Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

Temperature prediction control based on least squares support vector machines

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.

Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input

This paper presents a sliding mode(SM) based identifier to deal with the parameter idenfification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system;an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.

Mathematical modeling of salt-gradient ion-exchange simulated moving bed chromatography for protein separations

The salt-gradient operation mode used in ion-exchange simulated moving bed chromatography (SMBC) can improve the efficiency of protein separations. A detailed model that takes into account any kind of adsorption/ion-exchange equilibrium, salt gradient, size exclusion, mass transfer resistance, and port periodic switching mechanism, was developed to simulate the complex dynamics. The model predictions were verified by the experimental data on upward and downward gradients for protein separations reported in the literature. All design and operating parameters (number, configuration, length and diameter of columns, particle size, switching period, flow rates of feed, raffinate, desorbent and extract, protein concentrations in feed, different salt concentrations in desorbent and feed) can be chosen correctly by numerical simulation. This model can facilitate the design, operation, optimization, control and scale-up of salt-gradient ion-exchange SMBC for protein separations.

An integrated DBP for streams with （m, k）-firm real-time guarantee

(m, k)-firm real-time or weakly hard real-time (WHRT) guarantee is becoming attractive as it closes the gap between hard and soft (or probabilistic) real-time guarantee, and enables finer granularity of real-time QoS through adjusting m and k. For multiple streams with (m, k)-firm constraint sharing a single server, an on-line priority assignment policy based on the most recent k-length history of each stream called distance based priority (DBP) has been proposed to assign priority.In case of priority equality among these head-of-queue instances, Earliest Deadline First (EDF) is used. Under the context of WHRT schedule theory, DBP is the most popular, gets much attention and has many applications due to its straightforward priority assignment policy and easy implementation. However, DBP combined with EDF cannot always provide good performance, mainly because the initial DBP does not underline the rich information on deadline met/missed distribution,specially streams in various failure states which will travel different distances to restore success states. Considering how to effectively restore the success state of each individual stream from a failure state, an integrated DBP utilizing deadline met/missed distribution is proposed in this paper. Simulation results validated the performance improvement of this pro-posal.

Liveness Problem of Petri Nets Supervisory Control Theory for Discrete Event Systems

A quite great progress of the supervisory control theory for discrete event systems (DES)has been made in the past nearly twenty years, and now, automata, formal language and Petri nets become the main research tools. This paper focus on the Petri nets based supervisory control theory of DES. Firstly, we review the research results in this field, and claim that there generally exists a problem in Petri nets based supervisory control theory of DES, that is, the deadlock caused by the controller introduced to enforce the given specification occurs in the closed-loop systems, especially the deadlock occurs in the closed-loop system in which the original plant is live. Finally, a possible research direction is presented for the solution of this problem.

Congestion control for ATM multiplexers using neural networks：multiple sources／single buffer scenario

A new neural network based method for solving the problem of congestion control arising at the user network interface (UNI) of ATM networks is proposed in this paper. Unlike the previous methods where the coding rate for all traffic sources as controller output signals is tuned in a body, the proposed method adjusts the coding rate for only a part of the traffic sources while the remainder sources send the cells in the previous coding rate in case of occurrence of congestion. The controller output signals include the source coding rate and the percentage of the sources that send cells at the corresponding coding rate. The control methods not only minimize the cell loss rate but also guarantee the quality of information (such as voice sources) fed into the multiplexer buffer. Simulations with 150 ADPCM voice sources fed into the multiplexer buffer showed that the proposed methods have advantage over the previous methods in the aspect of the performance indices such as cell loss rate (CLR) and voice quality.

New synthesis algorithm of static output feedback sliding mode control for a class of uncertain systems

Based on a kind of regular form, a Lyapunov matrix with special structure is presented to design the sliding surface matrix conveniently and then an effective algorithm is developed on it. A simple static output feedback sliding mode control law without extra dynamic equation is given, such that the predefined sliding surface is reached in finite time for the general matching uncertainties. In the reported result, this extra dynamic equation is used for evaluating the norm bound of the unmeasured state vector. Finally, some examples are studied to illustrate the proposed approach.

State Fusion Estimation for Multilevel Multisensor System

Based on the single sensor Kalman filtering equations, this paper presents two-level and three-level optimal centralized and distributed estimation algorithms for hierarchical multisensor systems. The solution shows that when the correlated matrix, the mean of noise, the control input, and the measurement error are all zero, the result in this paper turns out to be the standard algorithm discussed. Simulation shows that the mean of noise, the control input, and the measurement error will not change the estimation covariance and the estimation covariance fluctuates greatly when the cross-correlated matrix is similar to the covariance of process noise.