A proportional integral extremum-seeking control approach for discrete-time nonlinear systems

This paper proposes a proportional-integral extremum-seeking control technique for a class of discrete-time nonlinear dynamical systems with unknown dynamics. The technique is a generalisation of existing time-varying extremum-seeking control (ESC) techniques that provides fast transient performance of the closed-loop system to the optimum equilibrium of a measured objective function. The main contribution of the proposed technique is the addition of a proportional action that can be used to minimise the impact of a time-scale separation on the transient performance of the ESC system. The integral action fulfils the role of standard ESC techniques to identify optimal equilibrium conditions. The effectiveness of the proposed approach is demonstrated using a simulation example.     

Transient fault tolerance in digital systems

It is hard to shield systems effectively from transient faults (fault avoidance techniques). So some other means must be employed to assure appropriate levels of transient fault tolerance (insensitivity to transient faults). They are based on fault-masking and fault recovery ideas. Having analyzed this problem, the author identifies critical design points and outlines some practical solutions that refer to efficient on-line detectors (detecting errors during the system operation) and error handling procedures. This framework provides a basis for understanding transient fault problems in digital systems. It can be helpful in selecting optimum techniques to mask or eliminate transient fault effects in developed systems     

Lyapunov-based switching control of nonlinear systems using high-gain observers

We consider output feedback stabilization of uniformly observable uncertain nonlinear systems when the uncertain parameters belong to a known but comparably large compact set. In a previous paper, we proposed a new logic-based switching control to improve the performance of continuous high-gain-observer-based sliding mode controllers. Our main goal here is to show that similar techniques can be exploited for solving challenging control problems for a more general class of uncertain nonlinear systems. We require neither the sign of the high-frequency gain to be known nor the system to be minimum-phase. The key idea is to split the set of parameters into smaller subsets, design a controller for each of them, and switch the controller if, after a dwell-time period, the derivative of the Lyapunov function does not satisfy a certain inequality. A high-gain observer is used to estimate the derivatives of the output as well as the derivative of the Lyapunov function. Another goal of this paper is to introduce a switching strategy that uses on-line information to decide on the controller to switch to, instead of using a pre-sorted list as in our previous work. The new strategy can improve the transient performance of the system.     

Data-driven control in marine systems

With the advent of cheap smart sensors installed on board marine vehicles and the increasing computational power of small embedded processors there is tremendous potential for the implementation of new strategies to control marine systems on the basis of input-output plant data. The emerging field of smart sensors affords a unique opportunity to have access to on-line measurement of dynamical systems’ variables seamlessly, at a low price. By applying a data-driven control algorithm to a marine vehicle, the paper introduces a new perspective on how data can be used in the control loop in marine systems. Classical control methodologies start by developing a model of the plant to be controlled, after which a number of control design techniques can be used. Recent advances in so-called model-free data-driven control methodologies, in particular unfalsified control, hold promise to merge the identification and control phases. Unfalsified control techniques build on the construction of a bank of controllers for a given plant, in which there exists at least one controller that meets the desired performance specification and a falsification unit. The latter is implemented using a cost function that directly evaluates the performance of the controllers (in and out of the feedback loop) using measured input and output data. At each sampling time, the performance of the controllers is assessed and the controllers that do not meet the pre-defined performance specification criteria will be falsified and removed from the bank of the controllers, after which an active controller will be selected among the unfalsified ones. In this paper, by presenting the results of the application of unfalsified control to the problem of Dynamic Positioning (DP) of marine vessels subjected to environmental forces, we aim to attract the attention of researchers in the field of marine control to the new perspective of using data to directly control marine system.     

Optimal placement of phasor measurement systems for enhancing on-line protection and control of large-scale power systems

Microprocessor-based real-time phasor measurements, i.e. measurement of fundamental frequency, positive sequence, complex three phase voltages and currents, for enhancing on-line protection and control of interconnected electric power systems are described. The proposed research demonstrates that real-time monitoring of key system states, in the bulk power transfer problem, can be identified. It also provides the means for determining which states are the key states for that problem, and that phasor measurements can be used to improve the protection and/or control of the system. The procedure is applied to a realistic system, where such a control problem exists in practice, for confirmation of the developed technique.     

On-line identification and optimal control of continuous-time systems

In this paper, a moving algorithm for on-line identification of continuous-time systems is developed. With the proposed algorithm, the observed input–output data can be directly used to estimate the system parameters without any numerical pre-processing, and by means of a recursive formula the estimates can be updated step by step without repeatedly computing the matrix inversion. In this way, the use of both computer memory and computing time can be reduced. Besides, the computations are simple and straightforward. From the moving identification algorithm, a linear moving model can be obtained to represent the control systems. The on-line optimal control algorithm is also developed via the linear moving model. A slider-crank motion control system is used to illustrate that the proposed on-line identification and optimal control algorithms can give satisfactory results.     

Auditing on-line systems: An evaluation of parallel versus Continuous and Intermittent Simulation

Two auditing techniques are described that can be used to audit on-line applications. Parallel simulation is a batch oriented technique that is used to audit on-line systems. Continuous and Intermittent Simulation (CIS) was specifically designed to audit on-line applications. CIS allows transactions to be audited as they are being processed. Procedures are given that determine which instructions of the application should be included in an audit using parallel simulation and CIS. Rules are given for an auditor to determine which auditing technique will create less overhead. This can be determined before the audit plan is implemented.     

Output transient trajectory shaping control for a class of nonlinear systems

This paper presents a transient trajectory shaping (TTS) control method for the SISO strict feedback nonlinear systems. The TTS control refers to explicitly constraining the system output tracking error transient trajectories within predesigned time‐varying boundaries while they are converging to equilibrium. By this method, the boundaries of system output transient trajectories can be designed a priori according to the system transient control performance requirements in both symmetric and asymmetric ways. With a class of time‐varying boundary functions, control laws can be devised by utilizing the enhanced differential unbounded function techniques. Such control laws can ascertain that the system output tracking errors travel within their respectively predesigned time‐varying boundaries while converging to origin. To handle the control input exaggeration issue in TTS, input constraint control techniques are proposed to effectively reduce the required control input magnitude for second‐order systems. A numerical example is utilized to show the effectiveness of the proposed TTS control methods. Copyright © 2013 John Wiley & Sons, Ltd.     

A bio-inspired approach for self-correcting compliant assembly systems

Statistical process monitoring and control has been popularized in manufacturing as well as various other industries interested in improving product quality and reducing costs. Advances in this field have focused primarily on more efficient ways for diagnosing faults, reducing variation, developing robust design techniques, and increasing sensor capabilities. However, statistical process monitoring cannot address the need for instant variation reduction during assembly operations. This paper presents a unique dimensional error-compensation approach for compliant sheet metal assembly processes. The resulting autonomous self-correction system integrates rapidly advancing data mining methods, physical models, assembly modeling techniques, sensor capabilities, and actuator networks to implement part-by-part dimensional error compensation. Inspired by biological systems, the proposed quality control approach utilizes immunological principles as a means of developing the required mathematical framework behind the self-correcting methodology. The resulting assembly system obtained through this bio-mimicking approach will be used for autonomous monitoring, detection, diagnosis, and control of station and system level faults, contrary to traditional systems that largely rely on final product measurements and expert analysis to eliminate process faults.     

Stabilization of switched linear systems with bounded disturbances and unobservable switchings

This paper studies the stabilization problem of switched linear systems with bounded disturbances. It is assumed that the system switches among an infinite set of uniformly controllable linear systems, and that the switching signals are not observable, but the switching duration has a lower bound. It will be shown that by combining on-line adaptive estimation and control in the controller design, a feedback control law can be constructed which makes the switched linear system globally stable.     

REX: Robotics expert systems building tool

REX, an expert systems development shell for robotics applications, is presented in the paper. The shell is intended for building expert systems in various domains of robotics, including design, planning, control, fault-detection, and navigation. REX's knowledge and data representation techniques include several standard techniques, like rules, frames, parameters, and variables, as well as certain techniques which are not commonly used by other expert systems building tools. These specific techniques include sensor data, status data, models and algorithms. The REX inference engine provides a means for reasoning with all of these knowledge and data types. The inference engine features both off-line and on-line modes of operation. REX has powerful interfaces for communicating both with the user and with other software modules in a complex application. Certain learning techniques are built into the shell. An example of applying REX for building an expert system in the domain of robot control is also presented. Finally, a discussion is provided on the relation between REX and second-generation expert systems.     

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.     

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.     

On-line identification of SISO linear time-invariant delay systems from output measurements

The paper presents a theoretical treatment of on-line identification issues and develops parameter identification algorithms for a class of single input single output (SISO) linear time delay systems (LTDS) with uncertain time-invariant parameters. The delays can also be unknown if their possible values belong to a set consisting of a finite number of known values. Since only output measurements are assumed to be available, a sliding mode-based observer of an underlying SISO LTDS with uncertain parameters is constructed and utilized to design an adaptive identifier of system parameters. The theory is supported by an application of the developed algorithms to transient fuel identification in a port fuel injected (PFI) internal combustion engine.     

Diagnosability of discrete event systems and its applications

As man-made systems become more and more complex, diagnostics of component failures is no longer an easy task that can be performed based on experience and intuition. Therefore, it is important to develop a systematic approach to diagnostic problems. Diagnostics can be done either on-line or off-line. By on-line diagnostics, we mean diagnostics performed while the system to be diagnosed is in normal operation. On the other hand, in off-line diagnostics, the system is not in normal operation. We will study both on-line and off-line diagnostics in this paper and identify main features and differences of these two types of diagnostics. We will also introduce the concept of diagnosability and study its properties, all in the framework of discrete event systems. This study is motivated by diagnostic problems in the automotive industry and we will emphasize its applications.     

Discrete-time reference governors and the nonlinear control of systems with state and control constraints

Discrete-time, linear control systems with specified pointwise-in-time constraints, such as those imposed by actuator saturation, are considered. The constraints are enforced by the addition of a nonlinear ‘reference governor’ that attenuates, when necessary, the input commands. Because the constraints are satisfied, the control system remains linear and undesirable response effects such as instability due to saturation are avoided. The nonlinear action of the reference governor is defined in terms of a finitely determined maximal output admissible set and can be implemented on-line for systems of moderately high order. The main result is global in nature: if the input command converges to a statically admissible input and the initial state of the system belongs to the maximal output admissible set, the eventual action of the reference governor is a unit delay.     

Regulation of nonlinear systems using conditional integrators

This paper studies the regulation of nonlinear systems using conditional integrators. Previous work introduced the tool of conditional integrators that provide integral action inside a boundary layer while acting as stable systems outside, leading to improvement in transient response while achieving asymptotic regulation in the presence of unknown constant disturbances or parameter uncertainties. The approach, however, is restricted to a sliding mode control framework. This paper extends this tool to a fairly general class of state feedback control laws, with the stipulation that we know a Lyapunov function for the closed‐loop system. Asymptotic regulation with improvement in transient response is done by using the Lyapunov redesign technique to implement the state feedback control as a saturated high‐gain feedback and introducing a conditional integrator to provide integral action inside a boundary layer. Improvement in the transient response using conditional integrators is demonstrated with an experimental application to the pendubot. Copyright © 2005 John Wiley & Sons, Ltd.     

一类非线性系统的自组织模糊CMAC神经网络自适应重构跟踪控制

提出了一种非线性系统的自组织模糊CMAC(SOFCMAC)神经网络自适应重构跟踪控制方法,首先通过构造增广系统,设计出线性渐近跟踪控制器,然后采用SOFCMAC神经网络在线重构系统的非线性特性,以消除非线性特性引起的系统误差,可保证非线性系统闭环稳定并使系统输出跟踪期望输出.仿真算例证明了SOFCMAC神经网络自适应重构跟踪控制系统的稳定性.     

Robust approximation-free prescribed performance control for nonlinear systems and its application

This paper presents a robust prescribed performance control approach and its application to nonlinear tail-controlled missile systems with unknown dynamics and uncertainties. The idea of prescribed performance function (PPF) is incorporated into the control design, such that both the steady-state and transient control performance can be strictly guaranteed. Unlike conventional PPF-based control methods, we further tailor a recently proposed systematic control design procedure (i.e. approximation-free control) using the transformed tracking error dynamics, which provides a proportional-like control action. Hence, the function approximators (e.g. neural networks, fuzzy systems) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. The proposed control design leads to a robust yet simplified function approximation-free control for nonlinear systems. The closed-loop system stability and the control error convergence are all rigorously proved. Finally, comparative simulations are conducted based on nonlinear missile systems to validate the improved response and the robustness of the proposed control method.     

Interconnectivity analysis techniques for error localization in large systems

Software systems contain multiple types of interrelations among components — data, control, and sequencing, among others. We are developing interconnectivity analysis techniques that derive multiple views of the structure of large-scale software systems. These techniques calculate interconnections among components and then recursively group the components into sets according to their degree of interconnection. These techniques are especially suited to large-scale systems (e.g., > 100,000 lines) since numerous types of interconnections can be determined automatically in a tractable manner. Interconnectivity analysis techniques produce visualizations of system structure and can be used to document systems, model their evolution over time, compare system structure, guide regression testing, or localize error-prone structure. This article summarizes two studies using interconnectivity analysis. In earlier work, one such technique was applied effectively in a feasibility study to characterize the error-prone components in a large-scale system from a production environment. Routines with the highest coupling/cohesion ratios had 8.1 times more errors per 1,000 source lines of code that did routines with the lowest coupling /cohesion ratios. A second validation study is currently underway. In this study, we are applying interconnectivity analysis techniques to the design specification of a large distributed command and control system. Tools supporting interconnectivity analysis will be integrated into the Amadeus measurement-driven analysis and feedback system.