Control Method For Systems Research Articles

Pulse engineering via projection of response functions

We present an iterative optimal control method of quantum systems, aimed at an implementation of a desired operation with optimal fidelity. The update step of the method is based on the linear response of the fidelity to the control operators, and its projection onto the mode functions of the corresponding operator. Our method extends methods such as gradient-ascent pulse engineering (GRAPE) and variational quantum algorithms, by determining the fidelity gradient in a hyperparameter-free manner, and using it for a multiparameter update, capitalizing on the multimode overlap of the perturbation and the mode functions. This directly reduces the number of dynamical trajectories that need to be evaluated in order to update a set of parameters. We demonstrate this approach, and compare it to the standard GRAPE algorithm, for the example of a quantum gate on two qubits, demonstrating a clear improvement in convergence and optimal fidelity of the generated protocol. Published by the American Physical Society 2025

Nonlinear reference tracking using robust MPC without computing all linearised models

The presence of systems with polytopic disturbances in practice is highlighted through a novel reference tracking control method for nonlinear systems. The main steps are to obtain a polytopic linear difference inclusion description of the nonlinear system by linearising the system around the references to be followed using Taylor linearisation and to apply the proposed robust model predictive control method for systems with polytopic disturbances. Not all linearised models are constructed and the main advantage is that through convex combinations, every linearised model is used in the control law computations. The efficiency of the method is demonstrated in a 3D simulation for an aerial vehicle.

Distributed Fault Tolerant Control of Multi-Agent System Based on LSTM Trajectory Prediction Model Under Intermittent Communication Faults

Abstract This paper proposes a novel trajectory prediction based fault-tolerant control method for multi-agent systems under intermittent communication faults. Agents affected by intermittent communication faults are unable to communicate with neighbors or obtain reference tracking information for a period of time. To address this issue, we first employ a trajectory prediction model trained using long short term memory neural network to predict the expected trajectory in the event of intermittent communication faults. Subsequently, a distributed controller based on fixed-time theory is designed to track the predicted values. Compared with the existing fault-tolerant control methods, the proposed strategy achieves smaller consensus errors and a longer tolerable intermittent inter-ruption interval. Moreover, it demonstrates the capability to predict the actual expected trajectory trends of agents during large-angle maneuvers. Finally, the effectiveness of the fault-tolerant control strategy is verified by simulation.

Height Control Method for Air Suspension Systems Based on Model-Free Adaptive Control and Improved Genetic Algorithm

<div>This article presents a height control method for air suspension systems, which are influenced by strong nonlinearity and multiple coupling factors, based on model-free adaptive control (MFAC) using full-form dynamic linearization (FFDL). To address the impact of different damping coefficients of the shock absorber on the height control effect, an improved genetic algorithm is employed to globally optimize the relevant parameters involved in the design of the control law, thereby enhancing the height control performance. The precision of modeling the air suspension system has a direct impact on the simulation of both static and dynamic vehicle models, as well as the accuracy of height control. In this article, an equivalent thermodynamic model of the air suspension system is established based on the principle of energy conservation for height control research. Considering the nonlinearity of the air suspension system and the need to make additional assumptions before modeling, a MFAC method using FFDL is adopted for controller design. Traditional height control methods do not consider the impact of changes in the shock absorber damping coefficient on the height control effect. For different damping coefficients, the body height tracking error is large when using the same height control law initialization parameters. Therefore, an improved genetic algorithm is employed to globally optimize the MFAC parameters under different damping states. The effectiveness of the thermodynamic model of the air suspension system and the MFAC method for height control, with parameters tuned using the improved genetic algorithm, was validated through MATLAB/Simulink simulations.</div>

Improved approximation-free control for the leader-follower tracking of the multi-agent systems with disturbance and unknown nonlinearity.

Approximation-free control effectively addresses uncertainty and disturbances without relying on approximation techniques such as fuzzy logic systems (FLS) and neural networks (NNs). However, singularity problems-where signals exceed preset boundaries under dynamic operating conditions-remain a challenge. This paper proposes an improved approximation-free control (I-AFC) method for the multi-agent system, which introduces a novel singularity compensator, providing a low-complexity design with exceptional adaptability while reducing the risk of singularity issues under changing working conditions (random initial values, system parameter variations, and changes in topology graph and followers' dynamics). Furthermore, theoretical analysis guides parameter selection by demonstrating the method's favorable convergence rate and appropriate control gain. Simulation results validate the approach.

Active disturbance rejection control for reducing vehicle body displacement based on an extended state observer and dynamic fuzzy techniques

This article presents a hybrid control method for an automotive suspension system to improve smoothness and comfort and eliminate the influence of sensor noise. The proposed algorithm is formed by combining an active disturbance rejection control technique and a dynamic fuzzy technique based on an extended state observer. The article’s novel contribution is to propose a solution for determining a reference signal and adjusting control parameters using soft computing techniques. Simulations are performed in three cases (corresponding to three types of road excitation signals) to validate the performance of the proposed algorithm. According to the calculation results, the displacement and acceleration of the vehicle body are significantly reduced when the proposed technique is applied to control the active suspension system. The error of the road disturbance observed by the extended state observer does not exceed 0.5 mm, while the error of the vehicle body displacement is less than 0.001 mm. In addition, the chattering phenomenon caused by sensor noise is completely eliminated once the proposed algorithm controls the active suspension system. In conclusion, the algorithm introduced in this work provides superior performance over traditional control methods, which is validated by simulation results.

Dynamic inverter station current control of HVDC system integrated offshore DFIG wind farm under onshore grid voltage distortions

A power control method for an HVDC system integrating a DFIG-based offshore wind farm supplying a weak onshore grid is the prime focus of this work. The real power generated by the wind farm is regulated by the LCC-based rectifier station and transmitted to the VSC-based inverter station through the HVDC line. The work proposes a control mechanism to integrate these two stations with better power quality management. The inverter station facilitates the effective transmission of generated power to the onshore grid under nominal onshore grid voltages. Nevertheless, the abnormalities in onshore grid voltage lead to converter failures due to huge harmonic grid currents and power pulsations beyond permissible limits; uninterrupted power transfer without compromising power quality is the target. The proposed method utilises a dynamic current control technique at the inverter station to inject compensating current. This enables independent regulation of active and reactive power, mitigating power oscillations and minimising grid current THD induced by onshore grid voltage deviations. The simulations in PSCAD/EMTDC and hardware-in-loop (HIL) experiments using OPAL-RT demonstrate that the proposed control strategy significantly reduces power oscillations while maintaining a grid current THD of 1.47% up to 50% unbalance in onshore grid voltage.

Research on design and control method of active vibration isolation system based on piezoelectric Stewart platform.

Space payloads in orbit are vulnerable to small vibrations from satellite platforms, which can degrade their performance. Traditional methods typically involve installing a passive vibration isolation system between the platform and the payload. However, such systems are usually effective only for high-frequency, large-amplitude vibrations and perform poorly in isolating low-frequency vibrations and resonances below 10Hz. To address this limitation, this paper proposes an active vibration isolation system using a 6-degrees-of-freedom Stewart platform driven by piezoelectric actuators. First, the characteristics of the Stewart platform are analyzed and modeled, with the deformation displacement of each leg calculated through decoupling, allowing for high-precision servo control. Next, given the inherent hysteretic nonlinearity of piezoelectric ceramics, which significantly affects positioning accuracy, the hysteresis mechanism of the actuators is analyzed, and a phenomenological mathematical model based on Bouc-Wen operators is established. A Modified particle swarm optimization (MPSO) method is proposed for identifying the model's nonlinear parameters, significantly enhancing the optimization efficiency. Finally, feedforward inverse compensation and feedback linearization methods are introduced. Experimental results verify that the designed active-passive vibration isolation system greatly improves both the positioning accuracy of the piezoelectric actuators and the active vibration isolation performance of the platform.

An Intelligent Maximum Power Point Tracking Strategy for a Wind Energy Conversion System using Machine Learning Algorithms

Background: Machine Learning (ML) techniques have successfully replaced traditional control algorithms in recent years due to their ability to carry out complicated tasks with significant efficiency and accuracy. Objective: The main objective of the current work is to investigate and compare the performances of different ML models in modeling Maximum Power Point Tracking (MPPT) control for a wind turbine system. The main advantage of the designed MPPT based on ML is that it does not require any detailed mathematical model or prior knowledge of the system, such as turbine parameters or aerodynamic properties, unlike traditional MPPT techniques. Methods: The ML models included in this study were Support Vector Machines, Regression Trees, and Ensemble Trees. Their design was performed through a training process, and their performances were evaluated based on various metrics. During the training phase, the ML models were selected to understand the basic concept of the control strategy and extract essential hidden connections between the inputs and the output of the system. Results: The effectiveness of the control method was investigated using MATLAB/Simulink. The findings of this study revealed that ML models were effective in modeling the MPPT for the studied wind power system, which provides an interesting and sophisticated alternative to classical control methods for wind systems. Conclusion: The ML models designed allow for optimal operation of the system with a simple structure that is independent of system parameters and wind speed measurement and is adaptable for any kind of system.

Cooperative-Critic Learning-Based Secure Tracking Control for Unknown Nonlinear Systems With Multisensor Faults.

This article develops a cooperative-critic learning-based secure tracking control (CLSTC) method for unknown nonlinear systems in the presence of multisensor faults. By introducing a low-pass filter, the sensor faults are transformed into "pseudo" actuator faults, and an augmented system that integrates the system state and the filter output is constructed. To reduce design costs, a joint neural network Luenberger observer (NNLO) structure is established by using neural network and input/output data of the system to identify unknown system dynamics and sensor faults online. To achieve the optimal secure tracking control, an augmented tracking system is formed by integrating the dynamics of tracking error, reference trajectory, and filter output. Then, a novel cost function is designed for the augmented tracking system, which employs the fault estimation and the discount factor. The Hamilton-Jacobi-Bellman equation is solved to obtain the CLSTC strategy through an adaptive critic structure with cooperative tuning laws. Besides, the Lyapunov stability theorem is utilized to prove that all signals of the closed-loop system converge to a small neighborhood of the equilibrium point. Simulation results demonstrate that the proposed control method has good fault tolerance performance and is suitable for solving secure control problems of nonlinear systems with various sensor faults.

Deep Reinforcement Learning and Deadbeat Hybrid Control Method for Hybrid Energy Storage System Considering Nonlinear Power Loss and Model Mismatch

Deep Reinforcement Learning and Deadbeat Hybrid Control Method for Hybrid Energy Storage System Considering Nonlinear Power Loss and Model Mismatch

An adaptive droop control method for hybrid electric aircraft propulsion system

An adaptive droop control method for hybrid electric aircraft propulsion system

A control method of electric boiler phase change thermal storage heating system based on dual-time scale load prediction model

A control method of electric boiler phase change thermal storage heating system based on dual-time scale load prediction model

Electromagnetically induced transparency (EIT) like control method for magnetic coupling wireless power transfer systems

Electromagnetically induced transparency (EIT) like control method for magnetic coupling wireless power transfer systems

A Control Method in Systems with Periodic Signals for Suppressing Higher Harmonic Components in the Subway Car Traction Current

A Control Method in Systems with Periodic Signals for Suppressing Higher Harmonic Components in the Subway Car Traction Current

On Adaptive Fractional Dynamic Sliding Mode Control of Suspension System

This paper introduces a novel adaptive control method for suspension vehicle systems in response to road disturbances. The considered model is based on an active symmetry quarter car (SQC) fractional order suspension system (FOSS). The word symmetry in SQC refers to the symmetry of the suspension system in the front tires or the rear tires of the car. The active suspension controller is generally driven by an external force like a hydraulic or pneumatic actuator. The external force of the actuator is determined using fractional dynamic sliding mode control (FDSMC) to counteract road disturbances and eliminate the chattering caused by sliding mode control (SMC). In FDSMC, a fractional integral acts as a low-pass filter before the system actuator to remove high-frequency chattering, necessitating an additional state for FDSMC implementation assuming all FOSS state variables are available but the parameters are unknown and uncertain. Hence, an adaptive procedure is proposed to estimate these parameters. To enhance closed-loop system performance, an adaptive proportional-integral (PI) procedure is also employed, resulting in the FDSMC-PI approach. A comparison is made between two SQC suspension system models, the fractional order suspension system (FOSS) and the integer order suspension system (IOSS). The IOSS controller is based on dynamic sliding mode control (DSMC) and a PI procedure (DSMC-PI). The results show that FDSMC outperforms DSMC.

An online fault-tolerant control approach based on policy iteration algorithm for nonlinear time-delay systems

This paper introduces an online fault-tolerant control method for nonlinear time-delay systems with actuator faults using a policy iteration algorithm. A new performance index function is proposed to account for time-delay states, from which control laws are derived. The approach combines policy iteration with a fault compensator, solving the Hamilton-Jacobi-Bellman equation associated with this innovative value function via a critic neural network. Actuator faults are reconstructed for online fault compensation without the need for fault detection and isolation. The Lyapunov functional analysis demonstrates the closed-loop system's asymptotic stability in the presence of actuator faults and time-delay states. Simulation results validate the effectiveness of the proposed fault compensation strategy. The key contribution of this paper is the incorporation of time-delay states into the value function of the policy iteration algorithm, addressing the problem of fault compensation in nonlinear time-delay systems.

Event-triggered optimal control with finite-time convergence critic networks for input-constrained nonlinear systems

Abstract This paper proposes an adaptive critic design-based event-triggered optimal control method for input-constrained continuous-time nonlinear systems. Adaptive critic design is a special framework of adaptive dynamic programming that approximates the value function by a critic neural network and derives the approximate optimal control policy through analytical methods. The proposed adaptive critic design considers the control input constraints by introducing a non-quadratic cost function and employs an event-triggered mechanism to reduce the number of controller executions. Unlike the existing event-triggered adaptive critic design, this paper proposes a novel finite-time adaptive law based on regression filtering scheme. The adaptive law utilizes the error information of the network weights to ensure fast convergence to the optimal control law under the event-triggered mechanism, which improves the real-time performance of the system. Additionally, explicit bounds for each parameter in the compact set and the specific convergence time estimates are provided in the convergence analysis. Finally, the effectiveness and practicality of the proposed method for real-time online applications are validated through two simulation examples.

Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with Sensor Faults.

This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The consensus error converges to the prescribed performance bounds in fixed-time by an improved performance function and coordinate transformation. Due to the unknown faults in sensors, the system states cannot be gained correctly; therefore, an adaptive compensation strategy is constructed based on the approximation capabilities of neural networks to solve the negative impact of sensor failures. The reinforcement-learning-based backstepping method is proposed to realize the optimal control of the system. Utilizing Lyapunov stability theory, it is shown that the designed controller enables the consensus error to converge to the prescribed performance bounds in fixed time and that all signals in the closed-loop system are bounded in probability. Finally, the simulation results prove the effectiveness of the proposed method.

Optimizing operations of flexible assembly systems: demonstration of a digital twin concept with optimized planning and control, sensors and visualization

AbstractThis paper presents the development of an optimized planning and control method for flexible manufacturing and assembly systems. While the significant potential of flexible manufacturing concepts to help producers adapt to market developments is recognized, the complexity of the flexible systems and the need to optimally plan and control them is a major obstacle in their practical implementation. Thus, this paper aims to develop a comprehensive digital planning method, based on a digital twin and to demonstrate the feasibility of the approach for practical application scenarios. The approach consists of four modules: (1) a simulation-based optimization module that applies reinforcement learning and genetic algorithms to optimize the module configuration and job routing in cellular reconfigurable manufacturing systems; (2) a synchronization module that links the physical and virtual systems via sensors and event handling; (3) a sensor module that enables a continuous status update for the digital twin; and (4) a visualization module that communicates the optimized plans and control measures to the shop floor staff. The demonstrator implementation and evaluation are implemented in a learning factory. The results include solutions for the method components and demonstrate their successful interaction in a digital twin, while also pointing towards the current technology readiness and future work required to transfer this demonstrator implementation to a full-scale industrial implementation.