A hybrid receding-horizon control scheme for nonlinear systems

A hybrid receding-horizon control scheme for nonlinear discrete-time systems is proposed. Whereas a set of optimal feedback control functions is defined at the continuous level, a discrete-event controller chooses the best control action, depending on the current conditions of a plant and on possible external events. Such a two-level scheme is embedded in the structure of abstract hybrid systems, thus making it possible to prove a new asymptotic stability result for the hybrid receding-horizon control approach.     

A method for design of a hybrid neuro-fuzzy control system based onbehavior modeling

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea lends to modeling of a plant with unknown structure by defining several types of dynamic behaviors. On the basis of dynamic behavior classification, a new method is presented for the design of a neuro-fuzzy control system in two steps: 1) we model a plant with unknown structure by choosing a set of simplified systems with equivalent behavior as “templates” to optimize their fuzzy controllers off-line; and 2) we use an algorithm for system identification to perceive dynamic behavior and a neural network to adapt fuzzy logic controllers by matching the “templates” online. The main advantage of this method is that convergence problem can be avoided during adaptation process. Finally, the proposed method is used to design neuro-fuzzy controllers for a two-link manipulator     

A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems

An algorithm for the generation of a TS-type neuro-fuzzy system is presented. There are two stages in the generation: in the first stage, an initial structure adapted from an empty neuron or fuzzy rule set, based on the geometric growth criterion and the -completeness of fuzzy rules; in the second stage, the obtained initial structure is refined by a hybrid learning algorithm based on backpropagation and a proposed recursive weight learning algorithm to minimize the system error. The similarity analysis applied throughout the entire learning process attempts both to alleviate overlap among membership functions and to reduce the complexity of the obtained system. Benchmark examples, comparing the proposed algorithm with previous approaches, show the proposed algorithm is more effective in terms of both model accuracy and compactness.     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.     

A control scheme for freeway traffic systems based on hybrid automata

In this paper a hybrid control scheme is devised in order to regulate traffic conditions in freeway systems. The considered control actions are ramp metering, i.e. using traffic lights at the on-ramps in order to regulate incoming traffic, and variable speed limits to be displayed on on-road variable message signs. The proposed scheme is composed of two levels: the lower level is characterized by different Model Predictive Control regulators, whereas at the higher level the different control actions are chosen according to a discrete-event dynamics. The overall scheme is then represented with the formalism of discrete-time discrete-event automata. More in detail, at the lower level, the prediction model used in the Model Predictive Control schemes is the first-order dynamical model of traffic flow in which we approximate the steady-state speed-density characteristic as a piecewise constant function. This approximation is motivated by the fact that we need a simpler finite-horizon problem to be solved on line, that in this case becomes a Mixed-Integer Linear programming problem. Depending on the system operating conditions, different regulators are determined by means of suitable Model Predictive Control schemes. The higher level of the control scheme has the function of identifying the present operating conditions and then switching to the suitable control action. The reported numerical results show the effectiveness of the proposed hybrid control framework.     

A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification

Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neuro-fuzzy scheme for designing a classifier along with feature selection. It is a four-layered feed-forward network for realizing a fuzzy rule-based classifier. The network is trained by error backpropagation in three phases. In the first phase, the network learns the important features and the classification rules. In the subsequent phases, the network is pruned to an "optimal" architecture that represents an "optimal" set of rules. Pruning is found to drastically reduce the size of the network without degrading the performance. The pruned network is further tuned to improve performance. The rules learned by the network can be easily read from the network. The system is tested on both synthetic and real data sets and found to perform quite well.     

Control design for integrator hybrid systems

The authors define controllability for hybrid systems as the existence of correct control laws that transfer the hybrid plant between predefined subsets of the hybrid state space. A methodology for analyzing controllability and synthesizing control laws for a class of hybrid systems, applicable especially in batch control, is proposed. They use a framework consisting of a hybrid plant and a hybrid controller that interact in a feedback fashion     

A study on neuro-fuzzy systems for fault diagnosis

Fault diagnosis can be facilitated by using either quantitative or qualitative information of the system monitored. This paper presents a novel approach to integrate quantitative and qualitative information in fault-diagnosis, based on the use of neuro-fuzzy systems. In this approach the diagnostic signals (residuals) are generated and evaluated via a B-Spline functions network. The configuration adopted allows the designer to both extract and include symbolic knowledge from the trained network to provide reliable diagnostic information. The effectiveness of the proposed diagnosis strategy is illustrated through a simulation study of a nonlinear two-tank system.     

Soft computing framework for intelligent human–machine system design,simulation and optimization

 This paper proposes a novel soft-computing framework for human–machine system design and simulation based on the hybrid intelligent system techniques. The complex human–machine system is described by human and machine parameters within a comprehensive model. Based on this model, procedures and algorithms for human–machine system design, economical/ergonomic evaluation, and optimization are discussed in an integrated CAD and soft-computing framework. With a combination of individual neural and fuzzy techniques, the neuro-fuzzy hybrid soft-computing scheme implements a fuzzy if-then rules block for human–machine system design, evaluation and optimization by a trainable neural fuzzy network architecture. For training and test purposes, assembly tasks are simulated and carried out on a self-built multi-adjustable laboratory workstation with a flexible motion measurement and analysis system. The trained neural fuzzy network system is able to predict the operator's postures and joint angles of motion associated with a range of workstation configurations. It can also be used for design/layout and adjustment of human assembly workstations. The developed system provides a unified, intelligent computational framework for human–machine system design and simulation. Case studies for workstation system design and simulation are provided to illustrate and validate the developed system.     

A real-time 3D motion planning and simulation scheme for nonholonomic systems

We propose a new motion planning and simulation scheme for nonholonomic systems in this paper to provide a practical solution for these application problems taking into account of real-time obstacle avoidance and the continuous curvature path generation simultaneously in 3D unknown environment. The proposed motion planning and simulation scheme generates the motion path using a new universal Euler spiral generation algorithm, which is locally optimal based on perceived points of view. The generated Euler spiral solution can be non-symmetrical and easily implemented while maintaining a C² continuous. It is therefore more flexible and powerful in dealing with dynamic situations in real-time, compared with current symmetrical Euler spirals solutions. Real-time solutions are particularly important in navigation in unknown environments. The universal Euler spiral algorithm proposed displays a smaller maximum curvature value and smaller mean square curvature value than the conventional symmetrical algorithm in tested cases. Another significant contribution of our work is the new motion planning scheme which extend current 2D based motion planning into three-dimensional (3D) space. In this paper, we have conducted experiments and describe simulation results including 3D motion trajectory modeling for a flight simulation.     

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.     

A modelling and simulation based process for dependable systems design

Complex real-time system design needs to address dependability requirements, such as safety, reliability, and security. We introduce a modelling and simulation based approach which allows for the analysis and prediction of dependability constraints. Dependability can be improved by making use of fault tolerance techniques. The de-facto example, in the real-time system literature, of a pump control system in a mining environment is used to demonstrate our model-based approach. In particular, the system is modelled using the Discrete EVent system Specification (DEVS) formalism, and then extended to incorporate fault tolerance mechanisms. The modularity of the DEVS formalism facilitates this extension. The simulation demonstrates that the employed fault tolerance techniques are effective. That is, the system performs satisfactorily despite the presence of faults. This approach also makes it possible to make an informed choice between different fault tolerance techniques. Performance metrics are used to measure the reliability and safety of the system, and to evaluate the dependability achieved by the design. In our model-based development process, modelling, simulation and eventual deployment of the system are seamlessly integrated.     

A scheme for the realization of graphical computer aided design systems

We present some of the problems encountered in the study of CAD systems in an electro-mechanical company, and propose a methodology based entirely on a detailed object study. In the first part of this paper we analyse the traditional design process in the company and define the most important functions of a CAD system. In the second part, we show how a functional hierarchical decomposition of the product helps us to realize the CAD system, and then we describe the practical application.     

Flexible multibody simulation using hybrid integration scheme

One issue in the dynamic simulation of flexible multibody system is poor computation efficiency, which is due to high frequency components in the solution associated with a deformable body. Standard explicit numerical methods should take very small time steps in order to satisfy the absolute stability condition for the high frequency components and, in turn, the computational efficiency deteriorates. In this study, a hybrid integration scheme is applied to solve the equations of motion of a flexible multibody system for achieving better computational efficiency. The computation times and simulation results are compared between the hybrid scheme and conventional methods. The results demonstrate that the efficiency of a flexible multibody simulation can be improved by using the hybrid scheme.     

A quantile-based simulation optimization model for sizing hybrid renewable energy systems

As the public has gradually realized the adverse impacts brought by global warming, hybrid renewable energy system (HRES) has become increasingly popular because it can reduce dependence on fossil fuels, while maintaining the stability of power supply. While the HRES is an attractive option in many aspects, the fundamentally uncertain nature of renewable energy sources makes the determination of the proper sizing of the HRES a very challenging task. Contrasting with the existing models that are largely focused on expectation-based system performance, this paper provides a quantile-based simulation optimization model, followed by the development of an efficient solution methodology, to enable the control of the upside risk and, as a result, to enhance the decision quality regarding the sizing of HRES. One advantage of the proposed model is that they can be based on any existing deterministic model that carries a cost structure regarding the sizing of the HRES. Moreover, the proposed solution methodology, consisting of a Monte Carlo simulation method, quantile estimation techniques, and an efficient stochastic optimizer, allows for not only accurate estimation of the objective function value, but also quick identification of the optimal solution due to a uniquely-defined neighborhood structure. An extensive numerical experiment is conducted to verify the efficacy and efficiency of the proposed solution methodology. Finally, in collaboration with a partner in industry, the proposed model and the solution methodology are integrated into a decision support system to provide visualized results for sizing HRES in practice.     

A genetic-based neuro-fuzzy approach for modeling and control ofdynamical systems

Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GAs). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of a FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.     

A massively parallel hybrid scheme for direct numerical simulation of turbulent viscoelastic channel flow

This paper describes in detail a numerical scheme designed for direct numerical simulation (DNS) of turbulent drag reduction. The hybrid spatial scheme includes Fourier spectral accuracy in two directions and sixth-order compact finite differences for first and second-order wall-normal derivatives, while time marching can be up to fourth-order accurate. High-resolution and high-drag reduction viscoelastic DNS are made possible through domain decomposition with a two-dimensional MPI Cartesian grid alternatively splitting two directions of space (‘pencil’ decomposition). The resulting algorithm has been shown to scale properly up to 16384 cores on the Blue Gene/P at IDRIS–CNRS, France.Drag reduction is modeled for the three-dimensional wall-bounded channel flow of a FENE-P dilute polymer solution which mimics injection of heavy-weight flexible polymers in a Newtonian solvent. We present results for four high-drag reduction viscoelastic flows with friction Reynolds numbers Re_τ0 = 180, 395, 590 and 1000, all of them sharing the same friction Weissenberg number We_τ0 = 115 and the same rheological parameters. A primary analysis of the DNS database indicates that turbulence modification by the presence of polymers is Reynolds-number dependent. This translates into a smaller percent drag reduction with increasing Reynolds number, from 64% at Re_τ0 = 180 down to 59% at Re_τ0 = 1000, and a steeper mean current at small Reynolds number. The Reynolds number dependence is also visible in second-order statistics and in the vortex structures visualized with iso-surfaces of the Q-criterion.     

A hybrid scheme for action representation

Strong deficiencies are present in symbolic models for action representation and planning, regarding mainly the difficulty of coping with real, complex environments. These deficiencies can be attributed to several problems, such as the inadequacy in coping with incompletely structured situations, the difficulty of interacting with visual and motorial aspects, the difficulty in representing low-level knowledge, the need to specify the problem at a high level of detail, and so on. Besides the purely symbolic approaches, several nonsymbolic models have been developed, such as the recent class of subsym-bolic techniques. A promising paradigm for the modeling of reasoning, which combines features of both symbolic and analogical approaches, is based on the construction of analogical models of the reference for the internal representations, as introduced by Johnson-Laird. In this work, we propose a similar approach to the problem of knowledge representation and reasoning about actions and plans. We propose a hybrid approach, symbolic and analogical, in which the inferences are partially devolved to measurements on analogical models generated starting from the symbolic representation. the interaction between the symbolic and the analogical level is due to the fact that procedures are connected to some symbols, allowing generating, updating, and verifying the mental model. the hybrid model utilizes, for the symbolic component, a representation system based on the distinction between terminological and assertional knowledge. the terminological component adopts a SI-Net formalism, extended by temporal primitives. the assertional component is a subset of first-order logics. the analogical representation is a set of concurrent procedures modeling parts of the world, action processes, simulations, and metaphors based on force fields concepts. A particular case study, regarding the problem of the assembly of a complex object from parts, is taken as an experimental paradigm. © 1993 John Wiley Sons, Inc.     

A compact design scheme of adaptive output-feedback control for uncertain nonlinear systems

Multiple uncertainties/noises, frequently exist in a plant, usually require multiple compensation techniques, which renders feedback controllers highly dynamic and nonlinear. This motivates us to search for a compact design scheme of compensation to reduce the complexity of controllers. In this paper, global output-feedback stabilisation is investigated for a class of uncertain nonlinear systems with unknown unmeasured states-dependent growth and input matching uncertainty. To solve the problem, a compact scheme is proposed to design a global adaptive output-feedback controller, which combines the technique of dynamic gain and extended state observer together. Particularly, only one dynamic gain, rather than two dynamic gains, is introduced to deal with the unknown polynomial-of-output growth rate, which makes our controller to have lower dynamics than those in the related works. Moreover, the input matching uncertainty is asymptotically estimated by the extended state observer, and thus its effect is well counteracted. It is shown that, under the designed controller, the system states globally converge to zero. A simulation example on non-zero set-point regulation demonstrates the effectiveness of the proposed approach.     

A neuro-fuzzy model for a new hybrid integrated Process Planning and Scheduling system

In customized mass production, isolation of Process Planning (PP) and Scheduling stages has a critical effect on the efficiency of production. In this study, to overcome this isolation problem, we propose an integrated system that does PP and Scheduling in parallel and responds to fluctuations in job floor on time. One common problem observed in integration models is the increase in computational time in conjunction with the increase of problem size. Therefore in this study, we use a hybrid heuristic model combining both Genetic Algorithm (GA) and Fuzzy Neural Network (FNN). To improve GA performance and increase the efficiency of searching, we use a clustered chromosome structure and test the performance of GA with respect to different scenarios. Data provided by GA is used in constructing an FNN model that instantly provides new schedules as new constraints emerge in the production environment. Introduction of fuzzy membership functions in Artificial Neural Network (ANN) model allows us to generate fuzzy rules for production environment.