Friction Uncertainties Research Articles

A neurofuzzy controller for active front steering system of vehicle under road friction uncertainties

In this study, a neurofuzzy controller is proposed to improve vehicle handling in different road friction coefficients. This controller adapts itself to neutralize the effects of unpredictable changes in road friction coefficient on vehicle handling. This adaptive neurofuzzy controller can improve vehicle handling, manoeuvrability and path tracking. First a proportional-integral-derivative (PID) controller is proposed and tuned by using PSO (particle swarm optimization). Then, this tuned PID controller is applied to the vehicle system and training data is gathered. The next step is to train a fuzzy controller by importing thistraining data tothe ANFIS (adaptive neurofuzzy inference system) toolbox of MATLAB software. Then the trained fuzzy controller is applied to a vehicle that exploits AFS (active front steering system). This controller is able to adapt itself during manoeuvres, by using back propagation of error as a learning algorithm. Results show that neurofuzzy controller can improve handling of the vehicle in different road conditions, because neurofuzzy conteroller can adapt itself in unpredictable situations.

Probabilistic Analysis of Tunnel Face Stability below River Using Bayesian Framework

A key issue in assessment on tunnel face stability is a reliable evaluation of required support pressure on the tunnel face and its variations during tunnel excavation. In this paper, a Bayesian framework involving Markov Chain Monte Carlo (MCMC) simulation is implemented to estimate the uncertainties of limit support pressure. The probabilistic analysis for the three-dimensional face stability of tunnel below river is presented. The friction angle and cohesion are considered as random variables. The uncertainties of friction angle and cohesion and their effects on tunnel face stability prediction are evaluated using the Bayesian method. The three-dimensional model of tunnel face stability below river is based on the limit equilibrium theory and is adopted for the probabilistic analysis. The results show that the posterior uncertainty bounds of friction angle and cohesion are much narrower than the prior ones, implying that the reduction of uncertainty in cohesion and friction significantly reduces the uncertainty of limit support pressure. The uncertainty encompassed in strength parameters are greatly reduced by the MCMC simulation. By conducting uncertainty analysis, MCMC simulation exhibits powerful capability for improving the reliability and accuracy of computational time and calculations.

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

AbstractIn this paper, an adaptive chattering free neural network‐based sliding mode control (ACFN‐SMC) method is proposed for tracking trajectories of redundant parallel manipulators. ACFN‐SMC combines adaptive chattering free radial basis function neural networks (RBFN), sliding mode control with online updating the robust term parameters, and a nonlinear compensation item for reducing tracking errors. The stability of the closed‐loop system with modeling uncertainties, frictional uncertainties, and external disturbances is ensured by using the Lyapunov method. The proposed controller has a simple structure and little computation time while securing dynamic performance with expected quality in tracking trajectories of redundant parallel manipulators. In addition, the ACFN‐SMC strategy does not need to know the upper bound of any uncertainties. From the simulation results, it is evident that the proposed control strategy not only has significantly higher robustness capability for uncertainties but also can achieve better chattering elimination when compared with those using existing intelligent control schemes.

Actuator stiction compensation via variable amplitude pulses

A novel model free stiction compensation scheme is developed which eliminates the oscillations and also reduces valve movement, allowing good setpoint tracking and disturbance rejection. Pulses with varying amplitude are added to the controller output to overcome stiction and when the error becomes smaller than a specified limit, the compensation ceases and remains in a standby mode. The compensation re-starts as soon as the error exceeds the user specified threshold. The ability to cope with uncertainty in friction is a feature achieved by the use of pulses of varying amplitude. The algorithm has been evaluated via simulation and by application on an industrial DCS system interfaced to a pilot scale process with features identical to those found in industry including a valve positioner.

Electropneumatic actuator position control using second order sliding mode

Pneumatic actuators are quite difficult to control in an accurate way. Indeed their dynamics are generally described by an uncertain nonlinear system. The uncertainties are caused by friction, (external) perturbations and parametric uncertainties due to a very tedious identification process. This paper deals with the position control problem of an electropneumatic actuator using sliding mode control.

Discharge estimation under uncertainty using variational methods with application to the full Saint‐Venant hydraulic network model

SummaryEstimating river discharge from in situ and/or remote sensing data is a key issue for evaluation of water balance at local and global scales and for water management. Variational data assimilation (DA) is a powerful approach used in operational weather and ocean forecasting, which can also be used in this context. A distinctive feature of the river discharge estimation problem is the likely presence of significant uncertainty in principal parameters of a hydraulic model, such as bathymetry and friction, which have to be included into the control vector alongside the discharge. However, the conventional variational DA method being used for solving such extended problems often fails. This happens because the control vector iterates (i.e., approximations arising in the course of minimization) result into hydraulic states not supported by the model. In this paper, we suggest a novel version of the variational DA method specially designed for solving estimation‐under‐uncertainty problems, which is based on the ideas of iterative regularization.The method is implemented with SIC2, which is a full Saint‐Venant based 1D‐network model. The SIC2 software is widely used by research, consultant and industrial communities for modeling river, irrigation canal, and drainage network behavior. The adjoint model required for variational DA is obtained by means of automatic differentiation. This is likely to be the first stable consistent adjoint of the 1D‐network model of a commercial status in existence.The DA problems considered in this paper are offtake/tributary estimation under uncertainty in the cross‐device parameters and inflow discharge estimation under uncertainty in the bathymetry defining parameters and the friction coefficient. Numerical tests have been designed to understand identifiability of discharge given uncertainty in bathymetry and friction. The developed methodology, and software seems useful in the context of the future Surface Water and Ocean Topography satellite mission. Copyright © 2016 John Wiley & Sons, Ltd.

Uncertainty analysis of deep drawing using surrogate model based probabilistic method

Deep drawing is an important manufacturing process in industry. In order to obtain high-quality products produced by deep drawing, the set of design variables used in forming operation is designed through deterministic optimization. However, in real forming process, the design variables show variability and randomness which will affect the product quality. These uncertainties are an inherent characteristic of nature and cannot be avoided. This paper focuses on uncertainty analysis of deep drawing with the consideration of uncertainties in material parameters and friction. An uncertainty analysis approach which combines the finite element method (FEM) simulation, surrogate modeling, and Monte Carlo simulation (MCS) is presented in this work. The constructed surrogate models are validated and compared by cross validation and error measures. Then Monte Carlos Simulation is conducted by the use of the constructed surrogate model. The surrogate model based probabilistic method used in this paper is an approach with high-efficiency and sufficient accuracy for uncertainty analysis in deep drawing.

Reducing wear of sticky pneumatic control valves using compensation pulses with variable amplitude

The presence of friction in control valves produces limit cycles that reduces the performance of control loops. Once friction is detected, its compensation avoids process stops until the next maintenance stop is performed. Model free methods adding feed-forward pulses to PID controller output have been proposed to reduce the effect of friction. A common drawback in such methods is the increase in the movement of the valve stem. A new method is here proposed to overcome this issue. The amplitude of the pulses vary and becomes zero when a specified limit for the error on the process variable is achieved. Another advantage of this method is its ability to cope with uncertainty in friction, since the amplitude of the pulses pulses vary between limits. The method is illustrated via simulation, and its implementation in a real industrial controller is shown and discussed.

Robust velocity control for an A-shaped micro-robot with stick-slip locomotion

In this paper, the problem of velocity control of a micro-robot’s locomotion with nanometric resolution has been investigated. A sliding, A-shaped micro-robot, used in precision positioning applications is analyzed. This micro-robot is actuated by means of a piezoelectric stack actuator in order to produce translational and periodic motion. A dynamic model of the robot is proposed assuming linear behavior for the piezoelectric stack and Coulomb friction model. Then, in order to control the velocity of micro-robot, first a robust sliding mode control is used so that the relative angle between the legs in the micro-robot tracks a periodic reference signal. The velocity tracking for the micro-robot is achieved using an amplitude modulation strategy by adapting the amplitude of this reference signal. Velocity control of locomotion is assumed to be in the presence of a non-separation constraint (between the legs and the substrate) and friction uncertainties. Also, a state observer is designed to estimate the rate of change of the relative angle between the legs of the micro-robot, which is needed in the velocity control algorithm. Finally, simulation results are presented which illustrate the effectiveness of the proposed control strategy.

GS07 多層ベローズの挙動評価に関する考察

Multi-ply bellows is widely used because its flexibility and pressure tightness. However, the analytical treatment of its behaviors is difficult compared with that of single-ply bellows, since the uncertainty of friction between plies exists. In this paper the strength evaluation equations of multi-ply bellows are examined based on EJMA Standard and the friction effect between plies is investigated through FEM analyses including material plasticity.

Fine attitude maneuvering by reaction wheel via sliding mode speed driver

The objective of this paper was to design a set of proper speed controllers for reaction wheels in the presence of unknown coulomb friction and parametric uncertainties. The controller goal was to change the rotational speed of reaction wheels to adjust the satellite to a desired attitude. The mathematical model of a satellite that utilizes three reaction wheels as actuators was developed by angular kinematics and kinetic equations. Variable-structure control theory was then applied on the reaction wheel via a real-time microcontroller-based Hardware in the Loop. The simulated satellite response to wheel speed shows that the sliding mode speed controller is effective, and a small attitude angle could be tracked more effectively than with a Proportional–Integral–Derivative speed controller.

MCS-based probabilistic design of embedded sheet pile walls

A probabilistic approach to the design of embedded sheet pile walls is developed in this paper. The approach is based on Monte Carlo simulation (MCS), and it is used to investigate the performance of the partial factors and different design approaches in Eurocode 7 in achieving the target degrees of reliability. The approach is illustrated through an embedded sheet pile wall design example that has been used in literature for the evaluation of Eurocode 7. The approach deals rationally with the correlated load and resistance, and it bypasses a difficult but frequently asked question in Eurocode 7 (i.e. should the passive earth pressure be considered as a load (i.e. action) or a resistance?). The probabilistic design approach (DA) is also used to explore the effects of the soil unit weight variability and uncertainties in over-digging depth and wall friction. The effects of uncertainties in over-digging depth and wall friction are found to be significant. It is also found that, although the soil unit weight variability is generally minor, its effect on the design of embedded sheet pile walls is significant and should be properly accounted for in the design. The MCS-based probabilistic DA proposed in this study provides a straightforward way for proper consideration of such variability with relative ease.

Slid Mode VSC for Aircraft Anti-Skid Braking System with Index Reaching Law

Aircraft anti-skid braking system providing protection for the safety of the aircraft landed by controlling the brake pressure to be maintained slip ratio in best condition. The aircraft anti-skid braking system is hard to control as the nonlinear model of the aircraft dynamics, the uncertainty of friction between the tires and the ground of braking process. With the study of slip ratio and research of aircraft anti-skid braking system dynamics model, the sliding mode variable structure controller is designed. Then index reaching law is adopted to eliminate the system chattering and the performance is analyzed, further more the robustness is strengthen. Simulation results indicate that: the slip ratio follows the optimum slip ratio, the input signal is smooth, achieve the purpose.

Bench tests on friction compensation enhanced H<SUB align="right">∞ control for MIMO magnetic levitation system

Due to uncertainties To solve the problem experiment is performed and Stribeck curve of LuGre model is fit by genetic algorithm. The system is compensated for friction and decoupled. A new H∞ controller regarding friction uncertainties as system’s input disturbance is designed to improve the control performance of this non-linear uncertain system. The effectiveness of the proposed controller is verified by practical bench tests.

Chattering-Free Neuro-Sliding Mode Control of 2-DOF Planar Parallel Manipulators

This paper proposes a novel chattering free neuro-sliding mode controller for the trajectory tracking control of two degrees of freedom (DOF) parallel manipulators which have a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. A feedforward neural network (NN) is combined with an error estimator to completely compensate the large nonlinear uncertainties and external disturbances of the parallel manipulators. The online weight tuning algorithms of the NN and the structure of the error estimator are derived with the strict theoretical stability proof of the Lyapunov theorem. The upper bound of uncertainties and the upper bound of the approximation errors are not required to be known in advance in order to guarantee the stability of the closed-loop system. The example simulation results show the effectiveness of the proposed control strategy for the tracking control of a 2-DOF parallel manipulator. It results in its being chattering-free, very small tracking errors and its robustness against uncertainties and external disturbances.

Modeling and Adaptive Control of an Omni-Mecanum-Wheeled Robot

The complete dynamics model of a four-Mecanum-wheeled robot considering mass eccentricity and friction uncertainty is derived using the Lagrange’s equation. Then based on the dynamics model, a nonlinear stable adaptive control law is derived using the backstepping method via Lyapunov stability theory. In order to compensate for the model uncertainty, a nonlinear damping term is included in the control law, and the parameter update law with σ-modification is considered for the uncertainty estimation. Computer simulations are conducted to illustrate the suggested control approach.

Bathymetric error estimation for the calibration and validation of estuarine hydrodynamic models

This paper investigates the impact of uncertainty in the definition of bathymetry on the performance of estuarine shallow water models. A depth-averaged hydrodynamic model is used to simulate tidal flows in a meso-tidal estuary. Simulations are performed for a range of bathymetric and bed roughness scenarios, which encompass the uncertainties typically encountered in the specification of estuarine and shallow coastal models. The relative sensitivity of model output to uncertainty in bathymetry and bed friction is analysed using Monte Carlo methods, and a new generalised error model is presented that allows various sources of error in bathymetry to be treated as a calibration parameter. Numerical results are compared with observed current speed and water level time series at multiple locations. It is shown that calibration that incorporates bathymetric uncertainty can be significantly more efficient than a classic calibration based only on adjustment of a bed friction coefficient. These findings have wide-ranging implications for the calibration and validation of estuarine and shallow coastal hydrodynamic models. Importantly, our proposed bathymetry calibration framework offers the prospect of significantly improved performance from the current generation of numerical models.

Searching for Good Policies

Policymaking is hard. Policymakers typically have imperfect information about which policies produce which outcomes, and they are left with little choice but to fumble their way through the policy space via a trial-and-error process. This raises a question at the heart of democracy: Do democratic systems identify good policies? To answer this question I introduce a novel model of policymaking in complex environments. I show that good policies are often but not always found and I identify the possibility of policymaking getting stuck at outcomes that are arbitrarily bad. Notably, policy stickiness occurs in the model even in the absence of institutional constraints. This raises the question of how institutions and the political environment impact experimentation and learning. I show how a simple political friction—uncertainty over voter preferences—interacts with political competition and policy uncertainty in a subtle way that, surprisingly, improves the quality of policymaking over time.

NPID and Adaptive Approximation Control of Motion Systems With Friction

This paper proposes a scheme for precise position control of a mechatronic servo system based on linear synchronous motors. The control is based on gain-scheduled proportional integral derivatives (PIDs) (known as NPID in related literature) and adaptive approximation of uncertainties. NPID and adaptive approximation are combined in a constructive way to inherit advantages and overcome limitations of the single methods. In particular, NPID is used to stabilize the nominal plant, and its gains are scheduled so as to minimize the effects of friction and other uncertainties. Adaptive approximation is used to compensate further effects of nonlinearities and obtain a better overall tracking accuracy. A report on an extensive experimental investigation is provided to illustrate the practical advantages of the proposed scheme.

Design of neural-fuzzy-based controller for two autonomously driven wheeled robot

An intelligent control architecture for two autonomously driven wheeled robot is developed in this paper. Consider the parametric variation, external load disturbance, nonlinear friction, unpredicted and unstructured uncertainties for the practical applications, the transient and unmodelled uncertainty will be occurred. In the proposed control scheme, the fuzzy inference is designated as a main controller and the neural network is an auxiliary part. In the fuzzy controller, the translation width and total sliding surface are adopted to reduce the chattering phenomena. The neural uncertainty observer is added in the balance, speed and synchronous controllers to reduce the accumulated error and ascend the stability. The hardware includes a microcontroller, gyroscope, accelerometer, and two autonomous motors, etc. The effectiveness is verified by simulation and experimental results, and the result is compared with conventional PD control scheme for the same robot.