Presented Control Method Research Articles

Active boundary control of spinning beams with elastic constraints using piezoelectric stack actuator

A spinning beam structure is usually subjected to severe vibration. To suppress this vibration, piezoelectric stacking actuators are installed on the boundary of the spinning beam. Here, an active boundary control model for the vibration of spinning beam structures with elastic constraints is established. The dynamic equation of motion of the coupled system is derived by combining the Lagrange equation with the modified Fourier series method. An active control strategy is used to suppress the vibration of the spinning beam with elastic constraints. The vibration suppression effect of the proposed method under different spinning speeds and different elastic boundary conditions is discussed. It is proven that the current active control scheme can quickly suppress the vibration of the spinning beam structure with elastic constraints. The present control method can provide a new way to suppress the vibrations of spinning beam structures with nonideal boundaries in practical engineering.

Temperature control of electric hotplate based on Smith fuzzy multi-level integral separation PID

In order to improve the processing yield of large-size wafers, the partition control method of silicon carbide wafer heating plate heated by metal oxide conductive coating is studied, and a 4-partition temperature control system based on Smith fuzzy multilevel integral separation PID is proposed so as to solve the surface temperature consistency of the wafer heating plate under the requirement of high precision. Through the thermal effect simulation to establish the system model, the heat distribution in each region is analyzed. And then the heating plate temperature control system based on PLC controller is built, and Smith fuzzy multilevel integral separation PID algorithm to facilitate the practical application to improve the control accuracy is proposed, combined with the system model and the conventional PID algorithm in MATLAB for comparison. Finally the experimental verification is carried out based on the established heating plate temperature control system. Simulation and experimental results show that the present control method has a higher control accuracy, and can realize the consistency of the surface temperature of the heating plate stabilized at about 0.05℃, the average temperature steady-state error ±0.05℃, and the control effect is good.

Adaptive backstepping control using a novel dual-differentiator for multi-degree-of-freedom manipulator uncertain systems with unknown multi-disturbances

In this article, the adaptive sliding mode backstepping controller (ASMBC) using a novel dual-differentiator is proposed for multi-degree-of-freedom (MDOF) manipulator systems with uncertain states and multi-disturbances. The dual-differentiator comprises a tracking differentiator (TD) and a disturbance observer (DOB) based on the proposed TD. A novel tracking differentiator based on the inverse hyperbolic sine function and terminal attractor function (IHSTD) is introduced to reconstruct unknown states like velocity responses of the manipulator systems. Furthermore, the other differentiator, DOB based on aforementioned IHSTD (IHSTD-DOB), is involved in estimating the uncertain and stochastic multi-disturbances affecting the manipulator systems. Then, an ASMBC scheme, combining proposed dual-differentiator, is developed for achieving accurate tracking control of manipulator. Additionally, to handle the “explosion of term” issue in backstepping control, the designed IHSTD is also utilized to estimate the derivative of the virtual control law. The stability of the controller is rigorously analyzed using the Lyapunov method. Finally, numerical simulation results are presented to validate the effectiveness of the proposed scheme. Comparison experiments with the traditional DOB, the scheme except IHSTD, and the classical sliding mode control method are carried out by Simulink. The results illustrate that the present control method not only has an excellent tracking performance but also accurately estimates the unpredictable multi-disturbance and reconstructs the unknown states of the manipulator systems.

Decentralized asymptotic tracking control for time‐varying interconnected nonlinear systems with prescribed performance

AbstractThis article studies decentralized adaptive asymptotic tracking control of time‐varying interconnected nonlinear systems. To address the obstacles caused by interactions and these unknown time‐varying parameters, a smooth function and a bound estimation approach are introduced. Additionally, by means of a class of funnel functions and barrier Lyapunov function approach, a prescribed performance control strategy is constructed, allowing tracking accuracy and the settling time to be predetermined in accordance with control requirements, without relying on initial conditions or design parameters. Particularly, reasoned proofs have been given to confirm that the presented control method not only guarantees the prescribed transient performance, but also achieves the asymptotic tracking without requiring knowledge of high‐order derivatives of the reference signal. Ultimately, the efficiency of our approach is verified by simulation results.

A mode localization inspired vibration control method based on the axially functionally graded design for beam structures

A mode localization inspired vibration control method based on the axially functionally graded design for beam structures

Adaptive fuzzy fixed‐time tracking control of nonlinear systems with unmodeled dynamics

SummaryThe problem of adaptive fuzzy fixed‐time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed‐time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed‐loop system are ensured to be bounded under the fixed‐time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method.

Optimal control of a wind farm in time-varying wind using deep reinforcement learning

Optimal control of a wind farm in time-varying wind using deep reinforcement learning

Critic learning control via zero-sum differential game for affine formation maneuver of multi-agent systems with cyber-attacks

The affine formation maneuver (AFM) control problem using the zero-sum differential game is studied for a second-order multi-agent system (MAS) against cyber-injection attacks on the actuators. First and foremost, the distributed control signals and the cyber-injected attack signals are viewed as two competing teams. Then, the leader-follower tracking error system is established where the followers aim to track the AFMs of the leaders. The leader-follower distributed optimal control policies and the cyber-attack attenuation policies are derived from the Nash equilibrium solution of the Hamilton-Jacobi-Isaac (HJI) equation. The solution of the HJI equation is estimated with the aid of the critic neural network. The weights of the critic system are learned online based on the adaptive dynamic programming (ADP) scheme. The uniformly ultimately bounded stability of the closed-loop system under the formulated zero-sum differential game control strategy has been proven by the Lyapunov stability theorem. A simulation example illustrates that the presented control method can attenuate cyber-injection attacks on the actuators and maintain the AFMs of the agents.

Observer-Based Neural Control of N-Link Flexible-Joint Robots.

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Adaptive finite‐time optimal time‐varying formation control for second‐order stochastic nonlinear multiagent systems

SummaryThis work addresses a fuzzy‐based finite‐time optimal time‐varying formation (TVF) control issue for a class of second‐order stochastic multi‐agent systems (SMASs) with unknown nonlinearities. First, novel optimal cost functions with exponential power terms are constructed, which enables the SMASs to achieve finite‐time stability in the mean square sense with minimum cost. Then, based on the cost functions, an optimal controller is proposed, in which fuzzy logic systems (FLSs) are used as universal approximators to identify the unknown uncertainties. Theorem analyses show that the proposed control strategy can guarantee the mean‐square finite‐time bounded of all signals in the system and then the TVF control task can be simultaneously realized with minimum cost. Finally, the effectiveness of the presented control method is verified by a multiple omni‐directional robot system.

Data-driven two-dimensional integrated control for nonlinear batch processes

Data-driven two-dimensional integrated control for nonlinear batch processes

Study and Evaluation a New Predictive Control Method for Speed and Stator Current Control of Induction Motor

It is clear that modern industries rely heavily on electric motors, especially induction motors. These motors convert electrical energy into mechanical energy. The distinction in the performance of the induction motor lies in that it is powerful when exposed to various operational and environmental variables, and it is also inexpensive. However, there are many traditional disadvantages that appear during the operation of the induction motor in its non-linear mechanical properties in addition to the difficulty in regulating the speed of the motor. In this paper , we present a new control method for controlling the stator current and speed of induction motor based on current control with speed control technique. The present model is based on conventional predictive controller development with a structure which is similar to rotor control and the direct torque control .It has double loops and both loops will use the prediction power. The inner loop controls the stator current based on Finite Control Set - Model Predictive Control (FCS-MPC) and the outer loop controls the speed to maximize the dynamic response of the loop. The MATLAB software has been used to implement the controller circuit. The obtained simulation results indicates that the presented control method has comparable performance to conventional controllers with some reduction in the overshoot and fast response interval.

Finite-Time Adaptive Neural Control for a Class of Nonlinear Systems With Asymmetric Time-Varying Full-State Constraints.

In this article, an adaptive finite-time tracking control scheme is developed for a category of uncertain nonlinear systems with asymmetric time-varying full-state constraints and actuator failures. First, in the control design process, the original constrained nonlinear system is transformed into an equivalent "unconstrained" one by using the uniform barrier function (UBF). Then, by introducing a new coordinate transformation and incorporating it into each recursive step of adaptive finite-time control design based on the backstepping technique, more general state constraints can be handled. In addition, since the nonlinear function in the system is unknown, neural network is employed to approximate it. Considering singularity, the virtual control signal is designed as a piecewise function to guarantee the performance of the system within a finite time. The developed finite-time control method ensures that all signals in the closed-loop system are bounded, and the output tracking error converges to a small neighborhood of the origin. At last, the simulation example illustrates the feasibility and superiority of the presented control method.

Disturbance observer–based backstepping sliding mode cascade control of 2–degrees of freedom hydraulic tunneling robot

For the 2–degrees of freedom position tracking problem of the robotized hydraulic-driven roadheader with high nonlinearities and strong uncertainties, a practical disturbance observer–based backstepping sliding cascade control method together with an adaptive compensator is proposed and investigated. The presented methodology mainly includes a continuous nonsingular fast terminal sliding mode with the backstepping technique and the power reaching law with time-varying coefficients used to achieve satisfactory performance against the multi-source disturbances, two disturbance observers used to approximately estimate the unknown dynamics in the mechanical and hydraulic subsystem, respectively. Simultaneously, a continuous robustifying term is also utilized to compensate for the residual disturbances and enhance the robustness. The presented control method doesn’t need the precise model thanks to the auxiliary disturbance observer, and can ensure fast convergence and small tracking error thanks to the backstepping sliding cascade control and the adaptive robust compensator. Based on Lyapunov theory, stability of the overall closed-loop system is proved rigorously, and asymptotically bounded tracking performance of the robotic manipulator is guaranteed. Finally, 2–degrees of freedom trajectory tracking experiments are conducted, and comparative results effectively verify the superiorities of the proposed method.

Fuzzy Logic Energy Management Based on Wind Turbine for Standalone Application

Wind turbines (WT) offer valuable environmentally friendly energy solutions, particularly for electricity generation, owing to their renewable nature. These renewable energy systems (RESs) present an indispensable solution for sites that are not associated with the electrical grid. However, their intermittent power generation, which can be more or less than that required by the load, due to variable wind patterns, poses a challenge. To overcome these weaknesses, the integration of robustly controlled storage systems is a good proposal as it enhances the stability of standalone applications in terms of voltages and powers. In this paper, an intelligent supervisory power control system is developed to manage the different components of the renewable energy generator (REG). A fuzzy logic proportional–integral PI controller is also introduced, in the permanent magnet synchronous generator (PMSG) and the batteries, to better regulate energy, compared to a conventional PI controller. The main objective of this study is to introduce a new control strategy to satisfy the energy requirement of the load. The configuration of the suggested control methodology consists of a fuzzy logic supervisor (FLS) elaborated to manage energy flows, ensuring equilibrium between production and consumption amidst weather and load changes. The presented control method was applied to an autonomous wind battery station, showcasing its ability to maintain energy supply, minimize fluctuations, and enhance stability. Simulation results, executed in MATLAB/Simulink, confirm the effectiveness of the proposed system under various scenarios of wind speed variability and storage system state of charge.

Leader-following cluster-delay consensus control for first-order nonlinear multi-agent systems based on event-triggered mechanism

In this paper, the event-triggered-based cluster-delay consensus control problem is investigated for leader-following nonlinear multi-agent systems (MASs). In control design, both state triggering in the agents’ network and constant time-delay in the leaders’ communication network are considered. Under the framework of Lyapunov function stability theory, the Lipschitz condition is used to overcome the influence caused by time-delay. In order to further effectively utilize data transmission resources, and reduce the communication load of the topology network between each agent, an event-triggered mechanism is established. Subsequently, a robust cluster consensus control strategy is proposed based on event-triggered mechanism, which can ensure all signals of the controlled system are bounded, and the tracking errors converge to zero. In addition, it can also effectively avoid the Zeno behavior. Finally, the effectiveness of the presented control method and theory is verified by a simulation example.

Enhanced energy management system for isolated microgrid with diesel generators, renewable generation, and energy storages

Enhanced energy management system for isolated microgrid with diesel generators, renewable generation, and energy storages

Develop Emperor Penguin Optimizer for Speed Regulation of BLDC Motor Using PV-Based Z-Source Inverter

This paper presented control methods for the reduction of the ripple torque and the control of speed in the residential of BLDC and different applications using ZSI. Photovoltaic (PV) systems’ renewable energy is regarded as the input of BLDC motors. The ZSI has different benefits such as low cost in addition to improved efficiency. In addition, control of the speed torque ripple is achieved with the consumption of the ZSI. To improve the control performance, the Functional Order Proportional Integral Derivative (FOPID) controller can be utilized. Here, the FOPID controller parameters can be optimized through the consumption of the Emperor Penguin optimization (EPO) algorithm. The errors of torques and speeds are decreased with the consumption of the ZSI with the EPO algorithm. Therefore, to realize the purposes, EPO can be proposed to standardize the FOPID frameworks of the motors of BLDC controllers of the speeds and torque. These presented methods are carried out from the Simulink/MATLAB in addition it is compared among the previous methods of PSO, SSA, also and FA. The performance can be evaluated with two different conditions, constant irradiance conditions, and different irradiance conditions.

Tracking-Differentiator-Based Position and Acceleration Feedback Control in Active Vibration Isolation with Electromagnetic Actuator

In order to improve the performance of the active vibration isolation system (AVIS) with electromagnetic actuator, several problems of vibration control are studied. Position control is a critical component in suspension systems, and the position sensor noise can extremely affect the stability of the system, so a tracking differentiator (TD) is proposed to obtain effective differential signal from relative position sensor. In vibration control, the feedback of acceleration combined with PD position feedback is presented to suppress transmission of periodic vibrations. Then, taking the acceleration transmission as the evaluation index, the acceleration transmission under the presented control method is derived, and the influence of control parameters on vibration isolation performance is discussed in detail. The vibration isolation performance is improved from 24.47 dB to 2.4 dB at resonance frequency, and −34 dB attenuation is achieved at 100 Hz with respect to vibration isolation mount system tested on the ground. The experimental results demonstrate that the performance of active vibration isolation system are improved by the proposed acceleration feedback control.

Self-aligned hybrid carrier-based PWM for modular multilevel converters

The self-aligned hybrid carrier-based pulse-width modulation (PWM) for modular multilevel converters (MMC) based on decentralized control is proposed in this paper. It is based on parallel cells in a submodule (SM) using self-aligned hybrid carrier-based PWM, which combines the concept of level-shifted carrier-based PWM (LSC-PWM) and phase-shifted carrier-based PWM (PSC-PWM) methods. By implementing a decentralized control system digitally, hybrid carriers align themselves. Each SM interacts with the other SMs and each parallel cell in an SM communicates with the other cells to generate hybrid carriers. The proposed control strategy makes advantage of the redundancy idea by adding or removing a cell in the case of cell malfunction and system reconfiguration. A current balancing method is incorporated into the decentralized control system to ensure current balance among parallel cells in an SM. The simulation results illustrate the effectiveness of the presented control method in piecewise linear electrical circuit simulation (PLECS) software.