Bound Of System Uncertainties Research Articles

Reliable Event-Triggered Load Frequency Control of Uncertain Multiarea Power Systems With Actuator Failures

Load frequency control (LFC) is crucial for the economic operation and safety of power systems. Therefore this paper addresses the LFC problem for uncertain multi-area power systems with actuator failures. Specifically, actuator failures, uncertainties and communication bandwidth constraints appearing in multi-area power systems are taken into account simultaneously, and novel reliable event-triggered LFC schemes are proposed to cope with these troubles. The proposed schemes can ensure the asymptotical stability of the closed-loop system when only matched uncertainty exists. For the case of coexisting matched and mismatched uncertainties, the state trajectories of the closed-loop system can be controlled within a bounded set, where the size of the bounded set is only related to the mismatched uncertainty. To illustrate the theoretical results, a numerical example of three-area interconnected power system is presented. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Load frequency directly affects the quality of electric energy and is one of the main observation states of power systems, hence LFC has been widely investigated in the literature. For multi-area power systems, the system model to be controlled may be subjected to multiple unfavorable factors in practical situations, such as limited bandwidths, model uncertainties and actuator failures. To cope with these unfavorable factors, this paper is devoted to developing a unified control framework to guarantee the stability of the frequency deviation based on the event-triggered mechanism. Considering both matched and unmatched system uncertainties may exist as well as the bound of system uncertainties can be unknown, event-triggered control schemes including static event-triggered LFC and adaptive event-triggered LFC are accordingly designed to deal with aforementioned situations such that the closed-loop system is asymptotically or boundedly stable. The research outcome of this paper provides simple but effective LFC approaches that can be used to maintain the reliable and stable operation of multi-area power systems.

Robust longitudinal motion control of underground mining electric vehicles based on fuzzy parameter tuning sliding mode controller

The desire to reduce costs and increase productivity is driving mining industries to deploy underground mining electric vehicles (UMEVs). Due to the complex, working environments in underground mines, safer and more stable control strategies of UMEVs are required than the conventional control approaches used for electric vehicles (EVs). In this paper, first a UMEV model with bounded system uncertainties is derived. Next, a fuzzy parameter tuning (FPT) technique is used to adjust parameters in the sliding mode controller (SMC) against unexpected external disturbances and unbounded system uncertainties, which significantly improves system stability and robustness. Simulation and experimental results demonstrate that the proposed fuzzy parameter tuning sliding mode controller (FPTSMC) achieves better performance than SMCs with parametric uncertainties.

Composite nonlinear feedback–based adaptive integral sliding mode control for a servo position control system subject to external disturbance

This paper presents a composite nonlinear feedback–based adaptive integral sliding mode controller with a reaching law (CNF-AISMRL) for fast and accurate control of a servo position control system subject to external disturbance. The proposed controller exploits the advantages of composite nonlinear feedback (CNF) and sliding mode control (SMC) schemes to improve the transient performance and increase the robustness of the closed-loop system. An integral sliding mode combined with a quick reaching law is designed to eliminate the effect of disturbances, which mitigates chattering and achieves finite-time convergence of the sliding mode. An adaptation tuning approach is utilized to deal with unknown but bounded system uncertainties and disturbances. When considering the model uncertainties and disturbances, the stability of the closed-loop system is verified based on the Lyapunov theorem. Numerical simulations are investigated to the effectiveness of the proposed scheme. The transient performance of load disk position to step signal with disturbances using CNF, composite nonlinear feedback based integral sliding mode control (CNF-ISM), and the proposed CNF-AISMRL schemes is given. The simulation results indicate that, without acquiring the knowledge of bounds on system disturbances, the proposed control scheme has superior performance in the presence of time-varying disturbances.

Adaptive neural sliding mode control with prescribed performance of robotic manipulators subject to backlash hysteresis

Robust and precise control of robot systems are still challenging problems due to the existence of uncertainties and backlash hysteresis. To deal with the problems, an adaptive neural sliding mode control with prescribed performance is proposed for robotic manipulators. A finite-time nonsingular terminal sliding mode control combined with a new prescribed performance function (PPF) is developed to guarantee the transient and steady-state performance of the closed-loop system. Based on the sliding mode variable, an adaptive law is presented to effectively estimate the bound of system uncertainties where the prior knowledge of uncertainties is not needed. To approximate nonlinear function and unknown dynamics, the Gaussian radial basis function neural networks(RBFNNs) is introduced to compensate the lumped nonlinearities. All signals of the closed-loop system are proven to be uniformly ultimately bounded (UUB) by Lyapunov analysis. Finally, comparative simulations are conducted to illustrate superiority and reliability of the proposed control strategy.

Adaptive chattering-free terminal sliding-mode control for full-order nonlinear system with unknown disturbances and model uncertainties

This study investigates an adaptive chattering-free sliding-mode control method for n-order nonlinear systems with unknown external disturbances and uncertain models. The proposed method takes the advantage of finite-time fast convergence to avoid singularity problem and ensure its robustness against system uncertainty and unknown disturbance. To achieve fast convergence from any initial condition to system origin, a full-order terminal sliding-mode controller containing differential terms is proposed based on the property of n-order nonlinear systems. Then the continuous and smooth actual control law is obtained by integrating the differential control law containing the discontinuous sign function to realize chattering free. Meanwhile, instead of evaluating the fixed upper bound of system uncertainty and interference in practical implementations, an adaptive method is utilized for its unknown upper bound estimation. The convergence of the adaptive terminal sliding-mode controller in finite time is verified based on Lyapunov stability theory. Finally, two simulation results demonstrate the effectiveness of the proposed control method.

Adaptive Sliding Mode Control for Ship Autopilot with Speed Keeping

Abstract The paper addresses an important issue in surface vessel motion control practice that the ship dynamics and sailing performance can be affected by speed loss. The vessel speed is significantly decreased by the added resistance generated by waves. An adaptive sliding mode course keeping control design is proposed which takes into account uncertain ship dynamics caused by forward speed variations, while avoiding performance compromises under changing operating and environmental conditions. The sliding mode control provides robust performance for time-varying wave disturbances and time-varying changes in ship parameters and actuator dynamics. After combining the unknown but bounded system uncertainties, the design of the adaptation law is obtained which is based on the Lyapunov’s direct method. Simulations on a ship with two rudders illustrate the effectiveness of the proposed solution.

Erratum to: Robust motion control of a two-wheeled inverted pendulum with an input delay based on optimal integral sliding mode manifold

This paper presents a robust integral sliding mode controller for the back-and-forth motion of a two-wheeled inverted pendulum. The control design of this nonlinear system is based on the linearized system with bounded uncertainty and with an input delay taken into account, where the uncertainty is the integrated effect of the linearization error and bounded system uncertainties. Firstly, a trajectory tacking target is selected according to the control task. Secondly, a quadratic performance criterion with large weight of tilt angle error for optimal control is introduced to “force” the tilt angle of inverted pendulum small enough and in turn to make the linearization error small. Thirdly, a new integral state transformation is used to convert the delayed error system with uncertainty into a delay-free one, and a key relationship between the original state variable and the new state variable is founded. Finally, the robust optimal integral sliding mode controller represented in the form of predictor state is designed by choosing the optimal state of the nominal error system as the integral sliding mode manifold. Numerical simulation shows that the designed controller not only works well in implementing the control task, but also has strong robustness against system uncertainties.

Adaptive Nonsingular Terminal Sliding Mode Control of 6-DOF Manipulator with Modified Switch Function

This paper presents an adaptive nonsingular terminal sliding mode control algorithm with a modified switch function for a 6-DOF manipulator with unknown modeling errors and external disturbances. The finitetime convergence of the controller is analyzed using Lyapunov stability theory. The algorithm avoids singular problems and estimates the upper bound of system uncertainties. A modified switch function is used to achieve precise tracking and reduce chattering in control torque. Finally, the effectiveness of the control method is verified through simulation.

Sliding mode control of longitudinal motions for underground mining electric vehicles with parametric uncertainties

Nowadays, zero-emission electric vehicles become more and more attractive personnel transport vehicles for mining industry. Owing to the special working environment and complex road conditions, a robust controller needs to be designed to achieve a stable and reliable vehicle system for underground mining electric vehicles (UMEVs). This paper first presents the UMEV model with system uncertainties. Then, a new controller based on the sliding mode control (SMC) is designed for UMEVs to track the longitudinal velocity while maintaining longitudinal slip ratio in a desired linear region in the presence of the bounded system uncertainties. The comparison of the simulation results for both the proposed SMC controller and a traditional SMC controller shows that the proposed SMC controller has a better performance for the long-distance up/down slopes with varying rolling resistance coefficients.

Robust Dynamic Event-triggered Control for Linear Uncertain System

Abstract Event-triggered control has a greater significance in networked control system (NCS) as it minimizes communication cost. The present paper introduces a novel event-based robust control framework for linear uncertain system. The robust control law is designed by solving an optimal control problem and realized thorough a dynamic event-triggering mechanism to reduce the communication traffic. A dynamic variable is used to generate the event-triggering law using Input-to-State Stability (ISS) criteria. Proposed control strategies ensure stability in the presence of bounded system uncertainties. Derivation of dynamic event-triggering rule with a non-zero positive inter-event time and corresponding stability criteria for uncertain system are the key contributions of this paper. The validation of proposed algorithm is carried out through a numerical simulation.

Optimized Sensorless Antivibration Control for Semiactive Suspensions With Cosimulation Analysis

This paper proposes an optimized sensorless control methodology to design semiactive controllers for vehicle vibration control. The semiactive suspension model including filtered feedback scheme and actuator dynamics is first investigated. Furthermore, to estimate the immeasurable states for feedback control, an observer considering practical applications is introduced, while the exponential convergence and the stability of the overall system are guaranteed. A direct fuzzy compensation scheme with genetic optimization is used to release the constraint requiring, in advance, the precise upper bound of system uncertainties in the control gain design. With the cosimulation approach, the multibody virtual prototype of a sedan with the proposed system is tested under various road conditions in a near real environment. The cosimulation data and results for a full-car semiactive suspension system are provided to validate the effectiveness of the proposed control system.

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

SUMMARYA new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

Time-Varying Integral Adaptive Sliding Mode Control for the Large Erecting System

Considering the nonlinearities, uncertainties of large erecting system, and the circumstance disturbances in erecting process, a novel sliding mode control strategy is proposed in this research. The proposed control strategy establishes the sliding mode without reaching phase using an integral sliding surface. Thus, robustness against uncertainties increases from the very beginning of the process. Furthermore, adaptive laws are used for the controller to estimate the unknown but bounded system uncertainties. Therefore, the upper bounds of the system uncertainties are not required to be known in advance. Then, the time-varying term is applied to ensure the global robustness. Moreover, the boundary layer method is used to attenuate the high frequency chattering. The experiment results demonstrated that the proposed strategy could effectively restrain parametric uncertainties and external disturbances and improve the tracking accuracy in the erecting process. In addition, the control performance of the proposed control strategy is better than that of the PID control and the conventional sliding mode control.

Sliding Mode Control with State Derivative Output Feedback in Reciprocal State Space Form

This paper investigates the novel sliding mode control design with state derivative output feedback in nontraditional reciprocal state space (RSS) form. The concepts and the need of RSS form are comprehensively reviewed and explained. Novel switching function and approaching condition based on the derivative of sliding surface are introduced. In addition, a sufficient condition for finding the upper bound of system uncertainty to guarantee the stability in sliding surface is developed for robustness analysis. A compact sliding mode controller utilizing only state derivative related output feedback is proposed for systems with system uncertainty, matched input uncertainty, and matched external disturbance. Simulation results for a circuit system successfully verify the validities of the proposed algorithms. Our derivation is basically parallel to that for systems in standard state space form. Therefore, those who understand the concepts of sliding mode control can easily apply our method to handle more control problems without being involved in complex mathematics.

Adaptive Sliding-Mode Control for Nonlinear Systems With Uncertain Parameters

This correspondence proposes a systematic adaptive sliding- mode controller design for the robust control of nonlinear systems with uncertain parameters. An adaptation tuning approach without high- frequency switching is developed to deal with unknown but bounded system uncertainties. Tracking performance is guaranteed. System robustness, as well as stability, is proven by using the Lyapunov theory. The upper bounds of uncertainties are not required to be known in advance. Therefore, the proposed method can be effectively implemented. Experimental results demonstrate the effectiveness of the proposed control method.

Robust control of nonlinear systems using neural network based HJB solution

In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm for robust controller design is proposed for a nonlinear system. Utilising the Lyapunov direct method, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort, the maximum bound on system uncertainty and cross-coupling between system state and control. The controllers are continuous and require the knowledge of the upper bound of system uncertainty. In the present algorithm, neural network is used to approximate value function to find approximate solution of HJB equation using least squares method. Proposed algorithm has been applied on a nonlinear system with matched uncertainties. It is also applied to the system having uncertainties in input matrix. Results are validated through simulation studies.

Adaptive Sliding-Mode Control for NonlinearSystems With Uncertain Parameters

This correspondence proposes a systematic adaptive sliding-mode controller design for the robust control of nonlinear systems with uncertain parameters. An adaptation tuning approach without high-frequency switching is developed to deal with unknown but bounded system uncertainties. Tracking performance is guaranteed. System robustness, as well as stability, is proven by using the Lyapunov theory. The upper bounds of uncertainties are not required to be known in advance. Therefore, the proposed method can be effectively implemented. Experimental results demonstrate the effectiveness of the proposed control method.

Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks

In this paper, a multi-layered feed-forward neural network is trained on-line by robust adaptive dead zone scheme to identify simulated faults occurring in the robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Consider the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on line to ensure convergence of the training algorithm, in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator.

Robust adaptive fault accommodation for a robot system using a radial basis function neural network

A discrete-time radial basis function (RBF) neural network is designed for the fault accommodation of robotic systems. A robust learning algorithm using the adaptive dead-zone technique is presented to train the network parameters (weights and centres). This scheme assures the convergence of the estimate errors of both the neural network and the fault-monitoring system in the presence of system uncertainties. Simulations have been done on applying the RBF-network-based fault accommodation scheme to a two-link robotic manipulator. The main advantage of the adaptive algorithm is that the upper bound of system uncertainties is not known in advance, which makes the system more practical for the fault accommodation scheme as demonstrated.

Robust adaptive fault accommodation for a robot system using a radial basis function neural network

A discrete-time radial basis function (RBF) neural network is designed for the fault accommodation of robotic systems. A robust learning algorithm using the adaptive dead-zone technique is presented to train the network parameters (weights and centres). This scheme assures the convergence of the estimate errors of both the neural network and the fault-monitoring system in the presence of system uncertainties. Simulations have been done on applying the RBF-network-based fault accommodation scheme to a two-link robotic manipulator. The main advantage of the adaptive algorithm is that the upper bound of system uncertainties is not known in advance, which makes the system more practical for the fault accommodation scheme as demonstrated.