Stability Of Control System Research Articles

Stability and stabilization for the hydraulic automatic gauge control system of cold rolling mill with time delay and saturation

This paper studies the stability and stabilization of hydraulic automatic gauge control (HAGC) systems with time-varying delay and actuator saturation. First, a mathematical model of HAGC systems is established considering the measurement delay and saturation factors. With the help of state transformation, the measurement delay is transformed into input delay and the generalized sector condition is adopted to deal with the saturation problem in HAGC systems. Then, a new augmented L–K functional is constructed to contain more delay information to reduce the conservativeness of the stability criteria. Furthermore, by combining the proposed L–K functional with some advanced inequalities, the stability conditions and controller are derived with the aid of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed control algorithm is verified by simulation results.

Analysis and optimization of smart home control system stability analysis and optimization

Analysis and optimization of smart home control system stability analysis and optimization

Necessary and sufficient conditions for global asymptotic of nonlinear satellite orientation control system stability

Necessary and sufficient conditions for global asymptotic of nonlinear satellite orientation control system stability

Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

Photovoltaic Microinverter Based on Buck-Boost Converter and Discharge Circuit

This paper presents a novel topology for photovoltaic microinverters that uses a buck-boost converter coupled with a discharge circuit. The system enables efficient conversion of electrical energy from the solar panel without requiring voltage filters or step-up transformers. For its control, an exponential action control strategy is used, as it allows effective tracking of the reference voltage. The stability of the overall control and inverter system is demonstrated and detailed using the Lyapunov method. The sizing of the components used is also detailed. The performance of this topology is evaluated under various load conditions, revealing low harmonic distortion (THD) in both current and voltage, with the best THD of 1% being obtained at a power factor of 0.8. This makes it a promising solution for photovoltaic energy conversion.

Safety-critical formation control of switching uncertain Euler-Lagrange systems with control barrier functions

This paper investigates the safety-critical formation control problem with disturbance rejection for switching uncertain EL MASs in the presence of multiple stationary/moving obstacles. A safety-critical control method is proposed for achieving a collision-free formation for a group of EL systems subject to safety constraints, model uncertainties, and external disturbances. Specifically, a distributed adaptive nominal control law is designed to accomplish the formation control task. Then, based on ISSf-CBF derived safety constraints, a distributed quadratic programming problem is established for computing the optimal control input subject to safety constraints. The safety and stability of the closed-loop control system have been demonstrated. Finally, one exemplary application to safety-critical formation control of some practical multiple mechanical systems is provided to illustrate the effectiveness of the main result.

Collaborative gas source localization strategy with networked nano-drones in unknown cluttered environments

This paper introduces a novel approach for improving gas source localization in dynamic urban environments, employing a swarm of nano-Crazyflie drones through a hybrid strategy that integrates Adaptive Robotic Particle Swarm Optimization (ARPSO) with Bidirectional Brain Emotional Learning (BBEL). The proposed method refines the ARPSO algorithm into two phases: an initial exploration phase and a subsequent seeking phase. The exploration phase activates when no gas is detected, seamlessly transitioning to the seeking phase upon gas detection. This facilitates efficient information exchange and desired velocity generation within the particle swarm. During the seeking phase, particles conduct measurements, share the global best position, intelligently navigate obstacles, and avoid collisions while directly identifying the global concentration field maximum. Unlike conventional methods, the ARPSO algorithm autonomously adapts its parameters online, mitigating algorithmic failures and local optima challenges. It also considers the limited communication of robots in complex environments, a factor often overlooked in recent methods. To enhance plume tracking robustness, facilitate the controller gain tuning process, and ensure system resilience against environmental disturbances and uncertainties, the BBEL method incorporates a sophisticated fuzzy neural network structure with reinforcement learning-based adaptation mechanisms. The stability of the closed-loop control system is rigorously proven using Lyapunov theory. Numerical simulations in complex urban scenarios validate the algorithm’s effectiveness, showcasing a significant 20% improvement in turnaround time and a flawless 100% success rate with no collisions, in addition to enhanced capabilities in handling uncertainties and disturbances compared to benchmarks like ARPSO-BBEL with unlimited communication range and no delayed communications (ARPSO-BBELU), Sniffy Bug (SB)-BBELU, and ARPSO-PIDU.

Adaptive compound control of laser beam jitter in deep-space optical communication systems

In deep-space optical communication systems, precise pointing and aiming of the laser beam is essential to ensure the stability of the laser link. In this paper, an adaptive compound control system based on adaptive feedforward and Proportional-Integral-Differentiation (PID) feedback is proposed. The feedforward controller is stabilized using Youla-Kucera (YK) parameterization. In the YK parameterized structure, the free parameter Q(z) consists of an adaptive filter. The proposed method constitutes an adaptive feedforward control algorithm through the adaptive filter Q(z). The problem of suppressing laser jitter is transformed into a problem of minimizing a sensitivity function containing the adaptive filter. The stability of the compound control system is ensured by configuring the individual parameters of the YK parameterized feedforward controller and the PID controller, and the adaptive regulation of the controller is realized on the premise of system stability. To verify the effectiveness of the compound control system, we established an experimental platform for laser beam stabilization control. We experimentally compare the effectiveness of the proposed control method with the classical method. The experimental results show that the method proposed in this paper can effectively suppress the complex laser beam jitter consisting of narrow-band sinusoidal and broad-band continuous vibrations.

Fuzzy sliding mode control with adaptive exponential reaching law for inverters in the photovoltaic microgrid

Photovoltaic inverters are widely utilized in microgrid systems working as the key equipment for converting solar energy into usable electricity. This paper presents a fuzzy sliding mode control (FSMC) method for the photovoltaic inverter in a microgrid. The inverter module uses voltage control to achieve stable AC output voltage. Moreover, to deal with system uncertainty and non-linearity in the photovoltaic inverter, a fuzzy controller was designed to realize real-time adaptation of the gains of the constant term and the reaching term in the sliding mode control law, which serves as a compensating controller for traditional sliding mode control. The gains of the exponential reaching law can be adjusted according to the system state instead of fixed gains, which can effectively reduce the chattering phenomenon and improve the robustness of the photovoltaic microgrid. Finally, the Lyapunov stability theory was used to ensure the stability of the entire control system, achieving a high-performance independent power supply for loads in a microgrid. Simulation results show that the designed control system is more robust to load disturbances and has superior dynamic performance.

Advanced Optimal Tracking Control With Stability Guarantee via Novel Value Learning Formulation.

In this article, to solve the optimal tracking control problem (OTCP) for discrete-time (DT) nonlinear systems, general value iteration (GVI) scheme and online value iteration (VI) algorithms with novel value function are discussed. First, the disadvantage of the traditional value function for the OTCP is presented and the novel value function is introduced. Second, we analyze the monotonicity and convergence of GVI and establish the admissibility condition of GVI to evaluate the admissibility of the current iterative control. Note that a novel approach is introduced to analyze the admissibility. Third, based on the attraction domain, improved control policies with online VI can be obtained by judging the location of the current tracking error and reference point. Finally, the stability of the online VI-based control system is guaranteed. Besides, we provide two simulation examples to show the performance of the proposed methods.

Sensorless fuzzy-sliding mode control of five phases permanent magnet synchronous motor using MRAS: in three different operating modes

This paper presents a comprehensive study on sensorless control techniques for five-phase permanent magnet synchronous motors (5P-PMSMs). To optimize the speed control performance of (5P-PMSM), which has more advantages than its counterpart three-phase PMSM system, we utilized a combination of fuzzy logic and sliding mode control techniques to optimize the proposed sliding mode control. Additionally, the Model Reference Adaptive System (MRAS) is employed to estimate the speed and rotor position of the 5P-PMSM to enhance the robustness and adaptability of the control system. The modelling of the machine, detailed with a robust nonlinear strategy based on the sliding control, is introduced to track the system until the desired sliding surface is achieved first. To guarantee that the tracking errors converge, the sliding mode control has a chatter due to the discontinuous term. To mitigate the drawback, the proposed control is designed by combining SMC with fuzzy logic, enhancing the performance in a steady state. Examining the control performance of the integrated control fuzzy-SMC with MRAS in different operating modes, including three tests in reversal speed operation, open phase fault mode, and the low-speed operating mode under load torque. The main aim is to assess and analyze the robustness and performance of the proposed control strategy. The stability of the overall control system is examined using the Lyapunov theorem and Popov’s theory analysis of MRAS observer stability. The simulation results of the suggested control displayed by MATLAB-Simulink illustrate the effectiveness and adaptability of the sensorless control scheme across different operating conditions, offering promising prospects for practical implementation in industrial applications.

Real-time coordinated control strategy for the hybrid electric propulsion system of a flying car with model adaptation and game theory

Coordination among the multiple power components is essential for the hybrid electric propulsion systems since the decision made by one impacts the state and decisions of others due to the coupling mechanical and electrical dynamics. Traditional model-based approaches with weight-sum objectives may result in misleading optimization or even unstable situations because of the inherent poor scalability and synergy. To overcome this issue, this paper proposes a novel game theory-based control strategy with model adaptation mechanism for the hybrid electric flying car to enhance the control performance, system stability, and robustness. Firstly, a control-oriented model of the system is derived with the utilization of the recursive least square parameter estimation method. The non-cooperative game framework is then established with the turboshaft engine subsystem and electric supply subsystem treated as two independent players. The performance of Nash equilibrium solutions with closed-loop and open-loop information structures are investigated where the problem is iteratively solved by exploiting Pontryagin’s Minimum Principle and dynamic programming, respectively. The simulation results demonstrate that the game theory-based controllers can outperform MPC with the weight-sum objectives in terms of control efficiency and robustness improvement and the game theory-based controller with open-loop information structure shows excellent computation efficiency. Moreover, the result of the hardware-in-the-loop experiment demonstrates the real-time performance of the proposed controller.

Feedback Stabilization of Quasi-One-Sided Lipschitz Nonlinear Discrete-Time Systems with Reduced-Order Observer

The feedback stabilization problem for nonlinear discrete-time systems with a reduced-order observer is investigated, in which the nonlinear terms of the systems satisfy the quasi-one-sided Lipschitz condition. First, a discrete-time reduced-order observer for nonlinear systems is designed. Then, a feedback controller with a reduced-order observer is designed for realizing the stabilization of nonlinear discrete-time systems. We prove that the design of a feedback controller and reduced-order observer of systems can be carried out independently in the case of discrete-time with nonlinear terms, which largely reduces the computational complexity of the observer and controller. The introduction of the quasi-one-sided Lipschitz condition simultaneously enhances the robustness and stability of nonlinear control systems. Finally, the feasibility and effectiveness of the proposed design approach is verified by a numerical simulation.

An investigation into the stability of helicopter control system under the influence of time delay using an improved LKF

This paper presents an improved Lyapunov–Krasovskii functional (LKF)-based stability analysis of time-delay systems (TDS) and its application to helicopter control systems. A novel LKF is proposed for the stability analysis of linear TDS after thoroughly examining the effects of each integral associated with a general class of LKF. The suggested stability results in the form of linear matrix inequalities (LMIs) are obtained using the Wirtinger-based integral inequality (WII), reciprocally convex combination lemma (RCCL), and extended reciprocally convex matrix inequality (ERCMI). Numerical examples are utilized to evaluate the impact of the presented theorems on stability. The proposed theorems are then used to examine the stability of helicopter control systems, and the results of these stability analyses are verified using a laboratory model of a helicopter control system.

Adaptive sliding mode and RBF neural network based fault tolerant attitude control for spacecraft with unknown uncertainties and disturbances

Taking the external disturbance, model uncertainty, actuator failure, and actuator saturation into consideration, this paper investigates the nonlinear fault-tolerant attitude control problem for the spacecraft. First, to deal with the disturbance and uncertainty with unknown boundaries, an adaptive sliding mode control method is proposed to stabilize the state variables of the spacecraft attitude control system. Then the actuator failure and saturation are further considered, and the second spacecraft fault-tolerant attitude controller is derived based on adaptive sliding mode control and radial basis function (RBF) neural networks. The adaptive control gains reduction technique is applied in the design process, which can weaken the chattering phenomenon of the controller to some extent, and the stability of the attitude control system is analyzed through Lyapunov theory. In the proposed controller, model information and external disturbance are not required and only the system states are needed. Furthermore, the boundaries of the model uncertainty, external disturbance, actuator fault and saturation are assumed to be existing but unknown by the proposed controllers. These features make the proposed attitude controllers need few modeling information of the spacecraft, or maintain strong robustness even if the model or external environment occurs significant changes. Finally, numerical simulations demonstrate the great performance of the proposed fault-tolerant attitude control method.

Experimental Evaluation of a Takagi–Sugeno Fuzzy Controller for an EV3 Ballbot System

In this paper, experimental results about the performance of a Takagi–Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration of the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi–Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3BRS’s TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3BRS’s body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3BRS’s body at its upright position. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation.

Enhancing energy recovery in automotive suspension systems by utilizing time-delay

This study introduces time-delay active control technology to with the aim of enhancing the system's energy harvesting potential and ride smoothness. The time-delay active suspension represents a nonlinear, multivariable system, and stability analysis in this context is intricate. The mainly challenge lies in effectively leveraging time delay to augment the system's energy collection potential while harmonizing the vehicle's ride comfort and energy efficiency. Addressing this issue, our study proposes enhancing the suspension's energy recovery capability through time-delay control. Initially, the impact of time-delay control parameters on energy collection ability under various operational conditions was analyzed, establishing that appropriate time-delay parameters can enhance energy harvesting capacity. Subsequently, to balance the relationship between vehicle comfort and energy efficiency, the research introduces a method for constructing an optimized objective function based on linear equivalent excitation, thereby quantifying the relationship between system time-domain vibrational response, time-delay control parameters, and external excitations. This approach enables the comprehensive optimization of the suspension system's comfort, safety, and energy efficiency. The control system's stability was analyzed using cluster treatment of characteristic roots method. Finally, simulations and experiments were conducted to evaluate the effectiveness of time-delay active across different scenarios, confirming the efficacy of the proposed control methodology.

OptimML: Joint Control of Inference Latency and Server Power Consumption for ML Performance Optimization

Power capping is an important technique for high-density servers to safely oversubscribe the power infrastructure in a data center. However, power capping is commonly accomplished by dynamically lowering the server processors’ frequency levels, which can result in degraded application performance. For servers that run important machine learning (ML) applications with Service-Level Objective (SLO) requirements, inference performance such as recognition accuracy must be optimized within a certain latency constraint, which demands high server performance. In order to achieve the best inference accuracy under the desired latency and server power constraints, this paper proposes OptimML, a multi-input-multi-output (MIMO) control framework that jointly controls both inference latency and server power consumption, by flexibly adjusting the machine learning model size (and so its required computing resources) when server frequency needs to be lowered for power capping. Our results on a hardware testbed with widely adopted ML framework (including PyTorch, TensorFlow, and MXNet) show that OptimML achieves higher inference accuracy compared with several well-designed baselines, while respecting both latency and power constraints. Furthermore, an adaptive control scheme with online model switching and estimation is designed to achieve analytic assurance of control accuracy and system stability, even in the face of significant workload/hardware variations.

Grid control strategy of wind turbines based on LADRC voltage control

Traditional grid-controlled wind turbines are required to rely on AC power grid operation, but when the power grid fluctuates, it will cause a series of oscillation instability problems in wind turbines, such as low-frequency oscillation, subsynchronous oscillation, etc. The safe and stable operation of the crisis power grid needs to be ensured. In this study, the virtual synchronous machine is supposed to be adopted as the main control strategy for grid-type control, transforming the traditional system into a more robust and reliable one. Additionally, a converter model of the grid-type control network is set up. By implementing grid-type control, voltage support and frequency support can be offered without relying solely on AC power grid operation. This approach effectively addresses issues caused by preventive such as reliance on wind turbines. The stability of the networked control system is dependent on maintaining stable system power. When there are fluctuations in system power, it impacts the stability of the networked control system. To mitigate these effects and enhance overall stability, this paper proposes using linear active disturbance rejection control (LADRC) based voltage and grid controls to suppress voltage fluctuations through linear active disturbance rejection techniques. The overall system performance is demonstrated through simulations conducted using Matlab/Simulink platform.

Model-Free RBF Neural Network Intelligent-PID Control Applying Adaptive Robust Term for Quadrotor System

This paper proposes a quadrotor system control scheme using an intelligent–proportional–integral–differential control (I-PID)-based controller augmented with a radial basis neural network (RBF neural network) and the proposed adaptive robust term. The I-PID controller, similar to the widely utilized PID controller in quadrotor systems, demonstrates notable robustness. To enhance this robustness further, the time-delay estimation error was compensated with an RBF neural network. Additionally, an adaptive robust term was proposed to address the shortcomings of the neural network system, thereby constructing a more robust controller. This supplementary control input integrated an adaptation term to address significant signal changes and was amalgamated with a reverse saturation filter to remove unnecessary control input during a steady state. The adaptive law of the proposed controller was designed based on Lyapunov stability to satisfy control system stability. To verify the control system, simulations were conducted on a quadrotor system maneuvering along a spiral path in a disturbed environment. The simulation results demonstrate that the proposed controller achieves high tracking performance across all six axes. Therefore, the controller proposed in this paper can be configured similarly to the previous PID controller and shows satisfactory performance.