Quantitative fault estimation for a class of non-linear systems

This paper develops a methodology for actuator fault diagnostics and quantitative estimation of fault signals in a class of non-linear systems. The class of non-linear systems considered is one in which the non-linearity is an incremental quadratic type of non-linearity that includes Lipschitz, positive real and sector non-linear functions. The methodology provides for both state estimation and fault vector estimation. An LMI procedure can be utilized for explicit computation of the observer gains. The procedure developed can also be specialized to linear time invariant systems. Compared to previous results for LTI systems, the procedure developed herein does not require the LTI system to be minimum phase. The use of the developed methodology is demonstrated through two illustrative examples of real world physical applications.     

State-feedback control of non-linear systems†

A design method for state-feedback controllers for single-input non-linear systems is proposed. The method makes use of the transformations of the non-linear system into ‘controllable-like’ canonical forms. The resulting non-linear state feedback is designed in such a way that the eigenvalues of the linearized closed-loop model are invariant with respect to any constant operating point. The method constitutes an alternative approach to the design methodology recently proposed by Baumann and Rugh. Also a review of different transformation methods for non-linear systems is presented. An example and simulation results of different control strategies are provided to illustrate the design technique.     

Disturbance attenuation using proportional integral observers

In this paper, we first propose a proportional integral observer design for single-output uncertain linear systems which permit us to attenuate either measurement noise or modelling errors. We show that, when only sensor noise is present in the system, an integral observer alone suffices to achieve good convergence and filtering properties. On the other hand, when modelling errors and sensor noise are present, we show that, for some classes of linear systems, the proportional integral observer allows us to decouple completely the modelling uncertainties while keeping satisfactory convergence properties. A comparison of the classical proportional observer to the proposed observers are given via academic simulation examples. We next extend the design to the class of single-output uniformly observable non-linear systems. We show through a practical simulation example, dealing with a flexible joint robot, that the non-linear proportional integral has very satisfactory disturbance attenuating properties.     

Exponential tracking of feedback linearizable non-linear control systems with uncertainties

This paper studies the robust output tracking problem of feedback linearizable non-linear control systems with uncertainties. Utilizing the input-output feedback linearization technique and the Lyapunov method for non-linear state feedback synthesis, a robust globally exponential output tracking controller design methodology for a broad class of non-linear control systems with uncertainties is developed. The underlying theoretical approaches are the differential geometry approach and the composite Lyapunov approach. One utilizes the parametrized coordinate transformation to transform the original non-linear system with uncertainties into a singularly perturbed model with uncertainties and the composite Lyapunov approach is then applied for output tracking. To demonstrate the practical applicability, the paper has investigated a pendulum control system.     

Gradient-Like Observers for Invariant Dynamics on a Lie Group

This paper proposes a design methodology for non-linear state observers for invariant kinematic systems posed on finite dimensional connected Lie groups, and studies the associated fundamental system structure. The concept of synchrony of two dynamical systems is specialized to systems on Lie groups. For invariant systems this leads to a general factorization theorem of a nonlinear observer into a synchronous (internal model) term and an innovation term. The synchronous term is fully specified by the system model. We propose a design methodology for the innovation term based on gradient-like terms derived from invariant or non-invariant cost functions. The resulting nonlinear observers have strong (almost) global convergence properties and examples are used to demonstrate the relevance of the proposed approach.      

Adaptive Fuzzy Sliding Mode Controller for the Kinematic Variables of an Underwater Vehicle

This paper address the kinematic variables control problem for the low-speed manoeuvring of a low cost and underactuated underwater vehicle. Control of underwater vehicles is not simple, mainly due to the non-linear and coupled character of system equations, the lack of a precise model of vehicle dynamics and parameters, as well as the appearance of internal and external perturbations. The proposed methodology is an approach included in the control areas of non-linear feedback linearization, model-based and uncertainties consideration, making use of a pioneering algorithm in underwater vehicles. It is based on the fusion of a sliding mode controller and an adaptive fuzzy system, including the advantages of both systems. The main advantage of this methodology is that it relaxes the required knowledge of vehicle model, reducing the cost of its design. The described controller is part of a modular and simple 2D guidance and control architecture. The controller makes use of a semi-decoupled non-linear plant model of the Snorkel vehicle and it is compounded by three independent controllers, each one for the three controllable DOFs of the vehicle. The experimental results demonstrate the good performance of the proposed controller, within the constraints of the sensorial system and the uncertainty of vehicle theoretical models.     

Non-linear tracking control for multivariable constrained input linear systems

This paper extends the non-linear tracking methodology of Lin et al. to high-order and multivariable linear systems. The results provide a method of synthesizing controllers which asymptotically track a constant set-point. As with the original scheme, allowances for control limits are handled directly in the design phase, and no a posteriori compensation is required. The resulting control law consists of both linear and non-linear parts; the non-linear term being added with a view to improving the response of the system, without reducing the size of its basin of attraction. The proposed technique is demonstrated on two aerospace examples which indicate promising results.     

Physical simulation: The use of scaled-down fully functional components to analyze and design automated production systems

This paper presents and discusses the physical simulation methodology currently employed in the Industrial Automation Laboratory in the Department of Industrial Engineering at Texas A&M University. Physical simulation is the study of complex automated manufacturing and material handling systems through the use of scaled-down system replicas controlled by mini and microcomputers using full-sized software. The physical simulation methodology is the design, construction, operation, and study of such systems in a laboratory environment. The methodology consists of identifying basic automated system components; constructing scaled-down, functionally-equivalent generic models of the components with mechanical breadboarding kits; and then using these generic models to construct fully functional scaled-down systems. Thus, it allows us to evaluate the dynamic physical interactions using the models to confirm design decisions and to develop and test system software in parallel with the construction of the full-sized system. This approach should allow a cost reduction in the design cycle for complex automation because (1) through it we can identify design errors early, and (2) it provides a mechanism for the parallel development of both the computer hardware/software control system and the system's machinery. The methodology is currently under development in the Industrial Automation Laboratory in the Department of Industrial Engineering at Texas A&M University.     

Stability analysis and non-linear behaviour of structural systems using the complex non-linear modal analysis (CNLMA)

Herein, a novel non-linear procedure for producing non-linear behaviour and stable limit cycle amplitudes of non-linear systems subjected to super-critical Hopf bifurcation point is presented. This approach, called Complex Non-Linear Modal Analysis (CNLMA), makes use of the non-linear unstable mode which governs the non-linear dynamic of structural systems in unstable areas. In this study, the computational methodology of CNLMA is presented for the systematic estimation of the non-linear behavior of structural system, and it is applied to a complex system with quadratic and cubic non-linearities. Results from this non-linear method are compared with results obtained by integrating the full original non-linear system. This procedure appears very interesting in regard to computational time, formulation, and also necessitates very few computer resources. Thus the CNLMA is suited to computational implementation for various complex non-linear system with many degrees-of-freedom.     

A safe, accurate intravenous infusion control system

Medical facilities use conventional intravenous (IV) infusion systems in cases where the patient needs some kind of programmed medicine or nutrition. Gravity controls the simplest and cheapest system. In this kind of system, however, the flow rate varies with the bottle's fluid volume and the hose's pressure. Adjusting the drip feed keeps the rate constant. This article details our development of a control system for conventional gravity-controlled infusion systems that guarantees a safe and accurate infusion process. The control system measures the current flow rate through an optical sensor in the drop window and controls it by compressing or decompressing the hose with a motor. We wanted the system to control conventional IV infusion systems with hardware and software. This kind of implementation creates a low-cost control system that resists software faults. Providing this kind of safety becomes crucial if the microcomputer is also used for other purposes. To analyze the various implementation possibilities, we used the PISH (integrated design of software and hardware) codesign methodology developed by our research group. This fault-tolerant system implemented in hardware and software and partitioned with a codesign methodology revolutionizes traditional IV infusion control systems     

Relaxed Lyapunov criteria for robust global stabilisation of non-linear systems

The notion of the restricted Robust Control Lyapunov Function (RCLF) is introduced and is exploited for the design of robust feedback stabilisers for non-linear systems. Particularly, it is shown for systems with input constraints that ‘restricted’ RCLFs can be easily obtained, while RCLFs are not available. Moreover, it is shown that the use of ‘restricted’ RCLFs usually results in different feedback designs from the ones obtained by the use of the standard RCLF methodology. Using the ‘restricted’ RCLFs feedback design methodology, a simple controller that guarantees robust global stabilisation of a perturbed chemostat model is provided.     

On the control of non-linear processes: An IDA–PBC approach

A high performance non-linear controller that exploits the properties of port-Hamiltonian systems to precisely define the interconnections and energy dissipations of non-linear processes is presented for a large class of chemical processes. The controller is derived from classical interconnection and damping assignment–passivity based control theory but an additional degree of freedom is introduced in the controller design by the use of “non-exact matching” closed-loop storage functions. By a proper closed-loop interconnection assignment the proposed controller achieves total decoupling between outputs and since no inversion of the process dynamics is made in the design it is equally applicable to minimum phase and nonminimum phase system. The controller design methodology is presented and stability conditions are stated. As illustrative example the proposed method is used to design controllers for a multiple-input/multiple-output non-isothermal continued stirred tank reactor that exhibits nonminimum phase behavior.     

Dynamic inversion for multivariable non-affine-in-control systems via time-scale separation

This paper presents a tracking design methodology applicable to multivariable non-affine-in-control systems. The main focus is on solving the tracking problem for non-linear systems whose dynamics depend non-linearly on the control input. The latter is designed to be faster than the main system dynamics. Using singular perturbation theory along with the Lyapunov stability theorems, it is shown that the proposed controller approximates an unknown dynamic inversion based solution with bounded errors, provides closed-loop stability, and solves the tracking problem with bounded errors. Simulations illustrate the theoretical results.     

可配置远程温度监控SoPC系统设计与实现

介绍了一种基于现场可编程逻辑门阵列( FPGA)的可配置远程监控系统的设计与实现,详细阐述该监控系统的工作原理和软件与硬件设计.设计实现的监控系统可以实时调整温度传感器的工作模式和相关参数,并通过IPv4通信协议传送到监控上位机PC端.采用基于SoPC的软硬件协同设计的系统设计方案,具有很高的灵活性和稳定性.根据实测结果表明,系统性能稳定,效率好,参数可实时重配置,与传统的温度传感监控系统相比具有一定的优势.     

Estimation of states,faults and unknown disturbances in non-linear systems

In this paper, we extend existing theory on non-linear unknown input observer design to a wider class of non-linear systems where the systems are subject to unknown input and output disturbances and experience faults. The approach used is to decouple the faults and unknown disturbances from the rest of the system through a series of transformations on state and output equations. Once total disturbance decoupling is achieved, an appropriate observer for the disturbance free part of the non-linear system is proposed and designed. The designed observer is, subsequently, used for estimation of unknown inputs, outputs, and fault signals. Two examples are given for illustration.     

Direct passivity of a class of MIMO non-linear systems using adaptive feedback

In this paper, an adaptive controller with adaptive laws specially designed is proposed to solve the problem of making a multi-input multi-output (MIMO) non-linear system, with explicit linear parametric uncertainty, equivalent to a passive system. These results are an extension of those obtained by the authors for the SISO case. Some stability issues associated to the resultant closed-loop passive system are also discussed. The results obtained are applied to models of dynamical MIMO systems, to illustrate the controller design methodology.     

A formal methodology using attributed grammars for multiprocessing-system software development. I. Design representation

With the growing complexity of multiprocessing systems and distributed computing systems, there is an increasing need to provide a formal methodology for deriving a model to represent software design for the software development of these systems. The formal methodology presented in this paper uses attributed grammars, and extends formal methods commonly used in the definition of programming languages and compiler techniques for representing the design specification of software systems and validating the implementation. This model provides a common basis in the software development phases through automated design analysis, test-case generation, and validation of the software system. This paper covers the construction of the model for the design representation using attributed grammar and the analysis of the software system design based on the model.     

A delay-dependent approach to H ∞ filtering for stochastic delayed jumping systems with sensor non-linearities

In this paper, a delay-dependent approach is developed to deal with the stochastic H _∞ filtering problem for a class of Itô type stochastic time-delay jumping systems subject to both the sensor non-linearities and the exogenous non-linear disturbances. The time delays enter into the system states, the sensor non-linearities and the external non-linear disturbances. The purpose of the addressed filtering problem is to seek an H _∞ filter such that, in the simultaneous presence of non-linear disturbances, sensor non-linearity as well as Markovian jumping parameters, the filtering error dynamics for the stochastic time-delay system is stochastically stable with a guaranteed disturbance rejection attenuation level γ. By using Itô's differential formula and the Lyapunov stability theory, we develop a linear matrix inequality approach to derive sufficient conditions under which the desired filters exist. These conditions are dependent on the length of the time delay. We then characterize the expression of the filter parameters, and use a simulation example to demonstrate the effectiveness of the proposed results.     

Nearly-orthogonal sampling and neural network metamodel driven conceptual design of multistage space launch vehicle

This paper presents a new design methodology for efficient conceptual design of complex systems involving multidisciplinary and computationally intensive analysis with large number of design variables. A nearly-orthogonal sampling of design space is proposed with good space filling properties to extract maximum useful information about system behavior using much lower number of trial designs. This sampled data is then used as training data for artificial neural network, which will act as a metamodel to represent the time consuming disciplinary analyses. A stage-wise interconnection of separate neural networks is also proposed for trajectory metamodel to offset dimensionality curse of neural networks. Genetic Algorithm performs global optimization by utilizing this metamodel and subsequently sequential quadratic programming performs the local optimization utilizing exact analyses. The performance of proposed methodology is investigated in this paper for the conceptual design optimization of multistage solid fueled space launch vehicle. The results show excellent approximation of highly non-linear functions using proposed sampling and drastic reduction in overall design optimization time, due to greatly reduced number of exact disciplinary analyses.