Volume 6, Number 1, 2009
Special Issue on Control Systems Design Using the Method of Inequalities and the Principle of Matching (pp.1-37)
During transportation by ambulance, a patient is exposed to inertial acceleration when an ambulance decelerates or turns a corner Such acceleration often gives a patient physical stress such as blood pressure variation or body sway, which causes strong pain, feeling of discomfort or sometimes critical damage for seriously injured persons To reduce this undesirable effect of the acceleration, the authors developed the actively-controlled bed (ACB) which controls the posture of a stretcher in real time to reduce foot-to-head and lateral acceleration acting on a supine person This paper describes development of the ACB, including control system design and performance evaluation The control system is designed by Zakian s framework, which comprises the principle of matching and the method of inequalities, so that the design specifications on the tracking error and the motor torque are satisfied From the results of driving experiments and simulation, it is estimated that the ACB can reduce the acceleration acting on a patient by 65% in the foot-to-head direction and by 75% in the lateral direction.
Control systems designed by the principle of matching gives rise to problems of evaluating the peak output This paper proposes a practical method for computing the peak output of linear time-invariant and non-anticipative systems for a class of possible sets that are characterized with many bounding conditions on the two-and/or the infinity-norms of the inputs and their derivatives The original infinite-dimensional convex optimization problem is approximated as a large-scale convex programme deffned in a Euclidean space, which are associated with sparse matrices and thus can be solved effciently in practice The numerical results show that the method performs satisfactorily, and that using a possible set with many bounding conditions can help to reduce the design conservatism and thereby yield a better match.
This paper investigates the use of the method of inequalities (MoI) to design output-feedback compensators for the problem of the control of instabilities in a laminar plane Poiseuille flow In common with many flows, the dynamics of streamwise vortices in plane Poiseuille flow are very non-normal Consequently, small perturbations grow rapidly with a large transient that may trigger nonlinearities and lead to turbulence even though such perturbations would, in a linear flow model, eventually decay Such a system can be described as a conditionally linear system The sensitivity is measured using the maximum transient energy growth, which is widely used in the fluid dynamics community The paper considers two approaches In the first approach, the MoI is used to design low-order proportional and proportional-integral (PI) controllers In the second one, the MoI is combined with McFarlane and Glover s H loop-shaping design procedure in a mixed-optimization approach.
Methods based on numerical optimization are useful and effective in the design of control systems This paper describes the design of retarded fractional delay differential systems (RFDDSs) by the method of inequalities, in which the design problem is formulated so that it is suitable for solution by numerical methods Zakian s original formulation, which was first proposed in connection with rational systems, is extended to the case of RFDDSs In making the use of this formulation possible for RFDDSs, the associated stability problems are resolved by using the stability test and the numerical algorithm for computing the abscissa of stability recently developed by the authors During the design process, the time responses are obtained by a known method for the numerical inversion of Laplace transforms Two numerical examples are given, where fractional controllers are designed for a time-delay and a heat-conduction plants.
This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system (CDPS) The designed controller, which is based on the human-simulated intelligent control (HSIC) method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase For the approach, the original method of inequalities-based (MoI) multiobjective genetic algorithm (MMGA) is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.
Interconnection networks are hardware fabrics supporting communications between individual processors in multi-computers The low-dimensional k-ary n-cubes (or torus) with adaptive wormhole switching have attracted significant research efforts to construct high-performance interconnection networks in contemporary multi-computers The arrival process and destination dis- tribution of messages have great effects on network performance With the aim of capturing the characteristics of the realistic traffic pattern and obtaining a deep understanding of the performance behaviour of interconnection networks, this paper presents an analytical model to investigate the message latency in adaptive-routed wormhole-switched torus networks where there exists hot-spot nodes and the message arrivals follow a batch arrival process Each generated message has a given probability to be directed to the hot-spot node The average degree of virtual channel multiplexing is computed by the GE/G/1/V queueing system with finite buffer capacity We compare analytical results of message latency with those obtained through the simulation experiments in order to validate the accuracy of the derived model.
Saving energy and increasing network lifetime are significant challenges in wireless sensor networks (WSNs) In this paper, we propose a mechanism to distribute the responsibility of cluster-heads among the wireless sensor nodes in the same cluster based on the ZigBee standard, which is the latest WSN standard ZigBee supports ad hoc on-demand vector (AODV) and cluster-tree routing protocols in its routing layer However, none of these protocols considers the energy level of the nodes in the network establishing process or in the data routing process The cluster-tree routing protocol supports single or multi-cluster networks However, each single cluster in the multi-cluster network has only one node acting as a cluster head These cluster-heads are fixed in each cluster during the network lifetime Consequently, using these cluster-heads will cause them to die quickly, and the entire linked nodes to these cluster-heads will be disconnected from the main network Therefore, the proposed technique to distribute the role of the cluster head among the wireless sensor nodes in the same cluster is vital to increase the lifetime of the network Our proposed technique is better in terms of performance than the original structure of these protocols It has increased the lifetime of the wireless sensor nodes, and increased the lifetime of the WSN by around 50% of the original network lifetime.
A control area network (CAN) based multi-motor synchronized motion control system with an advanced synchronized control strategy is proposed The strategy is to incorporate the adjacent cross-coupling control strategy into the sliding mode control architecture As illustrated by the four-induction-motor-based experimental results, the multi-motor synchronized motion control system, via the CAN bus, has been successfully implemented With the employment of the advanced synchronized motion control strategy, the synchronization performance can be significantly improved.
This paper proposes one method of feature selection by using Bayes theorem The purpose of the proposed method is to reduce the computational complexity and increase the classification accuracy of the selected feature subsets The dependence between two attributes (binary) is determined based on the probabilities of their joint values that contribute to positive and negative classification decisions If opposing sets of attribute values do not lead to opposing classification decisions (zero probability), then the two attributes are considered independent of each other, otherwise dependent, and one of them can be removed and thus the number of attributes is reduced The process must be repeated on all combinations of attributes The paper also evaluates the approach by comparing it with existing feature selection algorithms over 8 datasets from University of California, Irvine (UCI) machine learning databases The proposed method shows better results in terms of number of selected features, classi?cation accuracy, and running time than most existing algorithms.
The aim of this paper is to develop a neuro-fuzzy-sliding mode controller (NFSMC) with a nonlinear sliding surface for a coupled tank system The main purpose is to eliminate the chattering phenomenon and to overcome the problem of the equivalent control computation A first-order nonlinear sliding surface is presented, on which the developed sliding mode controller (SMC) is based Mathematical proof for the stability and convergence of the system is presented In order to reduce the chattering in SMC, a fixed boundary layer around the switch surface is used Within the boundary layer, where the fuzzy logic control is applied, the chattering phenomenon, which is inherent in a sliding mode control, is avoided by smoothing the switch signal Outside the boundary, the sliding mode control is applied to drive the system states into the boundary layer Moreover, to compute the equivalent controller, a feed-forward neural network (NN) is used The weights of the net are updated such that the corrective control term of the NFSMC goes to zero Then, this NN also alleviates the chattering phenomenon because a big gain in the corrective control term produces a more serious chattering than a small gain Experimental studies carried out on a coupled tank system indicate that the proposed approach is good for control applications.
A new robust proportional-integral-derivative (PID) tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification The tracked object is the statistical information of a given target probability density function (PDF), rather than a deterministic signal Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities (LMI) algorithms are proposed to fulfil the tracking problem Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance Simulations are given to demonstrate the efficiency of the proposed approach.
This paper describes a supervisory hierarchical fuzzy controller (SHFC) for regulating pressure in a real-time pilot pressure control system The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral (PI) controller are analyzed The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods.
In this paper, a hybrid genetic algorithm (GA) is proposed for the traveling salesman problem (TSP) with pickup and delivery (TSPPD) In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring In addition, a local search procedure is integrated into the GA to accelerate convergence The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.
In this paper, the robust H control problem for uncertain discrete-time systems with time-varying state delay is con- sidered Based on the Lyapunov functional method, and by resorting to the new technique for estimating the upper bound of the difference of the Lyapunov functional, a new less conservative sufficient condition for the existence of a robust H controller is obtained Moreover, the cone complementary linearisation procedure is employed to solve the nonconvex feasibility problem Finally, several numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.