Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The hws for model updating and the control hws for the neural adaptive controller are derived from Lyaptmov stability theorem, therefore the semi - global stability of the closed-loop system is guaranteed. At last, the simulation results are illuswated.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model （GFHM）. Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

Dissipative-based adaptive neural control for nonlinear systems

A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.     

Robust adaptive control for interval time-delay systems

1 Introduction The problem of time-delay is commonly encountered in various engineering systems, such as electric, pneumatic and hydraulic networks, long transmission lines, etc., and it is also a great source of systems instability and poor perfor- mance. During the last decades, we have seen an increasing interest for the control of this class of systems and many results have reported in the literature [1～5]. On the other hand, it has been recognized that nonlinear uncertainties are unavoid…     

An approximation-based load-balancing algorithm with admission control for cluster web servers with dynamic workloads

The growth of web-based applications in business and e-commerce is building up demands for high performance web servers for better throughputs and lower user-perceived latency. These demands are leading to a widespread substitution of powerful single servers by robust newcomers, cluster web servers, in many enterprise companies. In this respect the load-balancing algorithms play an important role in boosting the performance of cluster servers. The previous load-balancing algorithms which were designed for the handling of static contents in web services suffer from significant performance degradation under dynamic and database-driven workloads. Regarding this, we propose an approximation-based load-balancing algorithm with admission control for cluster-based web servers in this study. Since it is difficult to accurately determine the loads of web servers through feedbacks from distributed agents in web servers, we propose an analytical model of a web server to estimate the web servers’ loads. To achieve this, the algorithm classifies requests based on their service times and track numbers of outstanding requests for each class of each web server node and also based on their resource demands to dynamically estimate the loads of each node. For the error handling of the model a proportional integral (PI) controller from control theory is used. Then the estimated available capacity of each web server is used for load balancing and admission control decisions. The implementation results with a standard benchmark confirm the effectiveness of the proposed scheme, which improves both the mean response time and the throughput of the cluster compared to rival load-balancing algorithms, and also avoids situations in which the cluster is overloaded, even when the request rates are beyond the cluster capacity.     

Direct adaptive control for linear multivariable systems†

The aim of this paper is to survey direct adaptive control for linear multivariable systems. The discrete-time case is emphasized because of the increasing use of digital computers. Some adaptive control structures are given, as well as the different assumptions made on the process in order to achieve some specified objectives. Trends, new solutions and open problems are also discussed.     

Suboptimization techniques for improving transients in adaptive control†

Two possible optimization techniques for on-line adjustment of the design parameters involved in the adaptation algorithms of adaptive control schemes for minimum phase plants arc discussed. Sensitivity corrections adapled to this particular problem are introduced for correcting inaccuracies in an auxiliary model derived in order to be able to apply classical optimization techniques to the whole scheme. The main objective of such techniques is to improve the adaptation transient performances. The resulting strategies are discussed from the point of view of performance and possible implementation. Simulations illustrate the feasibility of the proposed optimizing procedures which are an extension, using a more general optimization theory and/or a sensitivity approach, of previous results and an alternative to the adaptive sampling approach of De la Sen (1984 c).     

A Youla–Kucera parametrized adaptive feedforward compensator for active vibration control with mechanical coupling

Most of the adaptive feedforward vibration (or noise) compensation systems feature an internal ”positive feedback” coupling between the compensator system and the correlated disturbance measurement which serves as reference. This may lead to the instability of the system. Instead of the standard IIR structure for the adaptive feedforward compensator, the paper proposes a Youla–Kucera parametrization of the adaptive compensator. The central compensator assures the stability of the system and its performances are enhanced in real time by the direct adaptation of the Youla–Kucera parameters. Theoretical and experimental comparison with recent results obtained using an IIR adaptive feedforward compensator are provided.     

A supervised Actor–Critic approach for adaptive cruise control

A novel supervised Actor–Critic (SAC) approach for adaptive cruise control (ACC) problem is proposed in this paper. The key elements required by the SAC algorithm namely Actor and Critic, are approximated by feed-forward neural networks respectively. The output of Actor and the state are input to Critic to approximate the performance index function. A Lyapunov stability analysis approach has been presented to prove the uniformly ultimate bounded property of the estimation errors of the neural networks. Moreover, we use the supervisory controller to pre-train Actor to achieve a basic control policy, which can improve the training convergence and success rate. We apply this method to learn an approximate optimal control policy for the ACC problem. Experimental results in several driving scenarios demonstrate that the SAC algorithm performs well, so it is feasible and effective for the ACC problem.     

The multi-objective optimal control with X?Q adaptive controller

This paper proposes a new type of nonlinear controllers and a large phase angle allowance design method based on the multi-objective optimal control system. With the proposed method, the performance of the system becomes better than that of the original system. Then, an example of the radar servo system is designed with a large phase angle allowance multi-objective optimal design method. Finally, the performance based on computer simulation demonstrates that the multi-objective optimal system is superior to linear optimal systems.     

Adaptive output feedback stabilisation for planar nonlinear systems with unknown control coefficients

The paper is concerned with the global adaptive stabilisation via output feedback for a class of uncertain planar nonlinear systems. Remarkably, the unknowns in the systems are rather serious: the control coefficients are unknown constants which do not belong to any known interval, and the growth of the systems heavily depends on the unmeasured states and has the rate of unknown polynomial of output. First, a delicate state transformation is introduced to collect the unknown control coefficients, and subsequently, a suitable state observer is successfully designed with two different dynamic gains. Then, an adaptive output feedback controller is proposed by flexibly combining the universal control idea and the backstepping technique. Meanwhile, an appropriate estimation law is constructed to overcome the negative effect caused by the unknown control coefficients. It is shown that, with the appropriate choice of the design parameters, all the states of the resulting closed-loop system are globally bounded, and furthermore, the states of the original system converge to zero.     

High-gam adaptive regulator for a string equation with uncertain harmonic disturbance under boundary output feedback control

This paper considers the boundary stabilization and parameter estimation of a one-dimensional wave equation in the case when one end is fixed and control and hamaomc disturbance with uncertain amplitude are input at another end. A high-gain adaptive regulator is designed in terms of measured collocated end velocity. The existence and uniqueness of the classical solution ofthe closed-loop system is proven. It is shown that the state of the system approaches the standstill as time goes to infinity and mean-while, the estimated parameter converges to the unknown parameter.     

H ∞ Control for uncertain system with time-delay and nonlinear external disturbance via adaptive control method

This paper considers the H _∞ control problem for the uncertain time delay system with nonlinear external disturbance. Given a system containing nonlinear external disturbance, our purpose is to design a robust controller such that the uncertain system is asymptotically stable with a generalized H _∞ disturbance attenuation level ρ. In this paper we propose a new approach to design the controller for the uncertain system which is composed of a linear controller and an adaptive controller. The problem is solved by introducing a switching function and using the idea of formulation. Based on Lyapunov stability theory, new sufficient conditions are obtained in terms of linear matrix inequalities. The effectiveness and advantages of the proposed controller design are shown via a numerical example.     

Model-based control architecture for attentive robots in rescue scenarios

In this paper we present a control architecture for an autonomous rescue robot specialized in victim finding in an unknown and unstructured environment. The reference domain for rescue robots is the rescue-world arenas purposefully arranged for the Robocup competitions. The main task of a rescue mobile robot is to explore the environment and report to the rescue-operators the map of visited areas annotated with its finding. In this context all the attentional activities play a major role in decision processes: salient elements in the environment yield utilities and objectives. A model-based executive controller is proposed to coordinate, integrate, and monitor the distributed decisions and initiatives emerging from the modules involved in the control loop. We show how this architecture integrates the reactive model-based control of a rescue mission, with an attentive perceptual activity processing the sensor and visual stimuli. The architecture has been implemented and tested in real-world experiments by comparing the performances of metric exploration and attentive exploration. The results obtained demonstrate that the attentive behavior significantly focus the exploration time in salient areas enhancing the overall victim finding effectiveness.

Pinning control for cluster synchronisation of complex dynamical networks with semi-Markovian jump topology

In this paper, a pinning controller is designed for cluster synchronisation of complex dynamical networks with semi-Markovian jump topology, which covers the concept of the conventional Markovian jump as a special case. To decide on the pinned-node for the cluster synchronisation, a novel procedure is proposed based on a topological structure. In addition, criteria for the synchronisation are separately derived for the controller with the mode-independent and mode-dependent pinned-node, respectively. A practical example is given to illustrate the effectiveness of the proposed methods.     

Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation

Modeling the long-tailedness property of network traffic with phase-type distributions is a powerful means to facilitate the consequent performance evaluation and queuing based analysis. This paper improves the recently proposed Fixed Hyper-Erlang model (FHE) by introducing an adaptive framework (Adaptive Hyper-Erlang model, AHE) to determine the crucially performance-sensitive model parameters. The adaptive model fits long-tailed traffic data set directly with a mixed Erlang distribution in a new divide-and-conquer manner. Compared with the well-known hyperexponential based models and the Fixed Hyper-Erlang model, the Adaptive Hyper-Erlang model is more flexible and practicable in addition to its accuracy in fitting the tail behavior.

Sampled-data H ∞ control for networked control systems with digital control inputs

In this article, the problem of sampled-data H _∞ control for networked control systems (NCSs) with digital control inputs is considered, where the physical plant is modelled as a continuous-time one, and the control inputs are discrete-time signals. By exploiting a novel Lyapunov–Krasovskii functional, using the Leibniz–Newton formula and a free-weighting matrix method, sufficient conditions for sampled-data H _∞ performance analysis and H _∞ controller design for such systems are given. Since the obtained conditions of H _∞ controller design are not expressed strictly in term of linear matrix inequalities, the sampled-data H _∞ controller is solved using modified cone complementary linearisation algorithm. In addition, the new sampled-data stability criteria for the NCSs is proved to be less conservative than some existing results. Numerical examples demonstrate the effectiveness of the proposed methods.     

Solution verification,goal-oriented adaptive methods for stochastic advection–diffusion problems

A goal-oriented analysis of linear, stochastic advection–diffusion models is presented which provides both a method for solution verification as well as a basis for improving results through adaptation of both the mesh and the way random variables are approximated. A class of model problems with random coefficients and source terms is cast in a variational setting. Specific quantities of interest are specified which are also random variables. A stochastic adjoint problem associated with the quantities of interest is formulated and a posteriori error estimates are derived. These are used to guide an adaptive algorithm which adjusts the sparse probabilistic grid so as to control the approximation error. Numerical examples are given to demonstrate the methodology for a specific model problem.