Volume 9, Number 4, 2012
Systems with large operating regions and non-zero state target tracking have limited the industrial application of robust model predictive control (RMPC) with synthetic action. To overcome the problem, this paper presents a novel formulation of synthesizing scheduled RMPC for linear time varying (LTV) systems. Off-line, we compute the matrix that transforms target output into steady state first. Then a set of stabilizing state feedback laws which are corresponding to a set of estimated regions of stability covering the desired operating region are provided. On-line, these control laws are implemented as a single scheduled state feedback model predictive control (MPC) which switches between the set of local controllers and achieve the desired target at last. Finally, the algorithm is illustrated with an example.
This paper addresses an integrated relative position and attitude control strategy for a pursuer spacecraft flying to a space target in proximity operation missions. Relative translation and rotation dynamics are both presented, and further integratedly considered due to mutual couplings, which results in a six degrees-of-freedom (6-DOF) control system. In order to simultaneously achieve relative position and attitude requirements, an adaptive backstepping control law is designed, where a command filter is introduced to overcome "explosion of terms". Within the Lyapunov framework, the proposed controller is proved to ensure the ultimate boundedness of relative position and attitude signals, in the presence of external disturbances and unknown system parameters. Numerical simulation demonstrates the effect of the designed control law.
Quantized H∞ fault-tolerant control for networked control systems (NCSs) with partial actuator fault with respect to actuators is concerned in this paper. Considering transmission delay, packet dropout and quantization, a synthesis model with partial actuator fault is established. The piecewise constant controller is adopted to model NCS with the transmission delay and packet dropout. Due to data transmitted in practical NCSs should be quantized before they are sent to the next network node, the logarithmic static and time-invariant quantizers at the sensor and controller sides are proposed in the paper. For the established model, an appropriate type of Lyapunov functions is provided to investigate the delay-dependent H∞ control problem. According to an optimal problem, the controller that makes the system achieve the best performance is designed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.
This paper presents a novel nonlinear continuous-time observer based on the differential state-dependent Riccati equation (SDRE) filter with guaranteed exponential stability. Although impressive results have rapidly emerged from the use of SDRE designs for observers and filters, the underlying theory is yet scant and there remain many unanswered questions such as stability and convergence. In this paper, Lyapunov stability analysis is utilized in order to obtain the required conditions for exponential stability of the estimation error dynamics. We prove that under specific conditions, the proposed observer is at least locally exponentially stable. Moreover, a new definition of a detectable state-dependent factorization is introduced, and a close relation between the uniform detectability of the nonlinear system and the boundedness property of the state-dependent differential Riccati equation is established. Furthermore, through a simulation study of a second order nonlinear model, which satisfies the stability conditions, the promising performance of the proposed observer is demonstrated. Finally, in order to examine the effectiveness of the proposed method, it is applied to the highly nonlinear flux and angular velocity estimation problem for induction machines. The simulation results verify how effectively this modification can increase the region of attraction and the observer error decay rate.
This paper investigates L2-gain analysis and anti-windup compensation gains design for a class of discrete-time switched systems with saturating actuators and L2 bounded disturbances by using the switched Lyapunov function approach. For a given set of anti-windup compensation gains, we firstly give a sufficient condition on tolerable disturbances under which the state trajectory starting from the origin will remain inside a bounded set for the corresponding closed-loop switched system subject to L2 bounded disturbances. Then, the upper bound on the restricted L2-gain is obtained over the set of tolerable disturbances. Furthermore, the anti-windup compensation gains aiming to determine the largest disturbance tolerance level and the smallest upper bound of the restricted L2-gain are presented by solving a convex optimization problem with linear matrix inequality (LMI) constraints. A numerical example is given to illustrate the effectiveness of the proposed design method.
A primary goal of broadcasting in vehicular ad hoc network (VANET) is to improve the road safety by transmitting alert messages to all surrounding vehicles as soon as possible. In this paper, we adopt the concept of opportunistic routing and propose a multiple candidate relays opportunistic broadcast (MCROB) protocol for VANET. The MCROB protocol is a sender-driven broadcast scheme independent of node density. The packet delivery ratio (PDR) is derived and an expected transmission speed (ETS) for the MCROB is proposed. A priority rule for selecting a proper candidate relay and an adaptive algorithm for forwarding timers of candidate relays are also presented in this paper. Simulations show that MCROB is adaptive to the rapid changing of network conditions. It keeps a low communication overhead introduced by the broadcast and increases the average transmission speed by around 40%.
In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.
To meet the future internet traffic challenges, enhancement of hardware architectures related to network security has vital role where software security algorithms are incompatible with high speed in terms of Giga bits per second (Gbps). In this paper, we discuss signature detection technique (SDT) used in network intrusion detection system (NIDS). Design of most commonly used hardware based techniques for signature detection such as finite automata, discrete comparators, Knuth-Morris-Pratt (KMP) algorithm, content addressable memory (CAM) and Bloom filter are discussed. Two novel architectures, XOR based pre computation CAM (XPCAM) and multi stage look up technique (MSLT) Bloom filter architectures are proposed and implemented in third party field programmable gate array (FPGA), and area and power consumptions are compared. 10Gbps network traffic generator (TNTG) is used to test the functionality and ensure the reliability of the proposed architectures. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of performance, speed and power-efficiency.
This paper presents the result of experiments conducted in mesh networks on different routing algorithms, traffic generation schemes and switching schemes. A new network on chip (NoC) topology based on partial interconnection of mesh network is proposed and a routing algorithm supporting the proposed architecture is developed. The proposed architecture is similar to standard mesh networks, where four extra bidirectional channels are added which remove the congestion and hotspots compared to standard mesh networks with fewer channels. Significant improvement in delay (60% reduction) and throughput (60% increase) was observed using the proposed network and routing when compared with the ideal mesh networks. An increase in number of channels makes the switches expensive and could increase the area and power consumption. However, the proposed network can be useful in high speed applications with some compromise on area and power.
This paper proposes a bargaining game theoretic resource (including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access (OFDMA) networks. We define a wireless user's payoff as a function of the achieved data-rate. The fairness resource allocation problem can then be modeled as a cooperative bargaining game. The objective of the game is to maximize the aggregate payoffs for the users. To search for the Nash bargaining solution (NBS) of the game, a suboptimal subcarrier allocation is performed by assuming an equal power allocation. Thereafter, an optimal power allocation is performed to maximize the sum payoff for the users. By comparing with the max-rate and the max-min algorithms, simulation results show that the proposed game could achieve a good tradeoff between the user fairness and the overall system performance.
It is well known that the performance of conventional adaptive beamformers degrades severely due to the presence of coherent or correlated interferences (multipath propagation) and various techniques have been developed to improve the performance of the beamformer. However, most of the work in the past has been focused on the narrowband case. In this paper, the wideband beamforming problem in the presence of multipath signals is addressed, with a novel approach proposed by employing a pre-processing stage based on the frequency invariant beamforming (FIB) technique. In this approach, the received wideband array signals are first processed by an FIB network, and then a traditional narrowband adaptive beamformer or an appropriate instantaneous blind source separation (BSS) algorithm can be applied to the network outputs. It is shown that with the proposed structure, cancellation of the desired signal is reduced, leading to a significantly improved output signal to interference plus noise ratio (SINR).
Cloud computing is a new and rapidly emerging computing paradigm where applications, data and IT services are provided over the Internet. The task-resource management is the key role in cloud computing systems. Task-resource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities. Task-resource scheduling problem is NP-complete. In this paper, we consider an approach to solve this problem optimally. This approach is based on constructing a logical model for the problem. Using this model, we can apply algorithms for the satisfiability problem (SAT) to solve the task-resource scheduling problem. Also, this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.
The number of Internet users and the number of web pages being added to WWW increase dramatically every day. It is therefore required to automatically and efficiently classify web pages into web directories. This helps the search engines to provide users with relevant and quick retrieval results. As web pages are represented by thousands of features, feature selection helps the web page classifiers to resolve this large scale dimensionality problem. This paper proposes a new feature selection method using Ward's minimum variance measure. This measure is first used to identify clusters of redundant features in a web page. In each cluster, the best representative features are retained and the others are eliminated. Removing such redundant features helps in minimizing the resource utilization during classification. The proposed method of feature selection is compared with other common feature selection methods. Experiments done on a benchmark data set, namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.