Controller Design For Systems Research Articles

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Non-fragile tracking controller design for fractional order systems against active disturbance rejection

Adaptive fixed-time neural consensus control for a class of uncertain nonlinear multi-agent systems with full state constraints

This paper is concerned with the fixed-time consensus control problem for non-strict feedback multi-agent systems with asymmetric output constraints and full state constraints. Considering the feasibility of controlling execution, a novel practical virtual control signal is developed utilizing both saturation function and hyperbolic tangent function to ensure that this signal can remain within the same restricted range as the corresponding state variable throughout entire operation process. In backstepping steps, the design of ideal virtual control signal also adopts a different form of piecewise function than before, introducing high-order polynomial functions to avoid singularity problems in the derivation process. In addition, function approximation ability of radial basis function neural networks technique is applied to estimate uncertainties derived from the system functions and controller design procedure. Moreover, universal barrier Lyapunov function approach is improved for constructing an adaptive constrained synchronization control scheme. By fixed-time stability theory, it is shown that the tracking errors of the MAS converge to an adjustable region around the origin in a fixed time and the state variables always obey their constraints. And the upper bound of the settling time is merely dependent on design parameters, which is not affected by the initial states of MAS. The effectiveness of the proposed control strategy is shown by a numerical simulation example at last. Two scenarios are provided to demonstrate the advantages of the control protocol proposed in this paper.

Bound‐based control design for permanent magnet linear motor servo system with inequality constraints and uncertainties

Bound‐based control design for permanent magnet linear motor servo system with inequality constraints and uncertainties

Controller design and energy analysis for stochastic differential systems with one-sided polynomial growth under finite-time attraction constraint

This article presents a novel controller design approach for stochastic nonlinear systems exhibiting one-sided polynomial growth. The objective is to achieve finite-time attractiveness and estimate control energy consumption. By constructing an appropriate controller and employing inequality techniques, sufficient conditions for achieving finite-time attractiveness in stochastic nonlinear systems are established. Moreover, the article introduces the concept of expected energy consumption for stochastic systems and provides upper bounds on control time and energy consumption. A comprehensive trade-off analysis between control time and energy consumption is conducted to identify the optimal control intensity. The proposed approach is validated through numerical examples, demonstrating its effectiveness in practice.

An analytical criterion for stability testing of delayed Bode’s transfer function with unity feedback

The unity feedback where delayed Bode’s transfer function is placed in forward path is used as reference transfer function in controller design for fractional time delay systems. In this study, bounded input-bounded output stability of this reference transfer function is investigated. This investigation is based on a theorem for fractional order time delay systems using Hassard’s techniques. Sufficient and necessary conditions are obtained for testing the stability of this reference transfer function. Moreover, an explicit formula is given for calculating the number of unstable roots in case of instability. The illustrative examples show that this stability criterion works efficiently.

A novel finite-time stability criteria and controller design for nonlinear impulsive systems

The issue of a novel finite-time stability and stabilization design for a class of nonlinear impulsive systems is discussed in this paper. In previous finite-time control designs for impulsive systems, the convergence rate of these control strategies is even less rapid than the exponential convergence rate when the initial state is distant from the origin. To overcome this problem, two sufficient conditions for the novel finite-time stability of impulsive systems are presented, in which the stabilizing impulses and the destabilizing impulses are considered, respectively. Meanwhile, the upper bound of the settling-time is evaluated. In addition, combining Lyapunov functions theory with impulsive control, we design a controller for impulsive dynamical systems to satisfy the novel finite-time stability. Finally, two simulation examples are given, in which the first example demonstrates the superiority of the proposed finite-time stability criterion for nonlinear impulsive systems by comparing it with traditional finite-time stability, and the second example verifies the effectiveness of the presented controller.

State Control Design of Ostensible Metzler Linear Systems with Unsigned Input Parameters

This paper deals with the design of a complete state control for input unsigned, rank deficient matrix parameters of a linear system with system dynamics defined by ostensible structures of Metzler matrices. The proposed solution is based on the principle of diagonal stabilization of positive systems and uses a stabilizing additional component over the decomposition of the Metzler matrix in solving the incomplete internal positivity of such linear system structures. The novelty of the proposed approach is the unified representation of the parametric constraints of the Metzler matrix and the structurally constrained system inputs using linear matrix inequalities, which guarantees that the closed-loop system will be asymptotically stable. Despite the complexity of the constraint conditions on this class of linear continuous systems, the design conditions are formulated using sharp linear matrix inequalities only. A detailed design process is presented using a system-linearized mathematical model to verify the superiority and practicality of the proposed method.

Multiple output response decoupling: With applications to vibration control of optical tables

This paper introduces a novel control theorem called multiple output response decoupling (MORD) and applies it to the vibration control of optical tables. Control design for multivariable systems is challenging because improving a particular performance might compromise others. Hence, this paper proposes the MORD theorem, which allows independent, simultaneous modifications of all output responses. First, we develop the output response decoupling (ORD) lemma, which can modify specified output responses while keeping others unchanged. Second, we derive the MORD theorem, which can adjust all output responses as individual ORD controls without cross-influences. Third, we apply the MORD theorem to optical table models, enabling the design of particular ORD controllers to suppress ground disturbances and other ORD controllers to minimize strut deflections. Integrating these controllers using the MORD structure provides concurrent optimization of these output responses. Finally, we perform experiments that illustrate the feasibility and effectiveness of the proposed MORD theorem.

Fuzzy PD-sliding mode control design for networked system with time delays

This research specifically addresses the problem of sliding mode control (SMC) in a particular group of T–S fuzzy systems that experience output disturbances and time delays using a networked control system. First, a model for a proportional–derivative sliding mode observer (SMO) is established, subsequently followed by the design of a controller based on SMO to maintain closed-loop system stability. The authors establish a novel mechanism to optimize the bandwidth utilization of the communication network by introducing a newly adjusted event-triggering parameter. After that, addressed a linear matrix inequality (LMI) problem to determine the controller gain and observer coefficients. This was done to guarantee the asymptotic stability of the closed-loop plant. Furthermore, the authors were able to directly identify the disturbances for these plants using the proposed descriptor SMO. Moreover, a parameter-based novel event-triggered scheme, along with a new resultant closed-loop system, will be employed to modify trigger frequency parameters within a unified framework. The controller gains will be determined by solving a linear matrix inequality, subject to certain conditions that ensure sufficiency. These conditions will be deduced to ensure asymptotic stability. In the final section, the authors presented examples from the Wind Energy System (WES) that illustrate the feasibility and effectiveness of our proposed methodology.

Systematic Control Design for Adaptive Rejection of Narrow-Band Disturbances Based on a Novel Resonant Generalized Extended State Observer

In this paper, the adaptive rejection of multiple narrow-band disturbances with time-varying frequencies is investigated. A systematic control approach based on a resonant generalized extended state observer (RGESO) is proposed. The frequency response of the RGESO is shaped into a desired form to reject the total disturbance, including the wide-band uncertainties and the narrow-band disturbances. The state-space realization of the RGESO is also provided to estimate both the states and the total disturbance. To handle the time-varying frequencies of the narrow-band disturbances, a direct frequency estimation approach based on the Levenberg–Marquardt method and an online supervision strategy is presented to improve convergence and reliability, and the estimations are then used to adapt the RGESO parameters. Parameter tuning and stability robustness analysis methods are also developed for the proposed scheme to facilitate its application. Finally, extensive simulations are conducted to demonstrate the effectiveness of our method.

A Lyapunov-based control design for centralised networked control systems under false-data-injection attacks

ABSTRACT A central processing unit receives data from all agents and transmits control commands in a Networked Control System (NCS) which is centralised. Centralised NCSs have numerous applications in industrial settings due to their efficiency, simplicity and cost-effective design. However, centralised NCSs are vulnerable to false data injection (FDI) attacks. Despite the fact that researchers have developed detection and mitigation defense mechanisms during past several years, most of these methods have focused on systems with linear dynamics. Furthermore, the existing literature only assumes the injection of FDI attacks on measurement signals. In this paper, we assume that an adversary has injected the FDI attack into both state measurements and control signals with nonlinear dynamics while considering communication noises and disturbances. We propose a secure nonlinear control design that mitigates FDI attacks in real time by combining learning and model-based approaches. We used Lyapunov stability analysis to design the controller, estimator and updating laws of the neural network (NN). In addition, we selected a network of two robots with Euler–Lagrange dynamics to illustrate the robustness of the proposed controller and estimator.

An Adaptive Control Scheme Based on Non-Interference Nonlinearity Approximation for a Class of Nonlinear Cascaded Systems and Its Application to Flexible Joint Manipulators.

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications.

Electric Vehicle Steering System Fuzzy Modeling and Controller Design

Electric vehicle (EV) steering control becomes much more important in many senses such as driving comfort and safety. Unlike the traditional internal combustion engine powered vehicles that possess only one control input related to the steering wheel, the so‐called direct yaw moment control is yet another control input in EVs thanks to the dispersedly located in‐wheel motors. In view of controller design crucially depending on a proper mathematical model, this paper first focuses on the modeling of EV steering system with uncertainty in the system considered for the latter robust controller design. Then a Takagi‐Sugeno (T‐S) fuzzy model is obtained based on the EV's model, which is capable of varying vehicle velocity in a range. Finally, a robust controller with inputs constraint is designed based on the model, and the effectiveness is verified by computer simulations. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Removing the Feasibility Conditions Imposed on Full-State Constraint Control Design for Discrete-Time Nonlinear Systems

Removing the Feasibility Conditions Imposed on Full-State Constraint Control Design for Discrete-Time Nonlinear Systems

Fuzzy Logic Controller Design for Intelligent Lighting and Air-Conditioning Management Systems

In an effort to conserve energy and optimize the use of resources, this research explores the application of fuzzy logic control techniques to improve energy efficiency in intelligent lighting and air conditioning (AC) management systems. This research aims to investigate how fuzzy logic control strategies can be incorporated into intelligent systems to regulate lighting and air conditioning operations with greater precision, adaptability, and energy efficiency. By utilizing a fuzzy logic algorithm, this research develops a model that is able to dynamically adjust lighting levels and AC settings based on environmental conditions, housing patterns, and user preferences. Fuzzy logic control has proven useful for maintaining the desired level of comfort and optimizing electrical energy consumption through trials. The research results show that the integration of fuzzy logic control methodology offers significant potential to improve energy efficiency in lighting and air conditioning management systems, leading to reduced energy consumption and operational costs. These findings highlight the importance of intelligent control in the sustainable management of electrical equipment and provide valuable insights for the design and implementation of energy-efficient systems in a variety of contexts.

Domain estimation and coupled controller design for high-dimensional nonlinear multi-agent systems

This paper investigates the consensus problem of a class of high-dimensional nonlinear multi-agent systems. The agents are homogeneous and have coupled interactions with their nearby agents. Firstly, we study the consensus of this class of nonlinear multi-agent systems and present some consensus conditions based on the linearization technique. By transforming the consensus problem into a stability problem, we show that there exists a domain such that if all pairs of connected agents stay in the domain, the system will achieve consensus under the consensus conditions. Then, we propose a method to estimate the consensus domain since the accurate description of the domain is still challenging. Moreover, a method of coupled controller design is presented to make the multi-agent systems achieve consensus. At last, two numerical examples and a practical example are used to show the effectiveness of the results proposed in this paper.

Systematic Controller Design for Inverter-Based Microgrids With Certified Large-Signal Stability and Domain of Attraction

Systematic Controller Design for Inverter-Based Microgrids With Certified Large-Signal Stability and Domain of Attraction

Intelligent Structure Identification and Robust Control Implementation

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

A new method for model reduction and controller design of large-scale dynamical systems

A new method for model reduction and controller design of large-scale dynamical systems