Showing papers by "Hong Wang published in 2012"

Knowledge-Based Global Operation of Mineral Processing Under Uncertainty

Abstract: In this paper, a novel knowledge-based global operation approach is proposed to minimize the effect on the production performance caused by unexpected variations in the operation of a mineral processing plant subjected to uncertainties. For this purpose, a feedback compensation and adaptation signal discovered from process operational data is employed to construct a closed-loop dynamic operation strategy. It uses the signal to regulate the outputs of the existing open-loop and steady-state based system so as to compensate the uncertainty in the steady-state operation at the plant-wide level. The utilization mechanism of operational data through constructing increment association rules is firstly described. Then, a rough set based rule extraction approach is developed to generate the compensation rules. This includes two steps, namely the determination of the variables to be compensated based on the significance of attributes in the rough set theory and the extraction of the compensation rules from process data. Based upon the operational data of the mineral processing plant, relevant rules are obtained. Both simulation and industrial experiments are carried out for the proposed global operation, where the effectiveness of the proposed approach has been clearly justified.

Minimum entropy control for non-linear and non-Gaussian two-input and two-output dynamic stochastic systems

Abstract: In this study, the problem of control algorithm design for a class of nonlinear two-input and two-output systems with non-Gaussian disturbances is investigated, where a general non-linear auto-regressive moving average with exogenous model is used to describe the system. Based on the deduced probability density functions of tracking errors, a new performance index is established using the entropy and joint entropy so as to characterise the uncertainty of the tracking errors of the closed-loop system. This performance also includes the expectations of tracking errors and the constrains of control energy. A recursive optimisation control algorithm is obtained by minimising the performance index. Moreover, the local stability condition of the closed-loop systems is established after some formulations. Finally, the comparative simulation results are presented to show that the performance of the proposed algorithm is superior to that of proportional–integral–derivative controller.

Modeling Component Concentrations of Sodium Aluminate Solution Via Hammerstein Recurrent Neural Networks

Abstract: The component concentrations of sodium aluminate solution are important indices in alumina processing. At present, they are obtained by laboratory titration on samples taken from the production process. Due to the delays in taking and testing samples, they cannot be used for real-time control and optimization. Existing online measurements are not adopted because of the characteristics of the sodium aluminate solution such as high viscosity and the ease of precipitation which leads to pipeline blocking and decreased precision. In this paper, a new modeling method is proposed to measure the component concentrations online using the measurements of conductivity and temperature. The method combines the partial least squares (PLS) technique and the Hammerstein recurrent neural networks (HRNN), where a stable learning algorithm with theoretical analysis is given for the HRNN model. For this PLS-based HRNN, the PLS technique is used to solve the high dimensional and correlated data. Meanwhile, the HRNN technique is used to fit the nonlinear and dynamic characters of the process. An industrial experimental study on a sodium aluminate solution is described. The experiment results show that the proposed method is sufficient to warrant further evaluation in industrial scale experiments.

High-Gain Observer-Based Estimation of Parameter Variations With Delay Alignment

Abstract: This technical note analyzes the estimation delay in a high gain observer, where the state estimates may lag behind the actual states due to the observer's non-zero phase response. The technical note proves that, for a slowly time-varying system subject to bounded noises, the estimation delay depends on the observer gain, but is independent of the variations of system parameters. Rather than estimating the delay, a novel method is proposed to calculate the delay from the observer's phase response. In terms of system identification, the delay is compensated by aligning other measurements with the lagged estimate so that they have the same lag. The simulation results of an aero engine model show significant improvements in estimation. On one hand, the proposed approach improves the estimation accuracy, and on the other hand, it removes the assumption of zero delay and gives a new insight into the high-gain observer design.

ILC-Based Minimum Entropy Filter Design and Implementation for Non-Gaussian Stochastic Systems

Abstract: A new filtering approach based on the idea of iterative learning control (ILC) is proposed for linear and non-Gaussian stochastic systems. The objective of filtering is to estimate the states of linear systems with non-Gaussian random disturbances so that the entropy of output error is made to monotonically decrease along the progress of batches of process operation. The term Batch is referred to a period of time when the process repeats itself. During a batch, the filter gain is kept fixed and state estimation is performed. Between any two adjacent batches, the filter gain is updated so that the entropy of closed-loop output error is reduced for the next batch. Analysis is carried out to explicitly determine the learning rates which lead to convergence of the overall algorithm. Experiments have been implemented on a laboratory-based process test rig to demonstrate the effectiveness of proposed filtering method.

Robust satisfactory fault‐tolerant control of continuous‐time interval systems with time‐varying state and input delays

Abstract: SUMMARY With the performance constraints on exponential stability, H∞ norm of disturbance attenuation and upper bound of quadratic cost performance, the satisfactory and passive fault-tolerant control problem is investigated for a class of interval systems with time-varying input and state delays in the case of possible actuator faults. The bounded-varying dynamics of actuator faults is described by interval matrix, which is more general and can be dealt with by the interval system theory. The delay-dependent satisfactory fault-tolerant controller design is developed based on multi-objective optimization strategy. The results are derived in the forms of linear matrix inequalities, which is convenient to be solved in practice. Simulative example is presented to illustrate the effectiveness and necessity of the proposed fault-tolerant control strategy. Copyright © 2011 John Wiley & Sons, Ltd.

An overview of modelling and simulation of thermal power plant

Abstract: Thermal power plants constitute the largest proportion of installed capability in global power generation system and consume large quantities of coal. Achieving optimal operation of thermal power units, improving the efficiency and reducing the coal consumption is of great significance for the reduction of greenhouse gas and pollutants emissions. Modelling and simulation is the base of optimal operation and control in thermal power unit. In this paper, research results of modelling and simulation of thermal power units are reviewed. Firstly, several common models which are used for researches of thermal power control systems are analysed, including simplified turbine and furnace models for unit coordinated control system (CCS) as well as local equipment models. Then the system structure, function and application of thermal power stimulated simulator are described. Finally, the structure and function of digital power plant is introduced. The challenges of modelling and simulation of thermal power plant res...

Reinforcement learning algorithm application and multi-body system design by using MapleSim and Modelica

Abstract: Advanced intelligent systems such as robots must be capable to interact with dynamic environment and adapt their behavior to it efficiently. Currently, modeling humanoid robots with sophisticated learning and cognitive capabilities is one of the most challenging issues in the field of intelligent robotics. Robots must be equipped with the ability to modify and add to its knowledge base information gained from its past failings. This might provide stable robust walking on unseen terrains as well. Moreover, a further critical stage in designing and evaluating such a sophisticated complex system is modeling and simulation. This paper describes preliminary work on designing a simple multi-body system by using MapleSim, which is a tool for multi-body modeling/simulation and reinforcement learning algorithm is applied to this multi-body system in terms of using Modelica models.

Static output feedback reliable controller design of nonlinear systems with multiple constraints

Abstract: The problem of robust reliable control with multi-objective requirements and static-output-feedback (SOF) form is studied for a class of uncertain nonlinear systems subject to vector-bounded nonlinear dynamics, norm-bounded disturbances and actuator faults or the loss of actuator effectiveness. Based on the Lyapunov stability theory and multi-objective optimization strategy, a sufficient condition for the reliable controller design with SOF-form is developed. It can guarantee the robust stability, the given magnitude constraints on control input and system output, the prescribed level on the disturbance attenuation and the optimized quadratic cost performance of the closed-loop system under all possible faults. Thus, the comprehensive performance of the post-fault system is ensured by the designed controller. Finally, a numerical example is provided to verify the effectiveness of our proposed design algorithm.

Energy-balancing-based control design for power systems

Abstract: This paper focused on the development of the Hamiltonian theory and building Hamiltonian model, especially power system. To obtain better control result of Hamiltonian system, adaptive control and energy-balancing-based control are considered. Combined those two methods with Hamiltonian control system, by using simulation, the performing result can be achieved.

Fault detection with MAC delay compensation in Wireless Sensor Actuator Networks

Abstract: Although the Wireless Sensor and Actuator Networks (WSANs) have many advantages than the wired networks, the nature of sharing wireless media and the complicated behavior of Media Access Control (MAC) introduce adverse impacts on the control system. In contrast to most work on networked control systems using simplified models of network induced delays, this paper considers the study the random access delay caused by a contention-based MAC scheme slotted ALOHA in WSANs. An improved fault detection observer with access delay compensation is proposed to improve the fault detection performance against the MAC delays.

Improved observer-based fault estimator design for a class of T-S fuzzy systems

Abstract: For a class of Takagi–Sugeno (T-S) fuzzy nonlinear systems, in this article, an improved fuzzy observer-based fault estimator is designed to estimate the fault signals on-line based on common Lyapunov function. Attention is focused on the linear matrix inequality (LMI)-based design method with specified performance constraints and less conservatism, which is suitable for complex system models with more rules. The multiobjective and H ∞ optimisation theory is applied to cope with the constraints on the disc-regional poles assignment index and the robustness to disturbances. Some developed relaxing techniques are utilised to reduce the conservatism and the number of LMI constraints which were generated by the conventional design method with common quadratic Lyapunov function. Thus, the resulting estimator is not only less conservative, but can also guarantee the desired fault estimation performance on the rapidity as well as robustness to neglected modelling dynamics, and the robustness against external dis...

MLP-based nonlinear modelling for energy saving in forming section of paper machines

Abstract: Due to the increasing cost of energy and the demand of reducing the environmental footprints, energy saving is becoming an important subject in the industry operation. To realize the energy consumption optimization of papermaking, the energy model should be established while the product quality and process model also need to be constructed, which are taken as the constraints for optimization. This paper describes the identification of a forming section of paper machines with Multilayer Perception (MLP) Neural Networks. The process model, product quality model and energy consumption model are established for the energy saving in papermaking. The real industrial step tests are performed and the data are used to model training and validation. The models are validated by means of mean-squared error (MSE), fit measure and Akaike's Final Prediction Error (FPE). The results show the effectiveness of the established models, which are suitable for the next work of energy optimization.

Sliding mode iterative learning control for non-linear systems with uncertainties

Abstract: A new sliding mode-based iterative learning control algorithm is proposed and its stability is proved An important characteristic of the algorithm is that the entire control signal is comprised of the sliding mode part and the iterative learning part, while the current signal of the iterative part is computed by its previous value and the sliding mode error As a result, repeatable disturbances are cancelled out by the iterative learning part, while non-repeatable uncertainties are dealt with by the sliding mode part The effectiveness is illustrated by simulations

An entropy approach to filtering for networked control systems

Abstract: This paper is concerned with minimum error entropy filter design for non-linear Networked Control Systems (NCSs) with multiple-packet transmission mechanism. Since the error of the filter in NCSs is generally non-Gaussian, the filter is designed under the information theoretic learning frame. The convergent condition of the proposed filter is established. A simulation example shows that the proposed approach is feasible and effective.

Based on the LabVIEW Frequency Modulation Continuous Wave Radar Signal Acquisition and Processing

Abstract: This paper introduces the FMCW anti-collision radar system, introduces the concept of virtual instrument and LabVIEW characteristics. The use of LabVIEW virtual instrumental technology, research and development of the related code, the virtual completion signal acquisition system design, at any time on the echo signal acquisition and processing, the calculation result and the safety distance data are compared to determine whether the alarm.

Measurement and Application of Radiant Energy

Abstract: Based on the application of radiant energy in various fields, the fundamental knowledge about incident radiation, including physical quantities and units, is summarized. Then, the category and principle of radiant energy detector are introduced, i.e. thermal detectors and photodetectors. Also, their application conditions are compared. It is significantly important for users to understand their operation principles and to choose an appropriate radiation measuring detector.

Stochastic stability of genetic regulatory networks with multiple time-varying delays

Abstract: This paper investigates the stochastic stability problem of genetic regulatory networks (GRNs) with multiple time-varying delays. Most of the results on the stability of GRNs with delays are based on a model in which the delays of the transcription and translation processes for each gene product take the same value. In this paper, we generalized the model to a differential equation with multiple time-varying delays. Sufficient conditions for the stochastic stability of the GRNs with stochastic noise are obtained. Finally, a simulation example is given to show the effectiveness of the proposed results.

Design and Development of an Embedded Intelligent Optimal Control Platform

Abstract: An embedded intelligent optimal control platform is designed and developed in this paper. On the hardware side, the embedded industrial controller uses ARM9 as the core processor to meet the requirements of low-power consumption, low-cost and high computation performance. On the software side, Xenomai Linux OS and ProConOS are adopted to realise hard real-time control performance. Besides, MultiProg is used to support block programming of the control software. To validate the effectiveness of embedded control platform, the grinding circuit mineral processing plants is used as an example to design an intelligent optimal control system and validates its performance through Hardware-in-the-Loop experiment. The experiment results show that this platform can fully satisfy the requirements of industrial optimal control.