPs) must be monitored online for public health. In order to address the shortcomings of conventional microbial fuel cell biosensor (MFC-biosensor), an innovative biosensor with peculiar closed-loop structure was developed for online monitoring of TOC in PSPs. Its design was based on experimental data, model identification, cybernetics, and digital and real-time simulation. The outcomes of the digital simulation demonstrated that the closed-loop MFC control system possesses the desired structure with a pair of dominant complex-conjugate closed-loop poles (−15.47 ± 7.73j), and the real-time simulation showed that its controller output signals can automatically and precisely track the variation in TOC concentration in PSP water with the desired dynamic response performances; for example, mean delay time was 0.06 h, rise time was 0.12 h, peak time was 0.18 h, maximum overshoot was 7.39%, settling time was 0.22 h, and best fit 0.98. The proposed principle and method of the closed-loop MFC-biosensor control system in the article can also be applied for online monitoring of other substances in water, such as heavy metal ions, chemical toxicants, and so forth, and lay a theoretical foundation for MFC-based online monitoring substances in an aquatic environment.

A Hybrid Switching Control Approach to Consensus of Multiagent Systems.

This article develops a novel hybrid switching control (HSC) approach to the consensus of multiagent systems (MASs), and the objective is to further improve the transient consensus performance compared with a reset control approach in recent results. First, a hybrid switched controller is introduced, which consists of two working modes: 1) reset control mode and 2) gain mode. Through appropriate switching mechanism design, the controller output signal can be guaranteed to be continuous, and the input-output of the switched controller always has the same sign. Then, HSC is applied to the consensus of MAS, and a consensus protocol consisting of proportional control part and HSC part is proposed. By introducing binary-value variables, an appropriate closed-loop model is constructed, and the Lyapunov-based consensus analysis results are obtained. Finally, an example is provided to show the superiority of the proposed approach.

Event-based distributed sliding mode formation control of multi-agent systems and its applications to robot manipulators

This article deals with the event-based distributed sliding mode formation control issue of leader-following multi-agent systems subject to uncertain perturbations. Firstly, a distributed linear sliding manifold that ensures the asymptotic convergence of the states of multi-agent systems is constructed. Secondly, based on the controller output signal, an event-triggered mechanism is put forward to weaken the demand of continuous monitoring. In spite of external disturbances, the distributed robust sliding mode formation protocol relied on an event-triggered condition can guarantee the closed-loop system to reach the preestablished sliding manifold in finite time and keep a desired formation pattern while eliminating the Zeno behavior. Thirdly, sufficient conditions are derived for the distributed formation control of multi-agent systems. Finally, a numerical example is given and a simulation is performed on the robotic manipulators systems to verify the effectiveness of the proposed strategy.

An Improved Residual-Based Detection Method for Stealthy Anomalies on Mobile Robots

With the expansion of the cyber-physical system (CPS) application area, its importance has become more and more prominent. As one of the typical applications of CPS, the anomaly detections of mobile robots have attracted the attention of all parties. As part of the CPS, mobile robots face the problem that conventional residual-based detection methods cannot identify stealthy anomalies. The conventional residual-based detection methods mainly use the residual signal calculated from the control signal and measure output for detection, which is widely used in fault diagnosis. Still, it is difficult to be useful in deceptive stealthy anomalies purposefully imposed on mobile robots, which are designed to evade the conventional detections by tampering with measure output. Furthermore, they can control the system to deviate from the expected operations, causing degradation of control performance or even damage without being detected. Based on this, by analyzing the system model of CPS and the stealthy conditions of anomalies, the improved residual-based detection method is proposed in this paper. Moreover, three stealthy anomalies purposefully imposed on an omnidirectional mobile robot (OMR) are detected by using the conventional residual-based methods and the improved residual-based method. Finally, the experimental results show that the method proposed can effectively detect the stealthy anomalies purposefully imposed on the OMR.

A hybrid renewable energy production system using a smart controller based on fuzzy logic

Introduction. This article proposes an improved energy management and optimization system with an intelligent economic strategy based on fuzzy logic technology with multiple inputs and outputs (I/O). It is used to control hybrid electric energy sources built around photovoltaic solar panels, wind turbine and electric energy storage system assisted by the electric grid. The novelty in this work that solar photovoltaic, wind turbine and storage system energy sources are prioritized over the grid network which is solicited only during adverse weather conditions, in order to supply a typical household using up to 4,000 Wh per day. In addition of that, the surplus of renewable energy produced during favorable climatic condition is used to produce hydrogen suitable for household heating and cooking using eletrolyzer system. Purpose. Development of improved energy management and optimization system with an intelligent economic strategy based on fuzzy logic technology. This system is embedded on Arduino 2560 mega microcontroller, on which the fundamental program of fuzzy logic and the distribution of events with all possible scenarios have been implemented according to a flowchart allowing the management of the hybrid system. Methods as well as a parametric search and a simulation to characterize the system, are carried out in order to put on the proposed techniques to ensure continuous accommodation at home. Results. The proposed system results confirm their effectiveness by visualizing the output control signals from the electronic switches. Practical value of which transmits power through a single-phase DC/AC converter to power the AC load for the accommodation.

Innovation of Artificial Intelligence and Digital Media in Environmental Art Design

With the current upsurge of innovation and entrepreneurship in China, this paper objectively analyzes the importance of designers’ professional quality and scientific awareness in response to the need for the integration and development of innovative skills and artistic spirit in environmental design. This paper emphasizes that the level of intelligent operation of science and technology should be improved in design, the rigor of intelligent utilization in creative expression should be improved, and the problem of evaluation criteria of artistic design value should be solved based on professional development. This paper gives the design scheme of intelligent monitoring module based on Lora technology and designs the hardware circuit of the intelligent monitoring module with MSP430F2272 as the core. It includes a microcontroller circuit, a Lora module interface circuit, an input signal detection module, a control output module, and the like. This paper focuses on the content, representation, and implementation of the associated configuration table, and compares four analog input signals with variable thresholds. The sensor detects 800Lux, and the error is 500Lux, which is higher than errmax, so it outputs -10 brightening every time to generate analog input logic. The 4 digital inputs are combined with the analog input logic to generate output control signals by associating operations.

TUNING THE ANGULAR SPEED CONTROLLER OF THE REACTIVE FLYWHEEL OF THE NANOSATELLITE ORIENTATION SYSTEM

The paper describes reactive flywheels based on brushless magnetoelectric motors for controlling the spatial orientation of satellites, developed at the Institute of Electrodynamics of the National Academy of Sciences of Ukraine. The main attention is paid to the problem of implementing a rotor angular speed and position sensor, provided that it can be integrated into the motor design. The influence of the number of feedback signal pulses per one revolution of the motor shaft on the tuning of the reactive flywheel angular speed controller is studied under the condition that the ripple range at the controller output signal is limited at a given level. The dependences of the filter time constant and the controller gains on the value of the reactive flywheel angular speed and on the number of feedback signal pulses per revolution are obtained. Ref. 8, fig. 5, table.

Payload-adaptive iterative learning control for robotic manipulators

Abstract. The main purpose of the iterative learning control (ILC) method is to reduce the trajectory tracking error caused by an inaccurate model of the robot's dynamics. It estimates the tracking error and applies a learning operator to the output control signals to correct them. Today's ILC researchers are suggesting strategies for increasing the ILC's overall performance and minimizing the number of iterations required. When a payload (or a different end effector) is attached to a robotic manipulator, the dynamics of the robot change. When a new payload is added, even the most accurately approximated model of the dynamics will be altered. This will necessitate changes to the dynamics estimates, which may be avoided if the ILC process is used to control the system. When robotic manipulators are considered, this study analyses how the payload affects the dynamics and proposes ways to improve the ILC process. The study looks at the dynamics of a SCARA-type robot. Its inertia matrix is determined by the payload attached to it. The results show that the ILC method can correct for the estimated inertia matrix inaccuracy caused by the changing payload but at the cost of more iterations. Without any additional data of the payload's properties, the suggested technique may adjust and fine-tune the learning operator. On a preset reference trajectory, the payload adaptation process is empirically tested. When the same payload is mounted, the acquired adaptation improvements are then utilized for another desired trajectory. A computer simulation is also used to validate the suggested method. The suggested method increases the overall performance of ILC for industrial robotic manipulators with a set of similar trajectories but different types of end effectors or payloads.

PENERAPAN MODEL PREDICTIVE CONTROL (MPC) PADA FLOW LINE SISTEM PRODUKSI MENGGUNAKAN ALJABAR MAX-PLUS

Model Predictive Control (MPC) is a controller design method is popularly used in the industrial world. This research will be presented at the MPC application of flow line production system modeled using max-plus linear systems. The main advantage of the MPC is its ability to provide certain constraints or limits on the control signal input and output. Flow line of production systems is a diagram illustrating the process sequence in the production system, ranging from the input, processing and output. Of flow line production system, be applicable MPC as time optimization control of the production process. Furthermore, the results of the input and output MPC construction of a flow line production system can be obtained by using matlab program