Comment on ‘Stability analysis and controller synthesis for discrete linear time-delay systems with state saturation nonlinearities’

A recently reported paper (Ji, X., Liu, T., Sun, Y., and Su, H. (2011), ‘Stability analysis and controller synthesis for discrete linear time-delay systems with state saturation nonlinearities’, International Journal of Systems Science, 42, 397–406) for the global asymptotic stability analysis and controller synthesis for a class of discrete linear time delay systems employing state saturation nonlinearities is reviewed. It is claimed in Ji, Liu, Sun and Su (2011) that a previous approach by Kandanvli and Kar (Kandanvli, V.K.R and Kar, H. (2009), ‘Robust stability of discrete-time state-delayed systems with saturation nonlinearities: Linear matrix inequality approach’, Signal Processing, 89, 161–173) is recovered from their approach as a special case. It is shown that this claim is not justified.     

Stability analysis and design of fuzzy control system with bounded uncertain delays

Fuzzy control problems for systems with bounded uncertain delays were studied. Based on Lyapunov stability theory and matrix theory, a nonlinear state feedback fuzzy controller was designed by linear matrix inequalities （LMI） approach, and the global exponential stability of the closed-loop system was strictly proved. For a fuzzy control system with bounded uncertain delays, under the global exponential stability condition which is reduced to p linear matrix inequalities, the controller guarantees stability performances of state variables. Finally, the simulation shows the validity of the method in tiffs paper.     

Simultaneous design of optimal gait pattern and controller for a bipedal robot

Nowadays, biped robotics becomes an interesting topic for many control researchers. The biped robot is more adaptable than the other mobile robots in a varied environment and can have more diverse possibilities in planning the motion. However, it falls down easily and its control for stable walking is difficult. Therefore, generation of a desired walking pattern for the biped robot in the presence of some model uncertainties is an important problem. The proposed walking pattern should be also achievable by the designed controller. To achieve this aim and to reach the best control performance, the walking pattern and controller should be designed simultaneously rather than separately. In the present study, an optimal walking pattern is proposed to be tracked by a designed sliding mode controller. In this respect, a genetic algorithm (GA) is utilized to determine the walking pattern parameters and controller coefficients simultaneously. Here, high stability, minimum energy consumption, good mobility properties, and actuator limitations are considered as the important indexes in optimization. Simulation results indicate the efficiency of the proposed scheme in walking the understudy biped robot.     

Maximum sensitivity based fractional IMC–PID controller design for non-integer order system with time delay

A simple approach with a small number of tuning parameters is a key goal in fractional order controller design. Recently there have been a number of limited attempts to bring about improvements in these areas. In this paper, a new design method for a fractional order PID controller based on internal model control (IMC) is proposed to handle non-integer order systems with time delay. In order to reduce the number of tuning parameters and mitigate the impact of time delay, the fractional order internal model control scheme is used. Considering the robustness of the control system with respect to process variations and model uncertainty, maximum sensitivity is applied to the tuning of the parameters. The resulting controller has the structure of a fractional order PID which is cascaded with a filter. This is named a fractional IMC–PID controller. Numerical results are given to show the efficiency of the proposed controller.     

New approach to stochastic stability and controller design for networked control systems

This paper addresses the random time-delays and packet losses issues of networked control systems （NCS） within the framework of jump linear systems with mode-dependent time-delays. A new delay-dependent condition on the stochastic stability is proposed by a new stochastic Lyapunov-Krasovskii functional. The condition is formulated as a set of coupled linear matrix inequalities （LMIs）. As an example to verify the proposed method, an inverted-pendulum system with network is considered. The simulation results demonstrate the effectiveness of the method.     

Development and application of a model for analysis and design phases of Web-based system development

The major characteristics of Web systems that shall be taken into account can be summarized as follows: First, the implementation of Web systems shall have a beneficial effect. To meet the requirements of Internet business system, the Web systems shall enable businesses to provide customers with something valuable through the Internet and profit from this process in return[1]. Second, the way to express the contents on the Web matters. To express the contents, Web systems shall introduce mu…     

Digital controller design for Bouc–Wen model with high-order hysteretic nonlinearities through approximated scalar sign function

In the models of piezoelectric actuator (PEA) systems, the hysteresis effects are often described by the concise Bouc–Wen models; however, these models are nonlinear and non-smooth because of the existence of terms which involve the absolute-value function. In particular, a challenging control problem is posed when, due to the properties of certain materials, such absolute-value terms are of high order. This control problem has been rarely studied. This article proposes the use of the approximated scalar sign function (ASSF), which is a numerically stable and differentiable nonlinear function, to represent the hysteresis function, with single-order or high-order absolute-value terms. This innovative step leads to a nonlinear but sufficiently smooth model. Then, a systematic digital design methodology is presented, which involves the following steps: (1) establish an optimal linear model based upon the resulting smooth model, (2) adopt a PI-based analogue controller and (3) apply the prediction-based digital redesign technique for digital implementation. A digital observer is also developed for state reconstruction, and to improve the input disturbance rejection. The positioning control of a PEA system with a high-order hysteretic Bouc–Wen model is implemented to demonstrate the effectiveness of the proposed ASSF based modelling and controller design approaches.     

PD–PID controller for delayed systems with two unstable poles: a frequency domain approach

This paper deals with the stability problem of linear delayed systems containing two unstable real poles by means of PD controllers. The analysis presented is based on frequency domain techniques. Necessary and sufficient conditions for the existence of stabilising controllers are given in terms of the parameters of the system and the time delay size. The main result is extended to delayed systems with two unstable poles and n stable real poles. PID controllers are also considered in order to control the studied systems, obtaining similar stability conditions. Numerical examples are presented in order to illustrate the control performance.     

Stability analysis of a model reference adaptive control system with sinusoidal inputs†

In this paper a rigorous method is presented for analysing an M.I.T. type model reference adaptive control system with sinusoidal inputs. The linearized equations for the adapting system, formed by using small perturbation analysis, are written in the matrix form [xdot] = A(t)x, where A(t) is periodic. This matrix equation is then integrated over one period using a Runge–Kutta technique. The transition matrix relating the value of x at the end of a period to its value at the beginning of the period is examined to see whether all its eigenvalues are within the unit circle, thus establishing stability.     

Delay-dependent H ∞ controller design for linear neutral systems with discrete and distributed delays

This article considers the delay-dependent H _∞ control problem for linear neutral systems with both discrete and distributed delays. The problem we address is to design a state feedback controller such that the resulting closed-loop system is asymptotically stable and satisfies a prescribed H _∞ performance level. First, a delay-dependent sufficient condition for the solvability of the problem is obtained in terms of matrix inequalities. Then, by using the cone complementarity linearization approach, an H _∞ controller is developed based on the solvability condition. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed method.     

Fuzzy predictive control based multiple models strategy for a tubular heat exchanger system

This work deals with the problem of controlling the outlet temperature of a tubular heat exchanger system by means of flow pressure. The usual industrial case is to try to control the outlet temperature by either the temperature or the flow of the fluid, which flows through the shell tube. But, in some situations, this is not possible, due to the fact that the whole of system coefficients variation cannot quite be covered by control action. In this case, the system behavior must precisely be modeled and appropriate control action needs to be obtained based on novel techniques. A new multiple models control strategy using the well-known linear generalized predictive control (LGPC) scheme has been proposed, in this paper. The main idea of the proposed control strategy is to represent the operating environments of the system, which have a wide range of variation with respect to time by multiple explicit linear models. In this strategy, the best model of the system is accurately identified, at each instant of time, by an intelligent decision mechanism (IDM), which is organized based on both new recursive weight generator and fuzzy adaptive Kalman filter approaches. After that, the adaptive algorithm is implemented on the chosen model. Finally, for having a good tracking performance, the generalized predictive control is instantly updated and its control action is also applied to the system. For demonstrating the effectiveness of the proposed approach, simulations are all done and the results are also compared with those obtained using a nonlinear GPC (NLGPC) approach that is realized based on the Wiener model of the system. The results can verify the validity of the proposed control scheme.     

Language and Space: a two-level semantic approach based on principles of ontological engineering

An increasing number of applications for dialogue systems presuppose an ability to deal appropriately with space. Dialogues with assistance systems, intelligent mobility devices and navigation systems all commonly involve the use of spatial language. For smooth interaction, this spatial language cannot be interpreted ‘in the abstract’—it must instead be related directly to a user’s physical location, orientation, goals and needs and be embedded appropriately in a system’s interaction. This is far from straightforward. The situated interpretation of natural language concerning space, spatial relationships and spatial activities represents an unsolved challenge at this time. Despite extensive work on spatial language involving many disciplines, there are no generally accepted accounts that provide support for the kind of flexible language use observed in real human-human spatial dialogues. In this paper, I review some recent approaches to the semantics for natural language expressions concerning space in order to motivate a two-level semantic-based approach to the interpretation of spatial language. This draws on a new combination of natural language processing and principles of ontological engineering and stands as a foundation for more sophisticated and natural dialogue system behavior where spatial information is involved.     

H∞ iterative learning controller design for a class of discrete-time systems with data dropouts

In this paper, the issue of H_∞ iterative learning controller design is considered for a class of discrete-time systems with data dropouts. With the super-vector formulation of iterative learning control (ILC), such a system can be formulated as a linear discrete-time stochastic system in the iteration domain, and then a sufficient condition guaranteeing both stability of the ILC process and the desired H_∞ performance in the iteration domain is presented. The condition can be derived in terms of linear matrix inequalities that can be solved by using existing numerical techniques. A numerical simulation example is also included to validate the theoretical results.     

Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

Based on high order dynamic neural network, this paper presents the tracking problem for uncertain nonlinear composite system, which contains external disturbance, whose nonlinearities are assumed to be unknown. A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the dosed loop. Certain measures are utilized to test its performance. No a priori knowledge of an upper bound on the “optimal” weight and modeling error is required; the weights of neural networks are updated on-line. Numerical simulations performed on a simple example illustrate and clarify the approach.     

Prediction-based sampled-data H∞ controller design for attitude stabilisation of a rigid spacecraft with disturbances

This paper investigates the H_∞ control problem of the attitude stabilisation of a rigid spacecraft with external disturbances using prediction-based sampled-data control strategy. Aiming to achieve a ‘virtual’ closed-loop system, a type of parameterised sampled-data controller is designed by introducing a prediction mechanism. The resultant closed-loop system is equivalent to a hybrid system featured by a continuous-time and an impulsive differential system. By using a time-varying Lyapunov functional, a generalised bounded real lemma (GBRL) is first established for a kind of impulsive differential system. Based on this GBRL and Lyapunov functional approach, a sufficient condition is derived to guarantee the closed-loop system to be asymptotically stable and to achieve a prescribed H_∞ performance. In addition, the controller parameter tuning is cast into a convex optimisation problem. Simulation and comparative results are provided to illustrate the effectiveness of the developed control scheme.     

A multi-expert system for ranking patents: An approach based on fuzzy pay-off distributions and a TOPSIS–AHP framework

The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed.