Uncertain External Disturbances Research Articles

Extended state observer for uncertain lower triangular nonlinear systems subject to stochastic disturbance

The extended state observer (ESO) is the most important part of an emerging control technology known as active disturbance rejection control to this day, aiming at estimating “total disturbance” from observable measured output. In this paper, we construct a nonlinear ESO for a class of uncertain lower triangular nonlinear systems with stochastic disturbance and show its convergence, where the total disturbance includes internal uncertain nonlinear part and external stochastic disturbance. The numerical experiments are carried out to illustrate effectiveness of the proposed approach.

Control synthesis for an unmanned helicopter with time-delay under uncertain external disturbances

This paper presents the controller synthesis for an unmanned helicopter with minimum initial information about the parameters of its mathematical model with time-delays of measured and control signals. The unknown parameters, wind disturbances, and system nonlinearity are considered as external disturbances that are estimated using a multi-gap observer. The estimates obtained are used in the control law to improve the stability rate for flight regimes.

Depth advanced control of an autonomous underwater robot

Autonomous underwater robot control is a very challenging task because of autonomous underwater robot system nonlinearity, time-variance, uncertain external disturbance and difficulty in hydrodynamic modelling. Based on detailed autonomous underwater robot control survey and description of autonomous underwater robot dynamics, in this article we have used a high order sliding modes controller (HOSMC) for depth control of the autonomous underwater robot, based on the third order sliding modes. High order methods allow overcoming the chattering effect by removing the discontinuity of the control vector. We show that these high order controllers hold the properties of classical SM control laws and remove chattering problem. Different simulations have been carried out to show the performance and effectiveness of the proposed method.

Depth advanced control of an autonomous underwater robot

Autonomous underwater robot control is a very challenging task because of autonomous underwater robot system nonlinearity, time-variance, uncertain external disturbance and difficulty in hydrodynamic modelling. Based on detailed autonomous underwater robot control survey and description of autonomous underwater robot dynamics, in this article we have used a high order sliding modes controller (HOSMC) for depth control of the autonomous underwater robot, based on the third order sliding modes. High order methods allow overcoming the chattering effect by removing the discontinuity of the control vector. We show that these high order controllers hold the properties of classical SM control laws and remove chattering problem. Different simulations have been carried out to show the performance and effectiveness of the proposed method.

Adaptive Sliding Mode Control for A 2 DOF Magnetic Levitation System with Uncertain Parameters

This paper investigates the stability control for a 2 DOF magnetic levitation system with uncertain parameters and external disturbance. The electromagnetic forces between the magnets and coils are obtained by numerical calculation. Then, an adaptive sliding mode controller is proposed to deal with the parameter uncertainties in the system matrices. The robust stability of the closed-loop system is proved by Lyapunov stability theory. Simulation results are presented to verify the effectiveness of the proposed control strategy.

An Adaptive Dynamic Surface Controller for Ultralow Altitude Airdrop Flight Path Angle with Actuator Input Nonlinearity

In the process of ultralow altitude airdrop, many factors such as actuator input dead-zone, backlash, uncertain external atmospheric disturbance, and model unknown nonlinearity affect the precision of trajectory tracking. In response, a robust adaptive neural network dynamic surface controller is developed. As a result, the aircraft longitudinal dynamics with actuator input nonlinearity is derived; the unknown nonlinear model functions are approximated by means of the RBF neural network. Also, an adaption strategy is used to achieve robustness against model uncertainties. Finally, it has been proved that all the signals in the closed-loop system are bounded and the tracking error converges to a small residual set asymptotically. Simulation results demonstrate the perfect tracking performance and strong robustness of the proposed method, which is not only applicable to the actuator with input dead-zone but also suitable for the backlash nonlinearity. At the same time, it can effectively overcome the effects of dead-zone and the atmospheric disturbance on the system and ensure the fast track of the desired flight path angle instruction, which overthrows the assumption that system functions must be known.

Adaptive Sliding Mode Control of Generator Excitation System with Output Constraints

A nonlinear excitation controller for the uncertain parameters and external disturbances is designed by using the backstepping method and sliding mode control theory. In the design process, nonlinear features are retained completely with no linear technique used here. Compared with the excitation controllers designed via conventional linearization, the newly designed one has superiorities in strong robustness to external disturbances, good adaptive ability to variation of system parameters, super transient performance, and the ability to assure preset constraints. Simulation results prove the validity and stability of the controller.

Stability of the Stochastic Model for Power Markets with Interval Parameters

Pertaining to the random nature of demand sides and the range of demand elasticity with suppliers and consumers, a stochastic model for power markets with interval parameters is described to illustrate uncertain external disturbances, which is a generalization of the Alvarado dynamic model, stochastic model, and interval model. The interval stochastic stability criteria of the provided model are investigated by the theory of economics, interval dynamical system, and the theory stability of stochastic differential equations. The conclusions indicate that the demand elasticity stable interval can be calculated and the random excitation intensity does not impact the system stability. Some numerical examples are given to show the applicability and validity of the obtained results from a statistical perspective.

Backstepping silding-mode control of nonlinear TCSC System based on adaptive observer

This paper proposes new approaches for nonlinear thyristor controlled series compensator (TCSC) controller design. The uncertain parameter and external disturbances are considered for practical implementation in power systems. The control law and the adaptive law are designed separately, which can avoid the oscillation of states caused by the coupling between parameter estimator and system states. The adaptive laws of uncertain parameters are estimated by using adaptive observer. The designed adaptive law can ensure that estimation error converge to zero in finite time. Furthermore, the backstepping control and sliding-mode control method is adopted to derive the control law recursively. By using the proposed method, all the state variables in the TCSC system will theoretically attain stability and eventually converge to desired values. Compared with other algorithms, the oscillation amplitudes of transient responses are reduced by nearly half, and the time of reaching steady state is shortened by at least 12%. The results indicate better performance of the proposed controller in both the transient and steady states.

A class of adaptive robust controllers for uncertain dynamical systems with unknown virtual control coefficients

The problem of backstepping approach-based adaptive robust controller design is considered for a class of nonlinear systems in the presence of uncertain system parameters, external disturbances, and unknown time-varying virtual control coefficients. It is supposed that the upper bounds of external disturbances and unknown time-varying virtual control coefficients are unknown. In particular, it is also supposed that the lower bounds of time-varying virtual control coefficients are unknown. Then, the improved adaptation laws with σ-modification are employed to estimate the unknown values containing all the unknown bounds of such uncertainties. With the help of the adaptation laws, a class of backstepping approach-based continuous adaptive robust state feedback controllers is developed. Furthermore, by employing the improved Lyapunov-Krasovskii functional method, it is also shown that the proposed adaptive robust backstepping controller can guarantee the uniform asymptotic stability of such uncertain dynamical systems. Finally, a numerical example is given to demonstrate the validity of the results.

Terminal Sliding Mode Control with Adaptive Law for Uncertain Nonlinear System

A novel nonsingular terminal sliding mode controller is proposed for a second-order system with unmodeled dynamics uncertainties and external disturbances. We need not achieve the knowledge for boundaries of uncertainties and external disturbances in advance. The adaptive control gains are obtained to estimate the uncertain parameters and external disturbances which are unknown but bounded. The closed loop system stability is ensured with robustness and adaptation by the Lyapunov stability theorem in finite time. An illustrative example of second-order nonlinear system with unmodeled dynamics and external disturbances is given to demonstrate the effectiveness of the presented scheme.

Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control

This paper addresses synchronisation problem of high-order multi-input/multi-output (MIMO) multi-agent systems. Each agent has unknown nonlinear dynamics and is subject to uncertain external disturbances. The agents must follow a reference trajectory. An adaptive distributed controller based on relative information of neighbours of each agent is designed to solve the problem for any undirected connected communication topology. A radial basis function neural network is used to represent the controller's unknown structure. Lyapunov stability analysis is employed to guarantee stability of the overall system. By the theoretical analysis, the closed-loop control system is shown to be uniformly ultimately bounded. Finally, simulations are provided to show effectiveness of the proposed control method against uncertainty and disturbances.

Neural network-based adaptive consensus tracking control for multi-agent systems under actuator faults

In this paper, a distributed output feedback consensus tracking control scheme is proposed for second-order multi-agent systems in the presence of uncertain nonlinear dynamics, external disturbances, input constraints, and partial loss of control effectiveness. The proposed controllers incorporate reduced-order filters to account for the unmeasured states, and the neural networks technique is implemented to approximate the uncertain nonlinear dynamics in the synthesis of control algorithms. In order to compensate the partial loss of actuator effectiveness faults, fault-tolerant parts are included in controllers. Using the Lyapunov approach and graph theory, it is proved that the controllers guarantee a group of agents that simultaneously track a common time-varying state of leader, even when the state of leader is available only to a subset of the members of a group. Simulation results are provided to demonstrate the effectiveness of the proposed consensus tracking method.

Nonlinear Observer-based Chaos Control for SPMSM with Both Uncertain Parameters and External Disturbances

This paper proposed a robust chaos control scheme for surface permanent magnet synchronous motor (SPMSM) drive system considering the uncertain parameters and external disturbances. A nonlinear time delay estimator is utilized to estimate the nonlinearities, uncertain parameters and disturbances of the system on line, so it is not necessarily required the exact model of the system. Then, based on the time delay estimator, a simple feedback controller, which is only related to the system errors and control input, is simple and easy to be constructed. The control gains can be obtained easily using pole placement method. The stability of the proposed control scheme is analyzed according to Lyapunov stability theory. Law-filter is also used to improve the performance of the system. Simulation results illustrate the effectiveness of the presented control method.

Nonlinear Observer-based Chaos Control for SPMSM with Both Uncertain Parameters and External Disturbances

Nonlinear Observer-based Chaos Control for SPMSM with Both Uncertain Parameters and External Disturbances

Rotor current control for a doubly-fed induction generator using a novel nonlinear robust control approach based on extended state observer–backstepping

The doubly-fed induction generator (DFIG) has been widely applied in wind power generation systems. However, the applications of DFIG are limited by the failure of power system and inefficient fault ride-through (FRT) strategy, which lead to overcurrent in rotor windings. The overcurrent that results from continuous operation of DFIG may cause a serious tripping accident in wind turbine generators. The occurrence of overcurrent at the rotor side of DFIG must be prevented to ensure safe operation of the rotor-side converter. Preventing overcurrent can enhance the control ability of rotor current and the FRT capacity of DFIG. A novel rotor current controller for DFIG is designed to overcome its strong nonlinearity. The controller combines extended state observer (ESO) with backstepping theory. ESO is used to dynamically estimate and eliminate the model error and the uncertain external disturbance of the rotor current of DFIG. The controller is designed on the backstepping method, and then the feedback of ESO is used to eliminate the nonlinear factor. As a result, computation complexity is reduced. The controller has both superior static, dynamic performance, and strong robustness. The effectiveness of the proposed control approach is verified.

The Sliding Mode Variable Structure Control Method for Brushless DC Motors

The sliding mode variable structure control method for brushless DC motors with uncertain external disturbances and unknown loads is studied. A neural sliding mode control scheme is proposed for reducing chattering of sliding mode control. A global sliding mode manifold is designed in this approach, which guarantees that the system states can be on the sliding mode manifold at initial time and the system robustness is increased. A radial basis function neural network (RBFNN) is applied to learn the maximum of unknown loads and external disturbances. Based on the neural networks, the switching control parameters of sliding mode control can be adaptively adjusted with uncertain external disturbances and unknown loads. Therefore, the chattering of the sliding mode controller is reduced. Simulation results proved that this control scheme is valid.

Distributed adaptive tracking backstepping control in networked nonidentical Lagrange systems

In this paper, the problem of adaptive tracking control in networked nonidentical Lagrange systems is investigated via backstepping schemes. Two distributed tracking control algorithms are designed for directed network topology graph with a spanning tree, where both the leader’s position and its velocity are assumed to be varying. Some generic criteria for adaptive tracking control algorithms with uncertain external disturbance and parametric uncertainties are presented. It is shown that the proposed algorithms only require a subset of the followers access to leader’s position. Furthermore, the adaptive control algorithm for uncertain external disturbance systems is robust, and it can avoid online measurement for neighbors’ velocities and efficiently eliminate the chattering during tracking. The results show that all followers can track the leader’s dynamics. Two examples and their simulations show the effectiveness of the proposed algorithms.

Simulation and experimental tests on active mass damper control system based on Model Reference Adaptive Control algorithm

In the process of sudden natural disasters (such as earthquake or typhoon), the active mass damper (AMD) system can reduce the structural vibration response optimally, which serves as a frequently applied but less mature vibration-reducing technology in wind and earthquake resistance of high-rise buildings. As the core of this technology, the selection of control algorithm is extremely challenging due to the uncertainty of structural parameters and the randomness of external loads. It is not necessary for the Model Reference Adaptive Control (MRAC) based on the Minimal Controller Synthesis (MCS) algorithm to know in advance the structural parameters, which produces special advantages in conditions of real-time change of system parameters, uncertain external disturbance, and the nonlinear dynamic system. This paper studies the application of the MRAC into the AMD active control system. The principle of MRAC algorithm is recommended and the dynamic model and the motion differential equation of AMD system based on MRAC is established under seismic excitation. The simulation analysis for linear and nonlinear structures when the structural stiffness is degenerated is performed under AMD system controlled by MRAC algorithm. To verify the validity of the MRAC over the AMD system, experimental tests are carried out on a linear structure and a structure with variable stiffness with the AMD system under seismic excitation on the shake table, and the experimental results are compared with those of the traditional pole assignment control algorithm.

Smooth second-order nonsingular terminal sliding mode control for reusable launch vehicles

Purpose – The purpose of this paper is to present a sliding mode attitude controller for reusable launch vehicle (RLV) which is nonlinear, coupling, and includes uncertain parameters and external disturbances. Design/methodology/approach – A smooth second-order nonsingular terminal sliding mode (NTSM) controller is proposed for RLV in reentry phase. First, a NTSM manifold is proposed for finite-time convergence. Then a smooth second sliding mode controller is designed to establish the sliding mode. An observer is utilized to estimate the lumped disturbance and the estimation result is used for feedforward compensation in the controller. Findings – It is mathematically proved that the proposed sliding mode technique makes the attitude tracking errors converge to zero in finite time and the convergence time is estimated. Simulations are made for RLV through the assumption that aerodynamic parameters and atmospheric density are perturbed. Simulation results demonstrate that the proposed control strategy is effective, leading to promising performance and robustness. Originality/value – By the proposed controller, the second-order sliding mode is established. The attitude tracking error converges to zero in a finite time. Meanwhile, the chattering is alleviated and a smooth control input is obtained.