Predictive control using feedback linearization based on dynamic neural models

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.

Mixed initial-boundary value problem in particle modeling of microelectronic devices

The Boltzmann equation in presence of boundary and initial conditions, which describes the general case of carrier transport in microelectronic devices is analysed in terms of Monte Carlo theory. The classical Ensemble Monte Carlo algorithm which has been devised by merely phenomenological considerations of the initial and boundary carrier contributions is now derived in a formal way. The approach allows to suggest a set of event-biasing algorithms for statistical enhancement as an alternative of the population control technique, which is virtually the only algorithm currently used in particle simulators. The scheme of the self-consistent coupling of Boltzmann and Poisson equation is considered for the case of weighted particles. It is shown that particles survive the successive iteration steps.

Development of a wearable assistive soft robotic device for elbow rehabilitation

The loss of motor function at the elbow joint can result as a consequence of stroke. Stroke is a clinical illness resulting in long lasting neurological deficits often affecting somatosensory and motor cortices. More than half of those that recover from a stroke survive with disability in their upper arm and need rehabilitation therapy to help in regaining functions of daily living. In this paper, we demonstrated a prototype of a low-cost, ultra-light and wearable soft robotic assistive device that could aid administration of elbow motion therapies to stroke patients. In order to assist the rotation of the elbow joint, the soft modules which consist of soft wedge-like cellular units was inflated by air to produce torque at the elbow joint. Highly compliant rotation can be naturally realised by the elastic property of soft silicone and pneumatic control of air. Based on the direct visual-actuation control, a higher control loop utilised visual processing to apply positional control, the lower control loop was implemented by an electronic circuit to achieve the desired pressure of the soft modules by Pulse Width Modulation. To examine the functionality of the proposed soft modular system, we used an anatomical model of the upper limb and performed the experiments with healthy participants.

A novel technique for differentiation of proteins in the development of acid gel structure from control and heat treated milk using confocal scanning laser microscopy

The incorporation of caseins and whey proteins into acid gels produced from unheated and heat treated skimmed milk was studied by confocal scanning laser microscopy (CSLM) using fluorescent labelled proteins. Bovine casein micelles were labelled using Alexa Fluor 594, while whey proteins were labelled using Alexa Fluor 488. Samples of the labelled protein solutions were introduced into aliquots of pasteurised skim milk, and skim milk heated to 90 degrees C for 2 min and 95 degrees C for 8 min. The milk was acidified at 40 degrees C to a final pH of 4.4 using 20 g gluconodelta-lactone/l (GDL). The formation of gels was observed with CSLM at two wavelengths (488 nm and 594 nm), and also by visual and rheological methods. In the control milk, as pH decreased distinct casein aggregates appeared, and as further pH reduction occurred, the whey proteins could be seen to coat the casein aggregates. With the heated milks, the gel structure was formed of continuous strands consisting of both casein and whey protein. The formation of the gel network was correlated with an increase in the elastic modulus for all three treatments, in relation to the severity of heat treatment. This model system allows the separate observation of the caseins and whey proteins, and the study of the interactions between the two protein fractions during the formation of the acid gel structure, on a real-time basis. The system could therefore be a valuable tool in the study of structure formation in yoghurt and other dairy protein systems.

Impedance control of redundant drive joints with double actuation

This paper proposes impedance control of redundant drive joints with double actuation (RDJ-DA) to produce compliant motions with the future goal of higher bandwidth. First, to reduce joint inertia, a double-input-single-output mechanism with one internal degree of freedom (DOF) is presented as part of the basic structure of the RDJ-DA. Next, the basic structure of RDJ-DA is further explained and its dynamics and statics are derived. Then, the impedance control scheme of RDJ-DA to produce compliant motions is proposed and the validity of the proposed controller is investigated using numerical examples.

Cultural techniques for improvement in biocontrol potential of fungal antagonists for biological control of Armillaria root rot of strawberry plants under glasshouse conditions

Several in vitro and in vivo experiments were conducted to develop an effective technique for culturing potential fungal antagonists (isolates of Trichoderma harzianum, Dactylium dendroides, Chaetomium olivaceum and one unidentified fungus) selected for activity against Armillaria mellea. The antagonists were inoculated onto (1) live spawn of the oyster mu shroom (Pleurotus ostreatus), (2) extra-moistened or sucrose-enriched mushroom composts containing living or autoclaved mycelia of P. ostreatus or Agaricus bisporus (button mushroom), (3) pasteurized compost with or without A. bisporus mycelium, wheat bran, wheat germ and (4) spent mushroom composts with living mycelia of A. bisporus, P. ostreatus or Lentinus edodes (the Shiitake mushroom). In one experiment, a representative antagonist (isolate Th2 of T. harzianum) was grown together with the A. bisporus mycelium, while in another one, the antagonist was first grown on wheat germ or wheat bran and then on mushroom compost with living mycelium of A. bisporus. Some of the carrier substrates were then added to the roots of potted strawberry plants in the glasshouse to evaluate their effectiveness against the disease. The antagonists failed to grow on the spawn of P. ostreatus even after reinoculations and prolonged incubation. Providing extra moisture or sucrose enrichment also did not improve the growth of Th2 on mushroom composts in the presence of living mycelia of A. bisporus or P. ostreatus. The antagonist, however, grew rapidly and extensively on mushroom compost with autoclaved mycelia, and also on wheat germ and wheat bran. Colonization of the substrates by the antagonist was positively correlated with its effectiveness in the glasshouse studies. Whereas only 33.3% of the inoculated control plants survived in one experiment monitored for 560 days, 100% survival was achieved when Th2 was applied on wheat germ or wheat bran. Growth of the antagonist alone on pasteurized or sterilized compost (without A. bisporus mycelia) and simultaneous growth of the antagonist and mushroom on pasteurized compost did not improve survival over the inoculated controls, but growth over mushroom compost with the living mycelium resulted in 50% survival rate. C. olivaceum isolate Co was the most effective, resulting in overall survival rate of 83.3% compared with only 8.3% for the inoculated and 100% for the uninoculated (healthy) controls. This antagonist gave the highest survival rate of 100% on spent mushroom compost with L. edodes. T harzianum isolate Th23, with 75% survival rate, was the most effective on spent mushroom compost with P. ostreatus, while D. dendroides isolate SP resulted in equal survival rates of 50% on all the three mushroom composts.

Pullpipelining: A technique for systolic pipelined circuits

Pullpipelining, a pipeline technique where data is pulled from successor stages from predecessor stages is proposed Control circuits using a synchronous, a semi-synchronous and an asynchronous approach are given. Simulation examples for a DLX generic RISC datapath show that common control pipeline circuit overhead is avoided using the proposal. Applications to linear systolic arrays in cases when computation is finished at early stages in the array are foreseen. This would allow run-time data-driven digital frequency modulation of synchronous pipelined designs. This has applications to implement algorithms exhibiting average-case processing time using a synchronous approach.

Fusing data-mining and knowledge elicitation for the supervisory control of an aluminium plate mill

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.

A clocking technique for FPGA pipelined designs

This paper presents a clocking pipeline technique referred to as a single-pulse pipeline (PP-Pipeline) and applies it to the problem of mapping pipelined circuits to a Field Programmable Gate Array (FPGA). A PP-pipeline replicates the operation of asynchronous micropipelined control mechanisms using synchronous-orientated logic resources commonly found in FPGA devices. Consequently, circuits with an asynchronous-like pipeline operation can be efficiently synthesized using a synchronous design methodology. The technique can be extended to include data-completion circuitry to take advantage of variable data-completion processing time in synchronous pipelined designs. It is also shown that the PP-pipeline reduces the clock tree power consumption of pipelined circuits. These potential applications are demonstrated by post-synthesis simulation of FPGA circuits. (C) 2004 Elsevier B.V. All rights reserved.

Empirical mode decomposition: a novel technique for the study of tremor time series

Tremor is a clinical feature characterized by oscillations of a part of the body. The detection and study of tremor is an important step in investigations seeking to explain underlying control strategies of the central nervous system under natural (or physiological) and pathological conditions. It is well established that tremorous activity is composed of deterministic and stochastic components. For this reason, the use of digital signal processing techniques (DSP) which take into account the nonlinearity and nonstationarity of such signals may bring new information into the signal analysis which is often obscured by traditional linear techniques (e.g. Fourier analysis). In this context, this paper introduces the application of the empirical mode decomposition (EMD) and Hilbert spectrum (HS), which are relatively new DSP techniques for the analysis of nonlinear and nonstationary time-series, for the study of tremor. Our results, obtained from the analysis of experimental signals collected from 31 patients with different neurological conditions, showed that the EMD could automatically decompose acquired signals into basic components, called intrinsic mode functions (IMFs), representing tremorous and voluntary activity. The identification of a physical meaning for IMFs in the context of tremor analysis suggests an alternative and new way of detecting tremorous activity. These results may be relevant for those applications requiring automatic detection of tremor. Furthermore, the energy of IMFs was visualized as a function of time and frequency by means of the HS. This analysis showed that the variation of energy of tremorous and voluntary activity could be distinguished and characterized on the HS. Such results may be relevant for those applications aiming to identify neurological disorders. In general, both the HS and EMD demonstrated to be very useful to perform objective analysis of any kind of tremor and can therefore be potentially used to perform functional assessment.

Parallelism in control

A parallel structure is suggested for feedback control systems. Such a technique can be applied to either single or multi-sensor environments and is ideally suited for parallel processor implementation. The control input actually applied is based on a weighted summation of the different parallel controller values, the weightings being either fixed values or chosen by an adaptive decision-making mechanism. The effect of different controller combinations is a field now open to study.

Simplified parameter adaptive control

A simple parameter adaptive controller design methodology is introduced in which steady-state servo tracking properties provide the major control objective. This is achieved without cancellation of process zeros and hence the underlying design can be applied to non-minimum phase systems. As with other self-tuning algorithms, the design (user specified) polynomials of the proposed algorithm define the performance capabilities of the resulting controller. However, with the appropriate definition of these polynomials, the synthesis technique can be shown to admit different adaptive control strategies, e.g. self-tuning PID and self-tuning pole-placement controllers. The algorithm can therefore be thought of as an embodiment of other self-tuning design techniques. The performances of some of the resulting controllers are illustrated using simulation examples and the on-line application to an experimental apparatus.

Application of a novel optimal control algorithm to a benchmark fed-batch fermentation process

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) which has been designed to achieve the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A method based on Broyden's ideas is used for approximating some derivative trajectories required. Ways for handling con straints on both manipulated and state variables are described. Further, a method for coping with batch-to- batch dynamic variations in the process, which are common in practice, is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch processes. The algorithm is success fully applied to a benchmark problem consisting of the input profile optimisation of a fed-batch fermentation process.

Optimal control of a class of discrete–continuous non-linear systems — decomposition and hierarchical structure

A technique is derived for solving a non-linear optimal control problem by iterating on a sequence of simplified problems in linear quadratic form. The technique is designed to achieve the correct solution of the original non-linear optimal control problem in spite of these simplifications. A mixed approach with a discrete performance index and continuous state variable system description is used as the basis of the design, and it is shown how the global problem can be decomposed into local sub-system problems and a co-ordinator within a hierarchical framework. An analysis of the optimality and convergence properties of the algorithm is presented and the effectiveness of the technique is demonstrated using a simulation example with a non-separable performance index.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.