Control Method For Systems Research Articles

Adaptive neural identification and non-singular control of pure-feedback nonlinear systems

This paper proposes a new constructive identification and adaptive control method for nonlinear pure-feedback systems, which remedies the ’explosion of complexity’ and potential control singularity encountered in the traditional adaptive backstepping controllers. First, to avoid using the backstepping recursive design, alternative state variables and the corresponding coordinate transformation are introduced to reformulate the pure-feedback system into an equivalent canonical model. Then, a high-order sliding mode (HOSM) observer is used to reconstruct the unknown states for this canonical model. To remedy the potential singularity in the control, the unknown system dynamics are lumped to derive an alternative identification structure and one-step control synthesis, where two radial basis function neural networks (RBFNN) are adopted to online estimate these lumped dynamics. In this framework, the online estimation of control gain is not in the denominator of controller, and thus the division by zero in the controllers is avoided. Finally, a new online learning algorithm is constructed to obtain the RBFNNs’ weights, ensuring the convergence to the neighborhood of true values and allowing accurate identification of unknown dynamics. Theoretical analysis elaborates that the convergence of both the tracking error and the estimation error is obtained simultaneously. Simulations and practical experiments on a hydraulic servo test-rig verify the effectiveness and utility of the suggested methods.

Robust consensus tracking control for switched multiple Lagrangian systems by UDE-based control method

The robust consensus tracking control of the switched multiple Lagrangian systems is investigated by the uncertainty and disturbance estimator (UDE)-based control method. Firstly, the switching control method based on the average dwell time (ADT) algorithm is utilized for parameters and network communication topology switching of multiple Lagrangian systems to reduce the impact of load variation and enhance robustness. Then, in two parts, a proposed adaptive UDE-based control method for multiple Lagrangian systems with lumped uncertainty and load variation. In the initial stage, a consensus tracking control of the nominal switched multiple Lagrangian systems is realized via an adaptive backstepping tracker. The UDE control method is integrated and used in the second stage for the exact adjustment of model uncertainties and disturbances as well as asymptotic estimates. It is worth noting that by designing different filters for UDE, it is possible to handle several specified external disturbances that are unbounded. The unified analysis framework of the closed-loop switched control system is realized through Lyapunov analysis. Therefore, the proposed consensus tracking algorithm has a simple structure and good robustness in handling load changes, model uncertainties, and external disturbances. Finally, illustrative examples with computer simulation are provided to verify the effectiveness and correctness of the obtained results.

Output Feedback Control of Overhead Cranes Based on Disturbance Compensation

In practice, various factors such as friction, unmodeled dynamics and uncertain external disturbances often affect overhead cranes. The existing crane control methods often neglect these factors or address these factors by robust techniques. Moreover, most of them do not take input saturation into account and require full-state feedback. In this paper, taking the practical issues of uncertain disturbances, input saturation and output feedback into account, we propose an input-saturated output feedback control strategy for the underactuated two-dimensional (2-D) overhead crane systems with uncertain disturbances. Specifically, we first design a disturbance observer that can accurately estimate the external disturbance. Then, the virtual horizontal location signal is introduced and the new energy storage function is constructed. A novel composite control method for overhead crane systems is proposed based on the developed disturbance observer and the new energy storage function. The stability and convergence analysis are given through Lyapunov techniques and LaSalle’s invariance theorem. In order to verify the performance of the proposed controller, we perform a series of simulation tests and compare the proposed method with some existing control methods.

Fast-Frequency-Response Control Method for Electrode Boilers Supporting New Energy Accommodation

With the large-scale integration of new energy generation, represented by wind and photovoltaic power, into the power grid, the intermittency, randomness, and fluctuations of their output pose significant challenges to the safe and stable operation of the power system. Therefore, this paper proposes a control method for electrode boiler systems participating in rapid grid frequency response based on a fuzzy control strategy. This method improves the traditional electrode boiler control strategy, giving it characteristics similar to those of synchronous generators in terms of active power–frequency droop, allowing it to actively adjust active power based on system frequency disturbances. Furthermore, it optimizes its control performance indicators using fuzzy algorithms. The simulation results on the Matlab/Simulink platform demonstrate that the modified electrode boiler control system, when applying this method, can effectively address power disturbances in the system, reduce system frequency deviations, and contribute to enhancing the grid frequency regulation capability and system stability.

An extensive critique on expert system control in solar photovoltaic dominated microgrids

AbstractSolar and wind power have recently become a potential option in power systems and act significantly to meet load penetration demands. The present growth of such renewable energy sources has shown an exponential increase. The high penetration of such system helps a grid effectively meet its load in an irregular demand but also creates some disturbances in the grid due to frequent additions and detachments of load or source. The way by which the renewable energy sources usually work in the on‐grid mode is to be attached to and cut down from the grids without creating disturbances in a stable grid. Another important requirement is effective load management with fewer transmission losses. This article presents a detailed review of a microgrid and enumerates the possible methods for the analysis of the system, feature extraction, control methods, and options for machine learning. This paper examines the factors affecting the operations in a power system, their nature, interdependability, and controllability. It also inspects the various machine learning algorithms, their feasibility, and possible applications in power systems. The major contribution of the paper is the elucidation of expert system control methods for the performance improvement of solar PV assisted DC microgrids. The major objective of the paper is to provide an overview on various algorithms intended for the microgrid systems pertaining to its accuracy, precision, classification, prediction and forecasting.

Dual‐mode event‐triggered predictive control for nonlinear systems with bounded disturbances

AbstractIn this article, we propose a dual‐mode event‐triggered predictive control method for nonlinear systems with bounded disturbances. The proposed method contains two triggering mechanisms, namely, the hybrid threshold‐based event‐triggered model predictive control (HETMPC) mechanism and the event‐triggered linear quadratic regulator mechanism. The former triggering mechanism is designed based on the error between the real state and the optimal state and also the disturbance information acted on the investigated system. Compared with the traditional fixed triggering threshold, the designed HETMPC has a fewer triggering numbers and reduces the computational burden of online real‐time optimization. This event‐triggered mechanism will be adopted before the states go into the terminal invariant set. The latter event‐triggered mechanism is designed based on the derivation of the system state and it will be adopted after the states enter the terminal invariant set. The feasibility and the input‐to‐state practical stability analysis of the designed strategy is presented. Some simulations, including the application to a mass‐spring‐damper system, are provided to show the correctness and feasibility of the designed algorithms.

Coordinated Control of Quadrotor Suspension Systems Based on Consistency Theory

This paper designs a cooperative control method for the multi-quadrotor suspension system based on consistency theory and realizes the cooperative formation trajectory tracking control of the multi-quadrotor suspension system by designing a consistent formation cooperative algorithm of virtual piloting and a nonlinear controller. First, a new quadrotor suspension system model is established based on the traditional quadrotor model using the Newton–Euler method. This model can accurately reflect the influence of the load on the quadrotor while obtaining the swing of the load. Then, the vertical and horizontal positions are designed separately based on the quadrotor motion characteristics, and the formation algorithm based on the virtual pilot consistency theory ensures that the final convergence of each position is consistent. An integral backstepping controller and an integral backstepping sliding mode controller are designed for quadrotor position, attitude, and load swing control to achieve accurate and fast quadrotor trajectory tracking control while reducing load swing. The stability of all the controllers is demonstrated using Lyapunov functions. Finally, a multi-quadrotor suspension system formation cooperative simulation experiment is designed to verify the designed control method.

Multivariable Coupled System Control Method Based on Deep Reinforcement Learning

Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller’s performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects.

Advanced load frequency control of microgrid using a bat algorithm supported by a balloon effect identifier in the presence of photovoltaic power source.

Due to the unpredictability of the majority of green energy sources (GESs), particularly in microgrids (μGs), frequency deviations are unavoidable. These factors include solar irradiance, wind disturbances, and parametric uncertainty, all of which have a substantial impact on the system's frequency. An adaptive load frequency control (LFC) method for power systems is suggested in this paper to mitigate the aforementioned issues. For engineering challenges, soft computing methods like the bat algorithm (BA), where it proves its effectiveness in different applications, consistently produce positive outcomes, so it is used to address the LFC issue. For online gain tuning, an integral controller using an artificial BA is utilized, and this control method is supported by a modification known as the balloon effect (BE) identifier. Stability and robustness of analysis of the suggested BA+BE scheme is investigated. The system with the proposed adaptive frequency controller is evaluated in the case of step/random load demand. In addition, high penetrations of photovoltaic (PV) sources are considered. The standard integral controller and Jaya+BE, two more optimization techniques, have been compared with the suggested BA+BE strategy. According to the results of the MATLAB simulation, the suggested technique (BA+BE) has a significant advantage over other techniques in terms of maintaining frequency stability in the presence of step/random disturbances and PV source. The suggested method successfully keeps the frequency steady over I and Jaya+BE by 61.5% and 31.25%, respectively. In order to validate the MATLAB simulation results, real-time simulation tests are given utilizing a PC and a QUARC pid_e data acquisition card.

Kinematic and dynamic performances of artificial swarm systems: Aggregation, collision avoidance and compact formation

A novel control method for the artificial swarm system is proposed in this paper. To reflect the biological swarm performance (aggregation), a kinematic model of the artificial swarm system is established by introducing a special potential function. Then, the dynamic model is further obtained to satisfy more accurate dynamic swarm performances — collision avoidance and compact formation. The former can be realized by diffeomorphism transformation approach and a safety zone is guaranteed accordingly. As for the compact formation, we creatively convert it into a constraint-following problem between leader and followers. Based on the dynamic model, a novel control consisting of two parts is designed, including the model-based control and the adaptive robust control. On one hand, the trajectory tracking problem of the artificial swarm system is solved. On the other hand, the uncertainties in the artificial swarm system can be compensated perfectly. The deterministic performance, uniform boundedness and uniform ultimate boundedness, is guaranteed. The effectiveness of the collaborative control is verified by the proof based on Lyapunov approach and simulations of the artificial swarm system.

Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification

This study presents an implementation of a proportional–integral–derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously.

Design of Fuzzy Logic Controller for Inductor Based Cell Balancing in Battery Management System

Lithium-ion batteries are a popular energy storage used in various applications including electronic devices, electric vehicles, renewable energy systems, and microgrids. The limitations in capacity and voltage of individual battery cells necessitate arranging them in series and parallel configurations to meet the energy demands of the system. However, connecting cells in series can potentially lead to charge imbalance among cells, resulting in reduced battery pack capacity and triggering safety concerns. Numerous topologies and control methods for battery cell balancing systems have been explored in previous research. In this study, two fuzzy logic controllers with different membership function shapes are designed to drive the duty cycle of PWM signals for inductor-based cell to cell balancing. Based on simulation results, the designed controllers demonstrate good performance compared to conventional methods.

MPPT Control Method of Photovoltaic System under Complex Lighting Conditions

MPPT Control Method of Photovoltaic System under Complex Lighting Conditions

Novel General Regression Neural Networks for Improving Control Accuracy of Nonlinear MIMO Discrete-Time Systems.

In this article, a novel version of the general regression neural network (Imp_GRNN) is developed to control a class of multiinput and multioutput (MIMO) nonlinear discrete-time (DT) systems. The improvements retain the features of the original GRNN along with a significant improvement of the control accuracy. The enhancements include developing a method to set the input-hidden weights of GRNN using the inputs recursive statistical means, introducing a new output layer and adaptable forward weighted connections from the inputs to the new layer, and suggesting an interval-type smoothing parameter to eradicate the need for selecting the parameter beforehand or adapting it online. Also, controller stability is studied using Lyapunov's method for DT systems. The controller performance is tested with different simulation examples and compared with the original GRNN to verify its superiority over it. Also, Imp_GRNN performance is compared with an adaptive radial basis function network controller, an adaptive feedforward neural-network (NN) controller, and a proportional-integral-derivative (PID) controller, where it demonstrated higher accuracy in comparison with them. In comparison with the formerly proposed control methods for MIMO DT systems, our controller is capable of producing high control accuracy while it is model free, does not require complex mathematics, has low computational complexity, and can be utilized for a wide range of DT dynamic systems. Also, it is one of the few methods that aims to improve the control system accuracy by improving the NN structure.

A New Control Strategy of Hybrid Reactive Power Compensation System in Distribution Network

Reactive power compensation is an important measure to improve the power quality of distribution networks, especially with the increasing connection of distribution transformers, heavy industrial electrical equipment, and asynchronous motors, which generate a large amount of reactive power demand. Traditional reactive power compensation devices, such as fixed capacitors (FC) and thyristor switched capacitors (TSC), have some drawbacks, such as low response speed, harmonic generation, and switching shock. A static synchronous compensator (STATCOM) is a flexible AC transmission system (FACTS) device that can provide fast and continuous reactive power compensation, but it also has some limitations, such as high cost, large capacity requirement, and low utilization rate. Therefore, hybrid reactive power compensation systems that combine FC or TSC with STATCOM have attracted much attention in recent years. Many researchers have proposed and studied different topologies and control methods of hybrid systems to overcome the disadvantages of single devices and achieve better performance. However, most of the existing hybrid systems are designed for low or medium-voltage distribution networks, and their performance under high-voltage distribution networks is still unclear. Moreover, the coordination control strategy of the hybrid system and its subsystems is also a key issue that affects the effectiveness and efficiency of reactive power compensation. In view of this, this paper aims to propose a TSC+STATCOM hybrid reactive power compensation system for high-voltage distribution networks, which optimizes the shortcomings of traditional reactive power compensation devices. The system uses the synergistic effect of TSC and STATCOM to achieve large-scale static reactive power and small-scale dynamic reactive power compensation and adopts a switching strategy based on an expert decision system. This paper introduces the working principle of each subsystem of the hybrid reactive power compensation system in detail and builds a simulation model of the hybrid reactive power compensation system under a high voltage distribution network on Matlab/Simulink platform. The simulation results verify the feasibility and excellent performance of the system control method in reactive power compensation and improving power quality.

The time-freezing reformulation for numerical optimal control of complementarity Lagrangian systems with state jumps

This paper introduces a novel time-freezing reformulation and numerical methods for optimal control of complementarity Lagrangian systems (CLS) with state jumps. We cover the difficult case when the system evolves on the boundary of the dynamic’s feasible set after the state jump. In nonsmooth mechanics, this corresponds to inelastic impacts. The main idea of the time-freezing reformulation is to introduce a clock state and an auxiliary dynamical system whose trajectory endpoints satisfy the state jump law. When the auxiliary system is active, the clock state is not evolving, hence by taking only the parts of the trajectory when the clock state was active, we can recover the original solution. The resulting time-freezing system is a Filippov system that has jump discontinuities only in the first time derivative instead of the trajectory itself. This enables one to use the recently proposed Finite Elements with Switch Detection (Nurkanović et al., 2022), which makes high accuracy numerical optimal control of CLS with impacts and friction possible. We detail how to recover the solution of the original system and show how to select appropriate auxiliary dynamics. The theoretical findings are illustrated on a nontrivial numerical optimal control example of a hopping one-legged robot.

Process Quality Control Method for Three-Cylinder Engine Balance Shaft System Oriented to Manufacturing Supply Chain

Automobile manufacturers often outsource key components and parts to suppliers, hence ensuring that process quality has been a hot topic in current research. The quality control demands at different stages of the manufacturing supply chain cycle have been ignored in traditional process quality control methods. To solve this issue, this paper takes the three-cylinder engine balance shaft system as the research object, and builds a process quality control model for the three-cylinder engine balance shaft system oriented to the manufacturing supply chain. The metagraph theory is introduced, and parts suppliers in the manufacturing supply chain, product process quality attributes, supply chain revenue function, and other key elements are defined. By constructing the revenue function of the three-cylinder engine balance shaft system process quality control model, the feasibility of the proposed method is verified by taking the process quality control of one certain three-cylinder engine balance shaft system as a verification example. The results show that, with other conditions unchanged, the application of this method can increase the overall profit of the manufacturing supply chain by 100% at the stage of preparation before product machining, and more than 8% at the stage of product performance evaluation. The proposed method of process quality control based on the metagraph theory provides a theoretical basis for building an information sharing platform of product process quality, and effectively reduces the machining cost for product improvement and upgrading in the manufacturing supply chain.

An Overview of Filtering for Sampled-Data Systems under Communication Constraints

Survey/Review Study An Overview of Filtering for Sampled-Data Systems under Communication Constraints Ye Wang 1,2, Hong-Jian Liu 1,2, and Hai-Long Tan 1,2 1 School of Mathematics-Physics and Finance, Anhui Polytechnic University, Wuhu 241000, China 2 Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China Received: 5 May 2023 Accepted: 21 July 2023 Published: 26 September 2023 Abstract: The sampled-data systems have been extensively applied to practical engineering because the digital signal shows great advantages in data transmission, storage and exchange. As a result, the analysis and synthesis problems of sampled-data systems have attracted ever-growing research interest due mainly to their significant application potential. On the other hand, the filtering or state estimation (which intends to reconstruct real system states from noisy measurements) is viewed as one of the most fundamental research topics in the control community. Until now, a lot of research efforts have been devoted to the filtering problem of sampled-data systems. The objective of the survey is to exhibit a systematic review with respect to filtering and control methods for sampled-data systems under communication constraints. First, some effective filtering algorithms are given. Then, the recent advances are shown in the filtering and control of sampled-data systems subject to network-induced phenomena based on the sampling methods. Finally, some future research topics are given on state estimation of sampled-data systems.

An adaptive impedance control method for polishing system of an optical mirror processing robot

AbstractTo solve the constant contact force control problem between the end tool of a 5 degrees of freedom hybrid optical mirror processing robot and a workpiece, an adaptive impedance control method for the pneumatic servo-polishing system of the robot is designed. Firstly, the pneumatic servo-polishing control system at the end of the robot is set up. Secondly, the impedance control method for contact force is investigated based on the mathematical model of the pneumatic servo-polishing control system. Additionally, the causes of steady-state error of impedance control are analyzed theoretically, and the calculation method for steady-state error of impedance control is deduced. Finally, an indirect adaptive impedance controller based on Lyapunov Stability Principle is developed to estimate the environmental stiffness and position online, so as to reduce steady-state error and realize the tracking of polishing contact force. The simulation and experimental results suggest that the adaptive impedance control method not only recognizes that the contact force of the robot is relatively constant during the polishing process but also has high control accuracy for the force, fast-tracking response for the abrupt force, and considerable adaptability to the variable environmental stiffness.

An improved robust distributed [formula omitted] control method for uncertain interconnected large-scale time-delayed systems

In modern industries, systems have become increasingly large-scale and complex, comprising multiple interconnected subsystems where the performance of each subsystem impacts the others. Ensuring stability and performance under uncertainties, external disturbances, and time delays is crucial for such systems. This paper proposes a new distributed control method that stabilizes a specific type of time-varying input-delayed large-scale system in the presence of uncertainties and external disturbances. The proposed method employs Jensen’s inequality to reduce the number of decision variables and uses reciprocally convex and descriptive methods to minimize conservatism and decrease computation time. The performance of the proposed method is evaluated on a full-car model with an active suspension system as a novel approach to controlling large-scale systems. This paper describes how to divide a large-scale full-car system into several interconnected subsystems, for the first time. The results indicate the potential of the proposed controller in enhancing the performance of the system in comparison with other previous control methods.