Control Of Nonlinear Systems Research Articles

Disturbance Observer-Based-Model-Free Adaptive Fuzzy Fractional-Order Prescribed Performance Control for Nonlinear PEMFC System with Uncertainties and Performance Constraints

Disturbance Observer-Based-Model-Free Adaptive Fuzzy Fractional-Order Prescribed Performance Control for Nonlinear PEMFC System with Uncertainties and Performance Constraints

A novel intelligent control of discrete-time nonlinear systems in the presence of output saturation

In this paper, a model free control method for a class of discrete time nonlinear systems is introduced. A type-3 fuzzy system estimates the unknown parameters required by the control system. The control system only uses the input and output data of the plant and therefore does not need to know its mathematical equations. On the other hand, the phenomenon of output saturation is a challenging problem for all control systems, addressed in detail in the proposed method. The convergence of the proposed method is guaranteed, and the control system is very robust in the face of changes in the dynamics of the plant. The simulation results on discrete-time nonlinear systems show that the proposed method is very accurate despite the high speed of convergence. In addition, the proposed method is robust for modeling uncertainties and has a better root mean square error and step response time compared to the other methods. Also, a comparison has been made between type-1 to type-3 fuzzy systems and control system based on trial and error, which shows firstly the importance of the presence of fuzzy system and secondly the superiority of type-3 fuzzy system compared to the other two types.

Robust position regulation of a 2-DOF experimental helicopter using higher order super twisting sliding mode control

This study presents a third-order super twisting sliding mode control algorithm for robust position regulation of a two-degree-of-freedom (2-DOF) experimental helicopter. The proposed approach explores high-order sliding mode control, offering advantages such as finite-time convergence and continuous control signals. The controller is implemented and validated on the Quanser Aero 2 platform, an inherently unstable and nonlinear system that presents significant challenges for control design including cross-coupling effects, model uncertainties, and external disturbances. Extensive simulations and experimental results are presented to validate the efficacy and robustness of the proposed control strategy. They highlight the controller's ability to achieve precise position control while significantly mitigating the chattering phenomenon, a common drawback associated with traditional sliding mode control. This study contributes to the field of nonlinear control systems by demonstrating the potential of high-order sliding mode control in managing nonlinear, coupled dynamic systems. The successful implementation on a real-world platform underscores the practical applicability of the proposed method in aerospace and robotics applications.

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

Deep neural networks-prescribed performance optimal control for stochastic nonlinear strict-feedback systems

This article explores the application of deep neural networks (DNNs) for optimized backstepping control design in a category of stochastic nonlinear strict-feedback systems with unknown dynamics, focusing on prescribed performance. DNNs, distinguished by their ability to handle high-complexity functions and enhance function approximation, are employed to estimate unknown nonlinear functions. The weight updating strategy for each layer of DNNs is determined through a Lyapunov function analysis. Tomitigate potential issues such as the tracking error exploding at uncertain moments, prescribed performance control (PPC) is introduced to constrain the tracking error within a predetermined range. Subsequently, a novel optimal tracking control scheme, integrating the backstepping method and Hamilton–Jacobi–Bellman (HJB) equation, is presented. The results indicate that all signals in the closed-loop system are bounded in probability, and the tracking error is maintained within a set of predefined arbitrarily small residuals. Simulation outcomes affirm the efficacy of the proposed control scheme.

Global output feedback control for uncertain nonlinear systems with input quantisation and sensor gain perturbation

A new quantised control method via output feedback is proposed by establishing two pairs of time-varying coupled matrix inequalities. Compared to relevant quantised control results, our control method has the novelty: (i) the sensor gain perturbation is allowed to be large and nondifferentiable; (ii) the growth constraint of system nonlinearities is relaxed to depend on unmeasured states and x 1 -polynomial function; (iii) a dynamic scaling technique, instead of backstepping design, is introduced to reduce the computational complexity of controller design. In particular, by using a symbolic function (or hyperbolic tangent function), global asymptotic stability is achieved under any hysteresis quantiser.

Practical fixed-time non-singular sliding mode control of second order nonlinear dynamic systems with chattering and overshooting avoidance

This paper introduces a Practical Fixed-Time Stabilization technique (PFxT) designed for a specific class of nonlinear second-order systems. The closed-loop systems, when appropriately parameterized, exhibit convergence within a predetermined time frame to a confined region near the origin. This outcome is achieved by amalgamating a nonlinear sliding mode approach with a practical fixed-time tracking virtual trajectory. The control algorithmś design incorporates considerations for overshoot reduction and chattering cancellation. Furthermore, the Lyapunov method is employed to establish the practical fixed-time stability of the proposed solution, providing insights into the limits within which the algorithm can be effectively deployed. To complete the algorithm specification, a parameter selection approach is introduced, enabling customization of the desired settling time. Comprehensive simulations are conducted to validate the effectiveness and viability of the proposed PFxT technique.

Neural network optimal control for tripartite UAV confrontation systems based on fuzzy differential game

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation.

Stabilization of fractional nonlinear systems with disturbances via sliding mode control

AbstractIn this article, the sliding mode control (SMC) of fractional nonlinear systems (FNSs) with disturbances is studied. First, a useful fractional power‐rate inequality for is extended to a more general form . Based on the generalized inequality, the method of a modified fractional integral SMC is completed, in which the parameter of the symbolic function is extended to and . This increases the degree of freedom of the sliding mode surface (SMS). Then, the SMC of FNSs is studied for the cases of known and unknown disturbances. In the case of known disturbance, it is proved by the generalized inequality and quadratic Lyapunov function method that the state of the FNSs can converge asymptotically to zero on the SMS. For the case of unknown disturbance, a fractional disturbance observer is used to estimate the disturbance by introducing auxiliary variables . The disturbance estimation error of the proposed FNSs is proved to be bounded. An equivalent global control law is obtained and asymptotic convergence of the FNSs under the action of the controller is proved. Finally, two numerical simulation examples are given to verify the feasibility of the method proposed in this article.

Consensus tracking control for nonlinear second-order multi-agent systems with input magnitude and rate constraints

Consensus tracking control for nonlinear second-order multi-agent systems with input magnitude and rate constraints

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay

Resilient adaptive quantized control for nonlinear cyber-physical systems under deception attacks

Resilient adaptive quantized control for nonlinear cyber-physical systems under deception attacks

Estimation-based event-triggered fixed-time fuzzy tracking control for high-order nonlinear systems

This paper investigates adaptive-estimation-based dynamic event-triggered fuzzy tracking control scheme for high-order nonlinear systems in fixed-time interval. The growing assumptions of coexistence unknown nonlinear uncertainties are removed with the aid of fuzzy logic systems. Contrary to the existing results, an adaptive estimation tactic is formulated to estimate the severe coexistence uncertainties online such that no boundaries are needed for the adaptive parameters, despite approximation errors, unknown virtual control gains and time-varying disturbances. On this estimation mechanism, the virtual control gains have been relaxed to unknown, which is more applicable. In view of controller and actuator, the communication burden is reduced with the aid of dynamic event-triggered rule, and Zeno behavior can be prevented successfully during dynamic sampling and updating of the control signal. Moreover, the singularity problem has been conquered by using the inequality transformation technique and all signals are bounded in fixed-time interval. Finally, two examples are used to verify the feasibility and rationality.

Adaptive Predefined-Time Fuzzy Tracking Control for Output Constrained Non-strict Feedback Nonlinear Systems with Input Saturation

Adaptive Predefined-Time Fuzzy Tracking Control for Output Constrained Non-strict Feedback Nonlinear Systems with Input Saturation

Fault tolerant control of uncertain hydraulic system against actuator fault based on redesign Lyapunov approach

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. This paper proposes the design of fault tolerant control for a class of uncertain nonlinear systems with actuator faults based on Lyapunov redesign principle. First, an assumption is introduced to design the control of the nominal system. Then, a new control law has been introduced to handle the difficulty caused by actuator failures. It is shown that the actuator faults can be completely compensated by the proposed nonlinear controller. Finally, the method will be applied to a hydraulic system to show its effectiveness.

Finite-time adaptive fuzzy event-triggered control for nonlinear time-delay system with input quantization via command filter

This paper investigates the problem of finite-time adaptive fuzzy event-triggered control for a class of uncertain nonlinear systems with input quantization. The nonlinear systems under consideration contain unmodeled dynamics and time-varying delays. Fuzzy logic systems are used to handle unknown nonlinear terms. Additionally, finite-time filters are introduced to solve the problem of “explosion of complexity” as well as to ensure that the derivative of the virtual controller can be approximated in finite time. The Lyapunov–Krasovskii function is used to handle time-varying delays. The hysteresis quantizer is used to ensure adequate precision when there is a low transmission rate requirement. The control strategy combines event-triggered mechanisms and quantization technology to ensure that all signals of the closed-loop system remain bounded in finite time. Finally, two examples are included to demonstrate the effectiveness of the proposed controller.

Adaptive fixed-time tracking control of nonlinear systems with unmodeled dynamics

Adaptive fixed-time tracking control of nonlinear systems with unmodeled dynamics

Observer-based self-triggered adaptive neural network control for nonlinear systems with prescribed time

This paper is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.

Dynamic uncertainty propagation analysis framework for nonlinear control problem based on manifold learning and optimal polynomial method and its application on active suspension system

A novel uncertainty propagation method based on manifold learning and optimal polynomial model is proposed to quantify and analyze the dynamic uncertainties of the nonlinear feedback control system. To deal with the multiple objectives and constraints of the nonlinear control problem, a barrier Lyapunov-function-based nonlinear filtered backstepping controller is developed and applied to active suspension system to obtain superior ride comfort performance under limited structural constraints. Considering the errors in the production, manufacturing, and assembly, the probability density function is employed to quantify the structural parameter uncertainties in the nonlinear control system. Moreover, to reveal the dynamic propagation mechanism of uncertainties in the system with nonlinearity, the manifold learning method is proposed to reduce the dimensionality of the dynamic system to avoid the complexity of uncertainty propagation. Simultaneously, data-driven optimal polynomial model is utilized to accurately approximate the internal mechanism of nonlinear filtered backstepping control system. Based on that, the response uncertainties of the nonlinear control system are accurately and quickly quantified through dynamic moment information and uncertain fluctuation space. Finally, an active suspension system with nonlinearities and uncertainties is developed to verify the effectiveness of the controller with improved ride comfort and better handling safety and the superiority of the framework in terms of efficiency and accuracy of the dynamic uncertainty propagation analysis for nonlinear control problem.

Comparison Between Conventional PID Controller and Neural Network PID Controller Based on DC Motor Speed Control

DC motor is a multivariable, strong coupling and nonlinear controlled system, and is difficult to setting up the accurate mathematical model. So, it cannot achieve good control effect using traditional PID control method. To tackle this problem ANN based PID controller is proposed in this paper. The objective here is to compare the response of DC motor controlled by two different PID controllers.