Pressure Regulation for Earth Pressure Balance Control on Shield Tunneling Machine by Using Adaptive Robust Control

Most current studies about shield tunneling machine focus on the construction safety and tunnel structure stability during the excavation. Behaviors of the machine itself are also studied, like some tracking control of the machine. Yet, few works concern about the hydraulic components, especially the pressure and flow rate regulation components. This research focuses on pressure control strategies by using proportional pressure relief valve, which is widely applied on typical shield tunneling machines. Modeling of a commercial pressure relief valve is done. The modeling centers on the main valve, because the dynamic performance is determined by the main valve. To validate such modeling, a frequency-experiment result of the pressure relief valve, whose bandwidth is about 3 Hz, is presented as comparison. The modeling and the frequency experimental result show that it is reasonable to regard the pressure relief valve as a second-order system with two low corner frequencies. PID control, dead band compensation control and adaptive robust control（ARC） are proposed and simulation results are presented. For the ARC, implements by using first order approximation and second order approximation are presented. The simulation results show that the second order approximation implement with ARC can track 4 Hz sine signal very well, and the two ARC simulation errors are within 0.2 MPa. Finally, experiment results of dead band compensation control and adaptive robust control are given. The results show that dead band compensation had about 30° phase lag and about 20% off of the amplitude attenuation. ARC is tracking with little phase lag and almost no amplitude attenuation. In this research, ARC has been tested on a pressure relief valve. It is able to improve the valve＇s dynamic performances greatly, and it is capable of the pressure control of shield machine excavation.

An adaptive energy regulation in a memristive map linearized from a circuit with two memristive channels

Nonlinear circuits can show multistability when a magnetic flux-dependent memristor(MFDM) or a charge-sensitive memristor(CSM) is incorporated into a one branch circuit,which helps estimate magnetic or electric field effects.In this paper,two different kinds of memristors are incorporated into two branch circuits composed of a capacitor and a nonlinear resistor,thus a memristive circuit with double memristive channels is designed.The circuit equations are presented,and the dynamics in this oscillator with two memristive terms are discussed.Then,the memristive oscillator is converted into a memristive map by applying linear transformation on the sampled time series for the memristive oscillator.The Hamilton energy function for the memristive oscillator is obtained by using the Helmholtz theorem,and it can be mapped from the field energy of the memristive circuit.An energy function for the dual memristive map is suggested by imposing suitable weights on the discrete energy function.The dynamical behaviors of the new memristive map are investigated,and an adaptive law is proposed to regulate the firing mode in the memristive map.This work will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map energy calculation.

Adaptive output-feedback power-level control for modular high temperature gas-cooled reactors

Small modular reactors(SMRs) are beneficial in providing electricity power safely and viable for specific applications such as seawater desalination and heat production. Due to its inherent safety feature, the modular high temperature gas-cooled reactor(MHTGR) is considered as one of the best candidates for SMR-based nuclear power plants. Since its dynamics presents high nonlinearity and parameter uncertainty, it is necessary to develop adaptive power-level control, which is beneficial to safe, stable, and efficient operation of MHTGR and is easy to be implemented. In this paper, based on the physically-based control design approach, an adaptive outputfeedback power-level control is proposed for MHTGRs. This control can guarantee globally bounded closedloop stability and has a simple form. Numerical simulation results show the correctness of the theoretical analysis and satisfactory regulation performance of this control.

Adaptive Regulation for Hammerstein and Wiener Systems with Event-Triggered Observations

The paper considers the adaptive regulation for the Hammerstein and Wiener systems with event-triggered observations.The authors adopt a direct approach,i.e.,without identifying the unknown parameters and functions within the systems,adaptive regulators are directly designed based on the event-triggered observations on the regulation errors.The adaptive regulators belong to the stochastic approximation algorithms and under moderate assumptions,the authors prove that the adaptive regulators are optimal for both the Hammerstein and Wiener systems in the sense that the squared regulation errors are asymptotically minimized.The authors also testify the theoretical results through simulation studies.

Adaptive Output Feedback Regulation for a Class of Uncertain Feedforward Time-Delay Nonlinear Systems

This paper addresses the global output feedback regulation problem for a class of uncertain feedforward time-delay nonlinear systems.Unlike to the previous works,the nonlinear functions in the class of systems under consideration are dominated by an input-output function multiplied by an unknown parameter and linear unmeasured states.By using only one dynamic gain and an appropriate Lyapunov-Krasovskii functionals,it is shown that the closed-loop system is globally asymptotically stable.Finally,a numerical example is provided to illustrate the effectiveness of the proposed design scheme.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

Adaptive output regulation for cyber-physical systems under time-delay attacks

In this paper,we present an output regulation method for unknown cyber-physical systems(CPSs)under time-delay attacks in both the sensor-to-controller(S-C)channel and the controller-to-actuator(C-A)channel.The proposed approach is designed using control inputs and tracking errors which are accessible data.Reinforcement learning is leveraged to update the control gains in real time using policy or value iterations.A thorough stability analysis is conducted and it is found that the proposed controller can sustain the convergence and asymptotic stability even when two channels are attacked.Finally,comparison results with a simulated CPS verify the effectiveness of the proposed output regulation method.

Direct adaptive regulation of unknown nonlinear systems with analysis of the model order problem

A new method for the direct adaptive regulation of unknown nonlinear dynamical systems is proposed in this paper,paying special attention to the analysis of the model order problem.The method uses a neurofuzzy (NF) modeling of the unknown system,which combines fuzzy systems (FSs) with high order neural networks (HONNs).We propose the approximation of the unknown system by a special form of an NF-dynamical system (NFDS),which,however,may assume a smaller number of states than the original unknown model.The omission of states,referred to as a model order problem,is modeled by introducing a disturbance term in the approximating equations.The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability properties.An adaptive modification method,termed ‘parameter hopping’,is incorporated into the weight estimation algorithm so that the existence and boundedness of the control signal are always assured.The applicability and potency of the method are tested by simulations on well known benchmarks such as ‘DC motor’ and ‘Lorenz system’,where it is shown that it performs quite well under a reduced model order assumption.Moreover,the proposed NF approach is shown to outperform simple recurrent high order neural networks (RHONNs).

ADAPTIVE REGULATION FOR DETERMINISTIC SYSTEMS

For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the parameter estimation is ignored, then the system input and outputcan be reduced to zero with an exponential rate.

Study of Multi media Streams Dynamic Rate Control Based on Fuzzy Adaptive PID

A Multimedia streams dynamic rate control algorithm based on Fuzzy adaptive PID （MFPID） has been proposed to implement multimedia streams＇ end sending rate on-line self-regulating and smoothing, and to track system resources in time, so that it can avoid system＇s regulating oscillation and guarantee system＇s stability. And, some work has been done to analyze adaptive session model of multimedia streams, to implement future available bandwidth estimation of IP network, to achieve PID parameters＇ on-line self-tuning by fuzzy controlling. Simulation validated the theoretical results of MFPID.

Optimal management adaptive of two crossroads by genetic algorithm

This paper addresses the issue of managing the traffic flow at traffic-light controlled junctions adopting a mixed,nonlinear programming model.More precisely,we adopt an adaptive,acyclic control system based on the genetic algorithm(GA),a system that allows to accurately decide in real time on the light switching pattern that minimizes the traffic waiting time.Equally importantly,the inherent crossroads constraints,namely,the security of their users and the structure thereof,also referred to as the antagonism principle,are considered in the proceeding of this method.The implementation of this model on two crossroads significantly decreases the waiting time and regulates the traffic flow.Therefore,the adaptive control system based on the GA proves efficient compared to the nonadaptive system.