Output Control Research Articles

Chaos suppression of a Seven-dimensional power system based on the predefined-time sliding mode control method

Abstract To suppress chaotic oscillation in the seven-dimensional power system model, the predefined-time sliding mode control of disturbed seven-dimensional chaotic power system is studied in this paper. Firstly, the dynamic characteristics of the seven-dimensional power system are analyzed, and the existence of chaotic attractors is demonstrated. Then, the seven-dimensional power system is divided into three subsystems according to control outputs, and the chaotic control of the seven-dimensional power system is decomposed into the control problem of three subsystems. For each subsystem, a predefined-time sliding mode controller is designed to regulate the system states converge to the desired values. Compared with other existing control methods, the proposed predefined-time sliding mode controller can control the chaotic power system to the expected states at any predefined time. The minimum upper bound of the convergence time is determined by the adjustable parameters of this controller, which make it meet the control requirements conveniently. Finally, the effective and excellent control performance of the predefined-time control scheme is proved by numerical simulation.

Quadruple Stimuli-Responsive Behaviour of Acylhydrazone Crystals: Mechanical, Photomechanical, Thermomechanical, and Optical Waveguide Properties.

As society advances, the demand for high-performance crystal materials with versatile stimuli-responsive capabilities has increased. However, there remains a notable shortage of simple molecular crystals that exhibit stable and swift responses to various stimuli. In this study, we synthesized a novel acylhydrazone crystal by integrating benzene-fused heterocycles and halogens, achieving a crystal material with multiple stimuli-responsive behaviours. The crystal demonstrates good mechanical flexibility, capable of with standing a strain of 2.3 % under applying mechanical force and recovering its original shape after force removal. Crystal structure analysis reveals this property is due to the strong and flexible intermolecular interactions. It also exhibits rapid photomechanical bending away from light source under UV irradiation, which could be attributed to the facile photoisomerization. Moreover, the crystal undergoes significant thermomechanical transitions, including cracking and jumping, upon heating. Additionally, the prepared crystal exhibits passive optical waveguide of red light at 640 nm, which, combining the photomechanical bending, can achieve controllable light output direction. These multi-stimuli-responsive behaviors make the acylhydrazone crystal a promising candidate for applications in multifunctional sensors, optoelectronic devices, and thermal switches. This work provides a helpful reference for the study of potential crystal material design and functionality of advanced responsive systems.

A Single-Input Multi-Output Inverter with Voltage Boosting for Multi-Load Wireless Power Transfer Systems

Multi-load wireless power transfer systems generally require the configuration of multiple transmitting coils. Using traditional single-output inverters will increase the number of inverters, leading to increased system costs and complex structures. Therefore, this paper proposes a single-input multi-output inverter that can drive multiple transmitting coils simultaneously. Compared with traditional single-output inverters and existing multi-output inverters, the proposed inverter utilizes a topology improvement design and an efficient expansion method. By adding only one inductor, one capacitor, and a small number of power switches, it can generate multiple controllable and stable outputs while ensuring output gain, which is expected to simplify the system structure and improve system performance. This paper first introduces the topology evolution process and operating principle of the proposed inverter. Secondly, a mathematical model is established to analyze its operating characteristics, and its parameter design is carried out. Meanwhile, a comparison with existing multi-output inverters is conducted. Then, the resonant compensation networks are analyzed and selected to match the requirements of different loads. Finally, a simulation model of the proposed inverter is constructed, and an experimental setup is set up. The feasibility and superiority of the proposed inverter are verified through simulation and experiments.

The output flexibility optimization control of load-side wind turbine considering client demand response

Abstract This study focuses on optimizing the output flexibility control of load-side wind turbines considering client demand response. Considering the potential and role of client demand response, a load-side wind turbine output objective function was constructed, client demand response constraints were designed, and a two-layer model for flexible optimization control of load-side wind turbine output was constructed. The top is the power allocation hierarchy, and the core objective of this layer is to achieve power optimization distribution of wind turbines under various constraint conditions. The lower layer is the frequency control layer, which optimizes and controls the dynamic frequency of load-side wind turbine output. The experimental results show that the optimized control strategy not only enhances the efficiency of wind turbines, but also effectively reduces energy loss and enhances the stability and reliability of the power grid. Through precise calculation and real-time adjustment of the double-layer model, we can more accurately predict and control the output of wind turbines, thereby ensuring the supply-demand balance of the power grid and reducing potential risks caused by frequency fluctuations and power offsets.

Enhancing Industrial Valve Diagnostics: Comparison of Two Preprocessing Methods on the Performance of a Stiction Detection Method Using an Artificial Neural Network

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value.

Design of 30 A PWM Type Solar Charge Controller Module for 100 Watt Lamp Load

The electrical energy produced by solar panels has been used as a renewable energy solution to support human life. In reality, this alternative energy has not been widely used due to the constraints of expensive initial investment, so it is necessary to design solar power generation components, one of which is a reliable, optimal, efficient and economical solar charge controller. The controller is made using the switching method. The switching process can occur by setting the duty cycle through the embedded controller in the solar charge controller circuit. Based on the test results, a duty cycle value of 1% to 100% can occur the switching process. The duty cycle that has been set on the solar charge controller is 90%. The duty cycle is a duty cycle where the output voltage approaches the setpoint of the minimum charger voltage. The input voltage from the solar panel when connected to the battery of ±16 volts can be regulated to ±14 volts by the solar charge controller. Based on the battery charging specifications, the controller output can be used to charge a 12 volt battery.

Speed control and maximum efficiency operation of three‐phase squirrel cage induction motors supplied by modified Γ−Z$\Gamma - Z$ impedance source inverter

AbstractA three‐phase squirrel cage induction motor (IM) drive system supplied by a new modified structure of impedance source inverter ( ISI) is proposed. The two main goals are speed (torque) control, and efficiency improvement. The modified ISI has the ability to boost the input voltage. This improves the operation range of the system particularly for limited DC‐link voltage conditions. A first order integral‐terminal sliding mode (ITSM) controller is designed incorporating the motor and inverter dynamic model equations. The maximum efficiency strategy based on Lagrange's theorem is achieved by optimizing the motor input power (input energy) as the objective function under the constant load torque. The maximum efficiency strategy using energy saving is suitable for electric drive system applications. Also, the control method is resilient to any change in motor parameters and unmodulated system dynamics. Furthermore, its output control error can be eliminated in a limited interval. Finally, a prototype of the system using a 1.5 kW three‐phase squirrel cage IM is provided and several tests are conducted. The experimental results show the efficacy of the proposed method.

Closed-loop workload input–output control of production systems: A hybrid simulation study

Workload Control (WLC) is a method of adjusting the overall workload in the production system by controlling the input of production orders and the output of produced items. It contributes to the predictability of lead times and more accurate delivery date commitments in make-to-order manufacturing. Input control relates to order release and output control to capacity adjustment. In a recent previous work, a novel approach to integrated input–output control has been proposed: a dynamic closed-loop model where automatic feedback control is added and where order release and capacity decisions are based on the observed shop floor state. In this paper, this approach is further investigated in a more realistic hybrid simulation setting, combining discrete-time controllers and a discrete-event model of a manufacturing shop. The hybrid model was applied to two different systems: an elementary 4-machine shop floor (as a proof of concept) and a more complex real system, a manufacturing cell of cylinder liners. The results show that simultaneous input–output control is effective and enables the systems to maintain WIP balance, absorb demand fluctuations, and effectively reject disturbances. The simulations of the complex system showed that it might be advantageous to provide more local or more global information to the controllers, depending on the variability of the processing times and on the managerial focus. Closed-loop discrete-event models for WLC production control are unprecedented. Also, a simultaneous input and output control setting is much less reported in WLC literature than settings with input control only (i.e., order release only). This paper contributes in both ways.

Event-triggered fuzzy adaptive control for a class of MIMO discrete-time nonlinear systems with uncertain control gains

ABSTRACT An event-triggered output control technique is devised for the output anticipated trajectory tracking controller design problem for a class of unknown multiple-input-multiple-output (MIMO) discrete-time nonlinear systems. A parameterized state observer is established to estimate the non-measurable states accurately by utilizing the filter states and the gradient-based fuzzy parameter updated laws. A series of fuzzy filters are proposed to evaluate the immeasurable states, and a filter state observer is constructed to overcome the state estimation that is triggered by the unknown control gains and the coupling of control input. One non-zero parameter is added to a fuzzy logic system (FLS) to reduce the computed burden of the controller with several updating laws. A FLS with one non-zero parameter is introduced to reduce the calculated burden of the controller with fewer updating laws. The proposed method reduces the computed burden and communication consumption while realizing the immeasurable state estimations, guaranteeing the ultimately uniformly bounded (UUB) of the closed-loop system, and achieving a good tracking impact. Finally, simulation analyses are used as a numerical example to confirm that the proposed control strategy is valid.

Reducing Peak Power in a Multiple Load System by Delaying the Activation of Electrical Loads Using a Filter Based on a PI Controller

In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems.

Pump-Free Pneumatic Actuator Driven by the Vapor Pressure at the Gas-Liquid Equilibrium of Aqua Ammonia.

Currently, pneumatic soft actuators are widely used due to their impressive adaptability, but they still face challenges for more extensive practical applications. One of the primary issues is the bulky and noisy air compressors required to generate air pressure. To circumvent this critical problem, this work proposes a new type of air pressure source, based on the vapor pressure at the gas-liquid equilibrium to replace conventional air pumps. Compared with the previous phase transition method, this approach gains advantages such as generating gas even at low temperatures (instead of boiling point), more controllable gas output, and higher force density (since both ammonia and water contribute to the gas pressure). This work built mathematical models to explain the mechanism of converting energy to output action force from electrical energy and found the aqua ammonia system is one of the optimal choices. Multiple prototypes were created to demonstrate the capability of this method, including a pouch actuator that pushed a load 20,555 times heavier than its dead weight. Finally, based on the soft actuator, an untethered crawling robot was implemented with onboard batteries, showing the potentially extensive applications of this methodology.

Fuzzy Adaptive Sliding‐Mode Control Optimized by an Arithmetic Optimization Algorithm for a Quadrotor Drone With Chaotic Nonlinear Dynamics

ABSTRACTAn optimal fuzzy adaptive sliding mode controller (OFASMC) is introduced in the present paper for stabilizing a quadrotor drone with chaotic and nonlinear dynamics. At first, control efforts related to the motor torques of the regarded quadrotor drone are determined by the sliding mode procedure as the main stabilizer. Next, using the gradient descent formulation as well as the chain derivative rule, the control gains are adapted through the system states. Then, human knowledge‐based fuzzy systems are appropriately designed to set the control parameters for achieving more accurate results. The output errors and control efforts are minimized through the optimum values found for the effective coefficients of the controller by applying the Arithmetic Optimization Algorithm (AOA). Simulation results clearly illustrate the effectiveness of the introduced strategy to stabilize the quadrotor drone system with and without external disturbances.

Robust integrated control of autonomous vehicles path following with response performance improvement considering lateral stability

In this paper, a robust integrated control scheme is developed for autonomous vehicles path following considering both yaw stability and roll stability. First, a controller with H∞ and optimal guaranteed cost performances is presented, and the poles of the closed-loop system are configured in a given disk region to improve the state response performance. The controller outputs front and rear wheel angles for path following, as well as an external yaw moment and an external roll moment for lateral stability control. Secondly, the braking forces acted on four wheels are optimally distributed by means of quadratic programing. Besides, the steering angles of the four wheels conform to the Ackermann geometry relation. Finally, the proposed control scheme is verified by CarSim/Simulink co-simulation, the results show that the scheme has better performance on path following accuracy and stability of yaw and roll. Meanwhile, constraining the poles in a given disk region can improve the state response performance of the vehicle during operation.

An efficient conditional GAN-based framework for high-resolution prediction of tyre-pavement contact stresses – a contribution towards a digital twin of the road system

ABSTRACT The high-resolution prediction of tyre-pavement contact stresses is crucial for advancing pavement design and vehicle safety. A significant knowledge gap exists in accurately and quickly predicting tyre-pavement contact stresses, especially under varying conditions. Addressing this challenge, this study is part of the concerted effort within the project CRC/TRR 339, contributing to the series ‘A Contribution towards a Digital Twin of the Road System’. It aims to bridge this gap by introducing a novel Multimodal Conditional Deep Convolutional Generative Adversarial Network (MC-DCGAN) model for forecasting tyre-pavement contact stresses, offering an efficient and accurate method. The incorporation of a multimodal conditional module within the GAN-based framework allows for controllable outputs tailored to specific tyre types, inflation pressures, and loads. Leveraging the DCGAN architecture significantly enhances prediction accuracy. Experimental validation confirms the method’s high accuracy, strong generalisation capabilities, and a substantial reduction in computational time – nearly two orders of magnitude lower compared to traditional Finite Element simulations. This research not only fills a critical void in the high-precision prediction of tyre-pavement contact stresses, but also holds significant implications for optimising pavement design and enhancing road traffic safety, marking a significant step towards realising a digital twin of the road system.

Analysis and control design for input‐series output‐parallel multi‐channel inductive power transfer system

AbstractTo realize high‐power inductive power transfer (IPT) for fast charging of electric vehicles (EVs), an input‐series output‐parallel (ISOP) multi‐channel IPT system is analysed in this paper, and an output control strategy based on single neuron controller is proposed to improve the stability of the system. Firstly, the steady‐state operating conditions of ISOP‐IPT system is analysed based on different compensation networks which shows that the constant‐voltage‐output compensation networks are more suitable for the proposed circuit structure. Then, to improve the voltage equalization between channels, an open‐loop control method combining parameter design and phase‐shift control is proposed against different coil misalignments. At the same time, based on the single neuron controller, the system output closed‐loop control is realized without the need of accurate system modelling. Finally, a three‐channel ISOP‐IPT experimental system was constructed, which operated stably at 2.9 kW with a power efficiency of 93.38%. The system closed‐loop control with control time of 22 ms is realized. The voltage equalization control offset is less than 1%.

Takagi-Sugeno fuzzy gain controller for Vehicle-to-Grid (V2G) load frequency control

Load Frequency Control (LFC) has gained more importance with the introduction of deregulated Renewable Energy Sources (RES) connectivity with the grid. Electrical Vehicles (EVs) can feed electricity back into the grid in Vehicle-to-Grid (V2G) mode to maintain stability. However, the increasing number of EVs penetrating the grid causes frequency instability in the power system. If required, EVs may utilize bi-directional chargers to transfer power back to the grid in the V2G mode while they are charging or in a grid-connected state, restoring the frequency instability of the grid. The frequency restoration response time is important to reset the grid frequency fluctuations in the shortest time possible to avoid shutting down the power system. This paper presents a Takagi-Sugeno (T-S) fuzzy linear output controller for LFC in two-area systems with tie-line control. This work models EV batteries as a single lump of large-capacity battery energy storage systems. The EV's battery system provides ancillary power to the two-area power system to reset it to a steady state after a load disturbance. The T-S fuzzy controller's linear output dependency on its inputs enables it to respond efficiently to load variations in the nonlinear two-area power systems. The proposed controller parameters are evaluated from stability analyses and its robustness is tested with sensitivity analysis. It is compared with other fuzzy controllers, and it demonstrates a fast-settling time and reduced frequency deviation response.

Fuzzy Logic-Based Feedwater Controller for the Super Fast Reactor

Abstract Super fast reactor is a Generation IV reactor with water coolant at supercritical pressure. It has advantages of having high efficiency of about 44%, more economical and excellent safety features. Its last core design which adopted one heating in one coolant cycle, however, results a large density change along the fuel channels. It leads to a sensitive steam temperature change against a change of power which might mismatch with the flow change. The sensitivity should be reduced to maintain the structure integrity of the reactor components. A feedwater controller which based on fuzzy logic was used to reduce the sensitivity. Method of fuzzy-based control is applied. Two signals of the temperature and the power changes were taken as the inputs of the fuzzy controller, and change of the feedwater coolant flow was taken as the control output. Fuzzifications used triangular member functions with Tsukamoto fuzzy model for inferencing process. Two control parameters of member function spread and signal values which lead to fuzzy degree of 1 were optimized so that the criteria of outlet coolant temperature and the stability were fulfilled. Test results of the control system showed that the deviation of the water temperature at outlet side could be kept within the criteria when mismatch change of power and water flow happened. The temperature deviation could be supressed by using the fuzzy-based control.

Research on the characteristics of a pneumatic proportional valve

Abstract The execution of autonomous driving in heavy-duty trucks necessitates stringent regulation of their braking torque output. Hydraulic retarder serves as a widely auxiliary braking system in heavy-duty vehicles, with the pneumatic proportional valve functioning as the core component of its control system, regulating the output response and control precision of the braking torque of the hydraulic retarder. To improve the accuracy of brake torque control for hydraulic retarders, it is critical to conduct thorough research on the control system, especially the pneumatic proportional valve. Therefore, A detailed analysis of the working mechanism of pneumatic proportional valves was conducted, encompassing the construction of mathematical and simulation models. The efficacy of the simulation model was confirmed via experimental validation. When the control pressure increases, the simulation results are close to the experimental results, with a maximum discrepancy of 18%; when the control pressure decreases, the simulation results show a similar trend to the experimental results. The research can aid in formulating control methodologies for pneumatic proportional valves and offer theoretical guidelines for future exploration into boosting the precision of hydraulic retarder braking torque control.

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.

Non-overshooting sliding mode for UAV control

AbstractFor a class of uncertain systems, a non-overshooting sliding mode control is presented to make them globally exponentially stable and without overshoot. Even when the unknown stochastic disturbance exists, and the time-variant reference trajectory is required, the strict non-overshooting stabilisation is still achieved. The control law design is based on a desired second-order sliding mode (2-sliding mode), which successively includes two bounded-gain subsystems. Non-overshooting stability requires that the system gains depend on the initial values of system variables. In order to obtain the global non-overshooting stability, the first subsystem with non-overshooting reachability compresses the initial values of the second subsystem to a given bounded range. By partitioning these initial values, the bounded system gains are determined to satisfy the robust non-overshooting stability. In order to reject the chattering in the controller output, a tanh-function-based sliding mode is developed for the design of smoothed non-overshooting controller. The proposed method is applied to a UAV trajectory tracking when the disturbances and uncertainties exist. The control laws are designed to implement the non-overshooting stabilisation in position and attitude. Finally, the effectiveness of the proposed method is demonstrated by the flying tests.