Nonlinear Model Uncertainties Research Articles

Current-Loop Control for the Pitching Axis of Aerial Cameras via an Improved ADRC

An improved active disturbance rejection controller (ADRC) is designed to eliminate the influences of the current-loop for the pitching axis control system of an aerial camera. The improved ADRC is composed of a tracking differentiator (TD), an improved extended state observer (ESO), an improved nonlinear state error feedback (NLSEF), and a disturbance compensation device (DCD). The TD is used to arrange transient process. The improved ESO is utilized to observe the state extended by nonlinear dynamics, model uncertainty, and external disturbances. Overtime variation of the current-loop can be predicted by the improved ESO. The improved NLSEF is adopted to restrain the residual errors of the current-loop. The DCD is used to compensate the overtime variation of the current-loop in real time. The improved ADRC is designed based on a new nonlinear functionnewfal(·). This function exhibits enhanced continuity and smoothness compared to previously available nonlinear functions. Thus, the new nonlinear function can effectively decrease the high-frequency flutter phenomenon. The improved ADRC exhibits improved control performance, and disturbances of the current-loop can be eliminated by the improved ADRC. Finally, simulation experiments are performed. Results show that the improved ADRC displayed better performance than the proportional integral (PI) control strategy and traditional ADRC.

A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery.

It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion. A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis function (RBF) neural network and a fuzzy system, respectively. Simulations and experiments were performed on a 3 degree-of-freedom (DOF) minimally invasive surgical robot. The results demonstrate that the FNNSMC is effective and can suppress vibrations at the surgical instrument tip. The proposed FNNSMC can provide a robust performance and suppress the vibrations at the surgical instrument tip, which can enhance the quality and security of surgical procedures.

Model Reference Adaptive Control Design for Slow Processes. A Case Study on Level Process Control

The design of controllers, using conventional techniques, for plants with nonlinear dynamics or model uncertainties can be often quite difficult. Model reference adaptive control (MRAC) is a modern alternative for classic control algorithms and a convenient method to updating the controller's parameters for slow processes with parameter variations. In this paper is presented a comparative study of two design procedures of MRAC for first order slow processes, particularly applied and tested on level process control. The first method it is based on the MIT rule and the second one it is based on the stability theory of Lyapunov. The theoretical results are obtained using Matlab simulation environment.

The composite hierarchical control of multi-link multi-DOF space manipulator based on UDE and improved sliding mode control

The problem of manipulating objects cooperatively for multi-link multi-degree of freedom space manipulator is very challenging because of the multisource disturbances, including nonlinear coupling, model uncertainties, and external disturbances. To solve this issue, a composite hierarchical control strategy is proposed, which has two layers. In inner layer, uncertainty and disturbance estimator (UDE) is employed to estimate the composite uncertainty that comprises the effect of multisource disturbances and compensate for it, producing the decoupled system. Furthermore, taking the error in UDE estimation into consideration, the chattering–eliminating sliding mode control is designed to suppress it in the outer layer. However, the obtained controller requires the measurement of joint velocities apart from joint positions. To address this issue, a robust velocity observer that employs the UDE-estimated uncertainty is proposed. The notable feature of the proposed design is that it requires neither accurate plant model nor any information about the uncertainty. Also, the design requires only joint position measurements for its implementation. Finally, to demonstrate the effectiveness of the composite hierarchical controller, the simulations of a planar dual-arm manipulator system and the comparisons of the proposed method with the other existing designs are presented.

Robust global sliding model control for water-hull-propulsion unit interaction systems - Part 1: System boundary identification

Unexpected severe hull deformation caused by the wave loads would significantly influence the dynamical behaviours of the propulsion system in large scale ships, resulting in degradation of the ship control performance. A new global sliding model control (GSMC) for marine water-hull-propulsion unit systems is proposed to obtain more accurate control performance in this paper. The GSMC was firstly employed to establish the marine propulsion control model with nonlinear uncertainties. In the GSMC model, the saturation function method is applied to eliminate chattering on the sliding surface. Then the Lyapunov stability criterion is adopted to confirm the stability of the control system. Following, for the first time, the boundary problem of the nonlinear model uncertainties were investigated quantitatively. The bounded nonlinear model uncertainties required in the proposed GSMC model, involving engine torque loss / variations, power transfer for various load conditions and shaft rotational speeds, were derived based on the experiments carried out on a marine shaft-line test-bed of the integrated propulsion system as well as a sea trial implemented for a running bulk carrier. An upper boundary of 1,85 % for the model uncertainty has been obtained, which would be introduced into the GSMC for the integrated marine propulsion system to derive the total control law realising the robust control of the system

Robust global sliding model control for water-hull-propulsion unit interaction systems - Part 2: Model validation

Unexpected severe hull deformation caused by wave loads poses alignment problem to the propulsion shaft line in large scale ships, which would significantly influence the dynamical performance of the marine propulsion system. How to suppress negative disturbance imposed by the interaction between water-hull-propulsion and ensure the normal operation of the marine propulsion system is a challenging task. To address this issue, a new global sliding model control (GSMC) for marine water-hull-propulsion unit systems is proposed and investigated to obtain more accurate control performance in a series of researches. In Part 1 the GSMC controller has been developed and the bounded nonlinear model uncertainties have been derived based on the experiments and sea trial. In this work the upper boundary of 1,85 % was introduced into the GSMC controller to derive the total control law realising the robust control of the marine propulsion system. Numerical simulations based on the real bulk carrier parameters show a high effectiveness of the GSMC for speed tracking, compared with the traditional sliding model controller and Proportional Integral Derivative (PID) controller. By the proposed and investigated control system in this paper may be developed a simple practical-effective robust control strategy for marine propulsion systems subject to some complex unknown uncertainties through further investigations, validations and modifications.

Intelligent Control System Design of a Unmanned Quadrotor Robot

This paper presents the position and attitude control of an unmanned aerial vehicle known as a quadrotor. A design methodology is introduced that a fuzzy logic controller in an intelligent way. It offers several advantages over certain types of conventional control methods, specifically in dealing with highly nonlinear systems and modeling uncertainties. The proposed controller was located at a position feedback loop which composed of x,y and z. Plus, it also designed at the angle acceleration loop. Simulation results prove the efficiency of this intelligent control strategy.

Two-Wheeled Self-Balancing Robot Systems Using Fuzzy Immune Algorithm

For two-wheeled self-balancing robot nonlinear model uncertainties and other characteristics, the design of fuzzy immune PD controller and a simulation study. The simulation results show that the control system than the conventional fuzzy PD controller with a small overshoot, adjust the time is short, anti-interference ability, etc., applicable to the mathematical model and the parameter change is difficult to determine two-wheeled self-balancing robot control system.

Constrained Dynamic Systems Estimation Based on Adaptive Particle Filter

For the state estimation problem, Bayesian approach provides the most general formulation. However, most existing Bayesian estimators for dynamic systems do not take constraints into account, or rely on specific approximations. Such approximations and ignorance of constraints may reduce the accuracy of estimation. In this paper, a new methodology for the states estimation of constrained systems with nonlinear model and non-Gaussian uncertainty which are commonly encountered in practice is proposed in the framework of particles filter. The main feature of this method is that constrained problems are handled well by a sample size test and two particles handling strategies. Simulation results show that the proposed method can outperform particles filter and other two existing algorithms in terms of accuracy and computational time.

Active Disturbance Rejection Control for Precise Position Tracking of Ionic Polymer–Metal Composite Actuators

Active disturbance rejection control method that can actively compensate nonlinear model uncertainties and unpredictable external disturbances is proposed for an accurate position tracking of an ionic polymer-metal composite actuator. An empirical model of the electroactive polymer actuator is constructed by fitting the step response of the actuator. Experimental results show that the present active disturbance rejection control scheme can substantially improve the control performance in the tracking of various reference motions including step, sinusoidal, trapezoidal, and sawtooth wave profiles. The proposed scheme offers an innovative solution for the position tracking of an ionic polymer-metal composite actuator with highly nonlinear dynamics.

Concept design, modeling and station-keeping attitude control of an earth observation platform

The stratosphere airship provides a unique and promising platform for earth observation. Researches on the project design and control scheme for earth observation platforms are still rarely documented. Nonlinear dynamics, model uncertainties, and external disturbances contribute to the difficulty in maneuvering the stratosphere airship. A key technical challenge for the earth observation platform is station keeping, or the ability to remain fixed over a geo-location. This paper investigates the conceptual design, modeling and station-keeping attitude control of the near-space earth observation platform. A conceptual design of the earth observation platform is presented. The dynamics model of the platform is derived from the Newton-Euler formulation, and the station-keeping control system of the platform is formulated. The station-keeping attitude control approach for the platform is proposed. The multi-input multi-output nonlinear control system is decoupled into three single-input single-output linear subsystems via feedback linearization, the attitude controller design is carried out on the new linear systems using terminal sliding mode control, and the global stability of the closed-loop system is proven by using the Lyapunov theorem. The performance of the designed control system is simulated by using the variable step Runge-Kutta integrator. Simulation results show that the control system tracks the commanded attitude with an error of zero, which verify the effectiveness and robustness of the designed control system in the presence of parametric uncertainties. The near-space earth observation platform has several advantages over satellites, such as high resolution, fast to deploy, and convenient to retrieve, and the proposed control scheme provides an effective approach for station-keeping attitude control of the earth observation platform.

Design and simulation of fault diagnosis based on NUIO/LMI for satellite attitude control systems

This paper presents a scheme of fault diagnosis for flexible satellites during orbit maneuver. The main contribution of the paper is related to the design of the nonlinear input observer which can avoid false alarm arising from the disturbance from orbit control force. The effects of orbit control force on the fault diagnosis system for satellite attitude control systems, including the disturbing torque caused by the misalignments and the model uncertainty caused by the fuel consumed, are discussed, where standard Luenberger observer cannot work well. Then the nonlinear unknown input observer is proposed to decouple faults from disturbance. Besides, a linear matrix inequality approach is adopted to reduce the effect of nonlinear part and model uncertainties on the observer. The numerical and semi-physical simulation demonstrates the effectiveness of the proposed observer for the fault diagnosis system of the satellite during orbit maneuver.

Mass conservative three‐dimensional water tracer distribution from Markov chain Monte Carlo inversion of time‐lapse ground‐penetrating radar data

Time‐lapse geophysical measurements are widely used to monitor the movement of water and solutes through the subsurface. Yet commonly used deterministic least squares inversions typically suffer from relatively poor mass recovery, spread overestimation, and limited ability to appropriately estimate nonlinear model uncertainty. We describe herein a novel inversion methodology designed to reconstruct the three‐dimensional distribution of a tracer anomaly from geophysical data and provide consistent uncertainty estimates using Markov chain Monte Carlo simulation. Posterior sampling is made tractable by using a lower‐dimensional model space related both to the Legendre moments of the plume and to predefined morphological constraints. Benchmark results using cross‐hole ground‐penetrating radar travel times measurements during two synthetic water tracer application experiments involving increasingly complex plume geometries show that the proposed method not only conserves mass but also provides better estimates of plume morphology and posterior model uncertainty than deterministic inversion results.

A novel method for non-linear control of wheel slip in anti-lock braking systems

Anti-lock braking system (ABS) provides active safety for vehicles during braking by regulation of the wheel slip at its optimum value. Due to the non-linear characteristics and model uncertainties in vehicle dynamics, a non-linear controller with increased robustness should be designed for ABS. In this paper, to achieve this aim, an optimization-based braking torque control law is developed for ABS using the prediction of the wheel slip response from a continuous non-linear vehicle dynamics model. To increase the robustness of the controller, the integral feedback technique is appended to the design method. The derived control law and its special cases are evaluated and discussed. At the end, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of braking on dry and slippery roads. The simulation results indicate that, the wheel slip tracking error is remarkably decreased by the proposed controller. Moreover, the achieved control input is entirely smooth and suitable for implementation.

Constrained Bayesian state estimation – A comparative study and a new particle filter based approach

This paper investigates constrained Bayesian state estimation problems by using a Particle Filter (PF) approach. Constrained systems with nonlinear model and non-Gaussian uncertainty are commonly encountered in practice. However, most of the existing Bayesian methods are unable to take constraints into account and require some simplifications. In this paper, a novel constrained PF algorithm based on acceptance/rejection and optimization strategies is proposed. The proposed method retains the ability of PF in nonlinear and non-Gaussian state estimation, while take advantage of optimization techniques in constraints handling. The performance of the proposed method is compared with other accepted Bayesian estimators. Extensive simulation results from three examples show the efficacy of the proposed method in constraints handling and its robustness against poor prior information.

Neuro-Sliding-Mode Control of Flexible-Link Manipulators Based on Singularly Perturbed Model

A neuro-sliding-mode control (NSMC) strategy was developed to handle the complex nonlinear dynamics and model uncertainties of flexible-link manipulators. A composite controller was designed based on a singularly perturbed model of flexible-link manipulators when the rigid motion and flexible motion are decoupled. The NSMC is employed to control the slow subsystem to track a desired trajectory with a traditional sliding mode controller to stabilize the fast subsystem which represents the link vibrations. A stability analysis of the flexible modes is also given. Simulations confirm that the NSMC performs better than the traditional sliding-mode control for controlling flexible-link manipulators. The control strategy not only gives good tracking performance for the joint angle, but also effectively suppresses endpoint vibrations. The simulations also show that the control strategy has a strong self-adaptive ability for controlling manipulators with different parameters.

A Benchmark Problem for Robust Feedback Control of a Flexible Manipulator

A benchmark problem for robust feedback control of a flexible manipulator is presented. The system to be controlled is a four-mass system subject to input saturation, nonlinear gear elasticity, model uncertainties, and load disturbances affecting both the motor and the arm. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. Four suggested solutions are presented, and even though the solutions are based on different design methods, they give comparable results.

Wind turbines: New challenges and advanced control solutions

Wind turbines: New challenges and advanced control solutions

THE DESIGN OF SLIDING MODE CONTROL SYSTEM BASED ON BACKSTEPPING THEORY FOR BTT UAV

A novel control system design approach is proposed based on backstepping theory and sliding mode control (SMC) for BankTo-Turn (BTT) Unmanned Aerial Vehicle (UAV). It ensures BTT UAV stable and accurate flight under large parametric perturbation. In addition, the aerodynamic coefficients are not necessary to be identified online. This approach is based on backstepping theory and the whole system is not divided into slow and fast subsystems. However, backstepping cannot ensure the robustness of the closed-loop system. To solve this problem, SMC is employed, which is designed in terms of the bounds of aerodynamic coefficients. To evaluate the performance of the flight control system using the proposed approach, the three channels united simulation is conducted considering the actuators' rate and magnitude saturation. The results show that the proposed approach is capable of handling serious nonlinear and large model uncertainty.

Robust Fault-Tolerant Control for a Biped Robot Using a Recurrent Cerebellar Model Articulation Controller

A design technique of a recurrent cerebellar model articulation controller (RCMAC)-based fault-tolerant control (FTC) system is investigated to rectify the nonlinear faults of a biped robot. The proposed RCMAC-based FTC (RCFTC) scheme contains two components: 1) an online fault estimation module based on an RCMAC is used to provide approximation information for any nonnominal behavior due to the system failure and modeling error of the biped robot; and 2) a controller module consisting of a computed torque controller and a robust FTC is utilized to achieve FTC. In the controller module, the computed torque controller reveals a basic stabilizing controller to stabilize the system, and the robust FTC is utilized to compensate for the effects of the system failure so as to achieve fault accommodation. The adaptive laws of the RCFTC system are rigorously established based on the Lyapunov function, so that the stability of the system can be guaranteed. Finally, two simulation cases of a biped robot are presented to illustrate the effectiveness of the proposed design method. Simulation results show that the RCFTC system can effectively recover the control performance for the system in the presence of the nonlinear faults and modeling uncertainties.