Compensation Of External Disturbances Research Articles

Predictor-Based Neural Attitude Control of A Quadrotor With Disturbances

An attitude control issue is concerned for a quadrotor with external disturbances in this paper. For unknown system dynamics, predictor-based neural networks (NNs) are introduced, where prediction errors, angular velocities, are constructed, instead of tracking errors, for updating NNs' weights. This replacement reduces the occurrence of high-frequency oscillations in NNs' approximation. With this improved NNs, a predictorbased NN disturbance observer is then developed for compensation for external disturbances and NNs' approximation errors, and a normalization learning technique is employed for reduction of the number of learning parameters. A predictor-based neural attitude control strategy is proposed for a quadrotor with external disturbances. Furthermore, measurement noise are taken into account in our predictor-based neural attitude control strategy. The Lyapunov-based stability analysis shows that all closed-loop signals in the designed attitude system are semiglobally bounded. A numerical simulation and a hardware-in-loop experiment as well as outdoor flight verify the effectiveness of the proposed anti-disturbance attitude control strategy.

Robust anti-disturbance fault-tolerant control of ship-board platforms with multiplicative actuator faults and unknown disturbances

Ship-board platform installed on ships as a heave compensation device can compensate for roll, pitch and heave motions of ships, ensuring the safe operations of special tasks such as sea launch, offshore installation and drilling configurations. Due to the harshness of the marine environment, the actuator fails may cause significant economic losses and even casualties. In this paper, the robust anti-disturbance fault-tolerant control scheme for the ship-board platform is built on the disturbance observer, the adaptive fault observer with the vectorial backstepping technique. The external disturbance compensation and actuator fault tolerance are realized separately. Specially, a robustifying term with the adaptive law is inserted to address the unmodeled dynamics of nonlinear systems. It is demonstrated with the Lyapunov functions that the robust adaptive anti-disturbance fault-tolerant controller can maintain the ship-board platform’s position and orientation to achieve the desired values and guarantee the global stability of the closed-loop ship-board platform control system. Finally, simulation results demonstrate that the compensation of external disturbances and actuator faults can reach 99.1% under 4 sea state, and the compensation performance is increased by 5.7% compared with the existing robust disturbance rejection control, such that the reliability and safety of the system can be improved effectively.

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

Повышение эффективности грузоперевозок на основе совершенствования механической части электровоза 2ЭС6

The key problems of railways are presented, the cause-and-effect relationships between the dynamic characteristics of a typical scheme of a locomotive axle box suspension, the features of the traction engine suspension and malfunctions of its mechanical components are revealed. The impact of the properties of the spring suspension of the electric locomotive on emergence of defects in rails and wheels is reflected. A brief overview of foundations of the theory of invariance and application of the compensation method of external disturbances to improve the indicators of locomotive dynamic qualities is presented. Mathematical modeling of oscillations of the «locomotive-track» system with standard and upgraded systems of the axle box stage of the spring suspension of the 2ES6 electric locomotive has been performed, which makes it possible to evaluate the effectiveness of the proposed method for improving the dynamic qualities of the locomotive based on application of compensation of external disturbances compared with the standard scheme of the box stage of the 2ES6 electric locomotive. In order to reduce dynamic loads in the body of the traction motor and in the supports of motor axial bearings arising from horizontal oscillations of the electric locomotive, a new technical solution has been proposed which increases the reliability of motor axial bearings, their attachment points and the body of the traction motor.

Compensation of output external disturbances for a class of linear systems with control delay

The paper considers the problem of the output external unknown disturbance compensation under unmeasurable state vector for a class of linear systems with the control channel delay. It is assumed that the disturbance is the output of an autonomous linear generator. A special observer was built to estimate the disturbance. A system with an extended state vector is formed on the base of the observer’s estimates. A controller that provides disturbance compensation is proposed. An algorithm for the output external disturbances compensation for a class of linear systems with input delay is presented. This method does not require identification of disturbance parameters. The performance of the proposed algorithm was confirmed using computer simulation in the MATLAB Simulink software. The developed algorithm can be effectively applied to a class of problems related to rocking compensation in ship systems, control of robotic complexes various kinds, etc.

Compensation of external disturbances for MIMO systems with control delay

The problem of compensation of external disturbing influences for MIMO system with input delay is important and relevant. A solution to this problem is proposed in the problems of dynamic objects control and in a number of others. The proposed method is based on the principle of an internal model and requires the identification of perturbation parameters. At the first stage, a scheme for extracting a disturbance is presented which is represented as a sinusoidal signal with an unknown frequency, amplitude, and phase. At the second stage, the problem of identifying the frequencies of a sinusoidal and multisinusoidal signal is solved. In the last stage, an algorithm for stabilizing the state of the object to zero is developed using feedback. A new scheme for compensating external disturbances for a MIMO system with an input delay is proposed. A new algorithm for identifying the frequencies of a multisinusoidal signal is proposed. The analysis of the possibilities of the proposed estimation method using computer simulation in the MATLAB Simulink environment is carried out. The developed method can be effectively applied to a wide class of applied tasks related to the control of robots and robotic manipulators for various purposes.

Predictor-Based Neural Dynamic Surface Control of a Nontriangular System With Unknown Disturbances

For a class of nontriangular nonlinear systems in presence of unknown disturbances, we propose a predictor-based neural dynamic surface control (PNDSC) strategy in this paper. This nontriangular system is transformed via the mean value theorem, and a predictor is then constructed. To avoid an algebraic loop problem, partial state vectors are employed as input signals of neural networks (NNs) for approximating unknown dynamics, and compensation items are designed to compensate for approximation errors from NNs. Different from the traditional NDSC, the PNDSC in this paper utilizes prediction errors to update learning parameters for improving NNs’ learning behaviors with overlarge adaptive gains. On the basis of improved NNs’ approximation behaviors, a predictor-based NNs disturbance observer (PNNDO) is constructed for compensation for external disturbances and approximation errors from NNs. Furthermore, with predictors, a normalization method of weights is developed to reduce the number of online learning parameters. On the basis of the aforementioned result, measurement noises are taken into account in our predictor-based neural control strategy. We employ predictor states, rather than measurement information paralyzed by noises, in design of our control strategy. This reduces high-frequency oscillations in control input. A Lyapunov-based stability analysis shows that all signals are ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by a numerical example and a permanent magnet brushless DC motor system.

Adaptive RBF -SMC control for multi-point mooring system

A stable adaptive control scheme for multi-point mooring system (MPMS) with uncertain dynamics is proposed in this paper. The control scheme is designed by a hybrid controller based on RBF (Radial Basis Function) NN (Neural Network) and SMC (Sliding Mode Control), which learns the MPMS dynamic changes, and the compensation of external disturbances is realized through adaptive RBFNN control. Meanwhile the RBF-SMC control parameters are adapted by the Lyapunov method to minimize squares dynamic positioning (DP) error. The convergence of the hybrid controller is proved theoretically, and the proposed mooring control scheme is applied to the “Kantan3” mooring simulation system. Finally, the simulation results are compared with the traditional PID controller and standard RBF controller to demonstrate the effective mooring positioning performance of the control scheme for the MPMS.

Neurons in Primary Motor Cortex Encode External Perturbations during an Orientation Reaching Task

When confronting an abrupt external perturbation force during movement, subjects continuously adjust their behaviors to adapt to changes. Such adaptation is of great importance for realizing flexible motor control in varied environments, but the potential cortical neuronal mechanisms behind it have not yet been elucidated. Aiming to reveal potential neural control system compensation for external disturbances, we applied an external orientation perturbation while monkeys performed an orientation reaching task and simultaneously recorded the neural activity in the primary motor cortex (M1). We found that a subpopulation of neurons in the primary motor cortex specially created a time-locked activity in response to a “go” signal in the adaptation phase of the impending orientation perturbation and did not react to a “go” signal under the normal task condition without perturbation. Such neuronal activity was amplified as the alteration was processed and retained in the extinction phase; then, the activity gradually faded out. The increases in activity during the adaptation to the orientation perturbation may prepare the system for the impending response. Our work provides important evidence for understanding how the motor cortex responds to external perturbations and should advance research about the neurophysiological mechanisms underlying motor learning and adaptation.

Synthesis of Active Disturbance Rejection Control

Synthesis of Active Disturbance Rejection Control (ADRC) has been discussed in this work. Two main approaches have been presented for the second-order ADRC: Linear ADRC and nonlinear ADRC. A design procedure for the three main components of ADRC: controller, estimator, and disturbance rejection scheme has been presented in each approach. Parameters of the linear approach are tuned using the desired closed loop system bandwidth. Parameters of the nonlinear approach are selected by categorizing them first into usual parameters and key parameters. After that, the key parameters are optimized using Genetic Algorithm (GA). The two approaches have been tested on a quarter-car system that deals with the passenger comfort problem. Simulation results show a good performance and a good compensation for external disturbances and dynamic uncertainties

A Sliding Mode Control with Nonlinear Fractional Order PID Sliding Surface for the Speed Operation of Surface-Mounted PMSM Drives Based on an Extended State Observer

A novel sliding mode controller (SMC) with nonlinear fractional order PID sliding surface based on a novel extended state observer for the speed operation of a surface-mounted permanent magnet synchronous motor (SPMSM) is proposed in this paper. First, a new smooth and derivable nonlinear function with improved continuity and derivative is designed to replace the traditional nonderivable nonlinear function of the nonlinear state error feedback control law. Then, a nonlinear fractional order PID sliding mode controller is proposed on the basis of the fractional order PID sliding surface with the combination of the novel nonlinear state error feedback control law to improve dynamic performance, static performance, and robustness of the system. Furthermore, a novel extended state observer is designed based on the new nonlinear function to achieve dynamic feedback compensation for external disturbances. Stability of the system is proved based on the Lyapunov stability theorem. The corresponding comparative simulation results demonstrate that the proposed composite control algorithm displays good stability, dynamic properties, and strong robustness against external disturbances.

Adaptive Algorithm of Filtration with Integrated Residuals

This paper proposes a filtering algorithm based on the use of not only the residuals between the measured and estimated coordinates, as in classical filtering algorithms, but also multiple integrals of these residuals. Classical filtering algorithms use reliable information about both the motion and measurement models and the statistical characteristics of the input random disturbances and measurement noise. The real control objects operate under conditions of action not only of highfrequency random disturbances, but also under the influence of low-frequency forces and moments from an aggressive environment, the characteristics of which are known with huge approximations. In this regard, the efficiency of using classical filtering algorithms for real systems is extremely low due to large errors. The algorithm proposed in the paper allows to eliminate these drawbacks by restoring external low-frequency disturbances in real time. Under external disturbances are understood not only external influences from the environment, but inaccurate knowledge about the motion model itself. For integral residuals, an algorithm is proposed for calculating the gains in the feedback in an analytical form. This algorithm is based on the processing of residuals, as well as estimates of external disturbances and their derivatives in the current time. A control algorithm is proposed that includes estimates of both phase coordinates, which are responsible for the quality of transients, and estimates of unknown disturbances, which is responsible for the compensation of external disturbances. Knowing the estimates of external disturbances in real time will, on the one hand, improve the quality of control, and, on the other hand, reduce the time and material costs associated with the study of the control object’s movement dynamics and the external environment. Using the example of an underwater vehicle model described by a linear system of differential equations under conditions of external disturbances (wave and hydrological forces and moments), the simulation was performed and the efficiency of the proposed algorithms for various numbers of integral residuals was shown.

Wind-gust Compensation Algorithm based on High-gain Residual Observer to Control a Quadrotor Aircraft: Real-time Verification Task at Fixed Point

Wind is considered a strong disturbance for quadrotor aircrafts (UAV) when an outdoor task at a fixed point is carried out. The effect of wind produces a distortion on the attitude of the vehicle which is reflected on undesired longitudinal movements. This paper addresses a real-time implementation and design of a robust embedded control-observer based on a type high-gain observer algorithm for on-line estimation and compensation of external disturbances produced by wind gusts on an autonomous quadrotor aircraft. A real-time experimental implementation of embedded Residual High Gain algorithm control is proposed in order to eliminate the effects of real perturbations in the hover position of the UAV. A Lyapunov function was used to practical stability analysis the system. Also numerical simulations were carried out to estimate wind behavior by the use of Drydel mathematical wind model. The main contribution of this work is the implementation of a Residual High Gain Observer in an outdoor real-time experiment in presence of real wind gusts perturbations. The proposed embedded algorithm control improves the stabilization of an UAV in the presence of real wind gusts with average of 8 m/s. The proposed algorithm improved the UAV behavior as shown by the GPS position experimental results, decreasing the wind effect on the translational movement of the aircraft.

Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft

Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8 km parachute deployment point distribution error) than the baseline control scheme (8.4 km) and integral control scheme (5.8 km).

Methodology for studying impulse disturbance from rail joints to the railway vehicle and assessment of the efficiency of the new spring suspension

The methodology for studying the impulse disturbance of the railway track joints on the indicators of the dynamic qualities of the railway vehicle has been developed. The dependence of the impulse repetition factor on the energy dissipation level in the system and the speed of the vehicle is obtained. A comparative assessment of the dynamic qualities of a freight car with a typical scheme of spring suspension and a car with suspension based on the principle of compensation of external disturbances is performed. It has been established that the spring suspension of a freight car based on the principle of compensation of external disturbances delivers to it significantly better indicators of dynamic qualities in comparison with car equipped with a new three-piece truck with a typical scheme of springs. Vertical acceleration of the car’s body with a new scheme of vibration protection and dynamic forces in spring suspension is several times less than for a car with a typical three-piece truck structure.

Extended state observer–based fractional order proportional–integral–derivative controller for a novel electro-hydraulic servo system with iso-actuation balancing and positioning

Aiming at balancing and positioning of a new electro-hydraulic servo system with iso-actuation configuration, an extended state observer–based fractional order proportional–integral–derivative controller is proposed in this study. To meet the lightweight requirements of heavy barrel weapons with large diameters, an electro-hydraulic servo system with a three-chamber hydraulic cylinder is especially designed. In the electro-hydraulic servo system, the balance chamber of the hydraulic cylinder is used to realize active balancing of the unbalanced forces, while the driving chambers consisting of the upper and lower chambers are adopted for barrel positioning and dynamic compensation of external disturbances. Compared with conventional proportional–integral–derivative controllers, the fractional order proportional–integral–derivative possesses another two adjustable parameters by expanding integer order to arbitrary order calculus, resulting in more flexibility and stronger robustness of the control system. To better compensate for strong external disturbances and system nonlinearities, the extended state observer strategy is further introduced to the fractional order proportional–integral–derivative control system. Numerical simulation and bench test indicate that the extended state observer–based fractional order proportional–integral–derivative significantly outperforms proportional–integral–derivative and fractional order proportional–integral–derivative control systems with better control accuracy and higher system robustness, well demonstrating the feasibility and effectiveness of the proposed extended state observer–based fractional order proportional–integral–derivative control strategy.

A nonregressor nonlinear disturbance observer-based adaptive control scheme for an underwater manipulator

A new nonlinear disturbance observer-based tracking control scheme for an underwater manipulator is presented in this paper. This observer overcomes the disadvantages of existing disturbance observers, which are designed or analyzed by the linear system techniques. It can be applied in underwater manipulator systems for various purposes such as payload compensation, interaction effects compensation, underwater current or external disturbance compensation, and independent system control. The performance of the proposed tracking control scheme is demonstrated numerically by the payload compensation and interaction effects compensation for a two degrees of freedom vertical underwater manipulator.

Indirect adaptive control of an autonomous underwater vehicle-manipulator system for underwater manipulation tasks

This paper presents an indirect adaptive control method for an autonomous underwater vehicle-manipulator system (UVMS) based on an extended Kalman filter (EKF). This method overcomes the disadvantages of existing disturbance observers and direct adaptive control schemes, which are based on linear system techniques and regressor-based techniques. The proposed control scheme can be applied to UVMSs for various purposes such as payload compensation, interaction effects compensation, underwater current or external disturbance compensation, reaction compensation, and independent system control. The performance of the proposed controller was demonstrated numerically by payload compensation, where it compensated for the reaction effects experienced during a manipulation task, and disturbance (underwater current) compensation in a UVMS with a six degrees of freedom (DOF) underwater vehicle and a 3-DOF underwater manipulator.

Induction motor IFOC based speed-controlled drive with asymptotic disturbance compensation

This paper presents the design of digitally controlled speed electrical drive, with the asymptotic compensation of external disturbances, implemented by using the IFOC (Indirect Field Oriented Control) torque controlled induction motor. The asymptotic disturbance compensation is achieved by using the DOB (Disturbance Observer) with the IMP (Internal Model Principle). When compared to the existing IMP-based DOB solutions, in this paper the robust stability and disturbance compensation are improved by implementing the minimal order DOB filter. Also, the IMP-based DOB design is improved by employing the asymptotic compensation of all elemental or more complex external disturbances. The dynamic model of the IFOC torque electrical drive is, also, included in the speed-controller and DOB section design. The simulation and experimental measurements presented in the paper illustrate the effectiveness and robustness of the proposed control scheme.

Results on the Robust Observer-based Position Controller for Parallel Kinematic Machines

The problem of state observation and position control by output feedback for a nonlinear three degrees-of-freedom (3-DOF) parallel kinematic machine (PKM) system is considered, based on the limited signal availability (only the moving platform displacement measurements are assumed available). Unknown velocity signals are estimated via a nonlinear robust observer that is designed for the nonlinear system with observable linear dynamics part and bounded nonlinearities and disturbances, and that guarantees global exponential stability of the observation error. A proportional-derivative (PD) controller is designed to solve the position control problem, utilizing the estimated velocity, as well as the gravitation compensation, dynamic friction and external disturbance compensation for the PKM. The closed-loop system is proven to have global asymptotical stability according to the Lyapunov analysis method and LaSalle's invariance principle. Performance of the resulting observer and controller is illustrated in a simulation study of a 3-DOF PKM. Modifications to the nonlinear observer and control law are discussed, that assure convergence of the position error and state observation error to zero when the upper bounds on the model uncertainties/disturbances are not known a priori.