The theoretical solution of the problem of the optimal feedback regulation of multi-axled vehicle suspension dynamics is provided. It is shown to consist of the sum of a static linear gain and an integral operator, op...
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The theoretical solution of the problem of the optimal feedback regulation of multi-axled vehicle suspension dynamics is provided. It is shown to consist of the sum of a static linear gain and an integral operator, operating on the system state. These operators are found to depend on the solution of a set of matrix valued partial differential equations of Riccati type. In the finite horizon case dealt with here these must be solved off-line as is usual in such problems. It is shown how the equations of motion of the suspension dynamics of a multi-axled vehicle may be brought to a form suitable for the application of the results.
A control engineer is required to design a system to meet the specified desired performance with guaranteed reliability and safety. Reliability and safety are particularly important in the field of process control. Ho...
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A control engineer is required to design a system to meet the specified desired performance with guaranteed reliability and safety. Reliability and safety are particularly important in the field of process control. However, not only in process control but also in other industrial applications, reliability and safety are emphasised as well as the performance of the controller. The goal of a supervisory structure is to establish the health of a system and design an alarm system for the user in the hope of preventing undesirable consequences. The component is called fault detection and diagnosis (FDI), and a process model-based algorithm is used in this study to monitor the operation of a system. Nevertheless, because stability is a primary requirement of a control system, another requirement of supervision is to guarantee the stability as well as the health of the plant. This is the theme of this paper which combines both control and supervision using GPC.
Early and current self-tuning algorithm formulations were generally based on the assumption that the process model under investigation is linear within a certain operating point. A standard Recursive Least-Squares (RL...
Early and current self-tuning algorithm formulations were generally based on the assumption that the process model under investigation is linear within a certain operating point. A standard Recursive Least-Squares (RLS) estimation algorithm was used to update the model parameters whose data input and output were normally fed through a filter with adequate characteristics. One of the most popular themes belonging to this class of adaptive controllers is that of generalized predictive control (GPC). Classified in the category of long-range predictive controllers (LRPC) its control law stems from the minimization of a cost function over a horizon which spans that used by the RLS algorithm (one step ahead). This paper describes a new approach which derives the same model parameters using extra filtering provided by an identification objective similar to the one used for control derivation. Already successfully applied in real-time to a SISO control system by its original authors, the scheme, known as long-range predictive identification (LRPI), is applied here to a nonlinear multivariable anaesthetic model in combination with generalized predictive control with feedforward (GPCF) and multivariable GPC using a P-canonical form for the process model, and its performance is assessed.
In this paper we study the inversion of the multidimensional Laplace transform by a combination of a general partial-fraction expansion formula and the theory of residues. The ideas may be applied to nonlinear systems...
In this paper we study the inversion of the multidimensional Laplace transform by a combination of a general partial-fraction expansion formula and the theory of residues. The ideas may be applied to nonlinear systems defined by Volterra series.
This paper investigates the effectiveness of several criteria for validating models which exhibit chaotic dynamics. Embedded trajectories, Poincare sections, bifuraction diagrams, the largest Lyapunov exponent and cor...
This paper investigates the effectiveness of several criteria for validating models which exhibit chaotic dynamics. Embedded trajectories, Poincare sections, bifuraction diagrams, the largest Lyapunov exponent and correlation dimension are considered. The Duffing-Ueda equation and four identified models are used as examples. The results show that models with similar invariants such as Poincare sections, the largest Lyapunov exponent and correlation dimension may have very different bifuracation behaviours. This suggests that the requirement that an identified model should reproduce the bifurcation pattern of the original system is a very exacting criterion which is well suited for validation purposes.
The authors present a practical solution to the problem of real-time robot control including the nonlinear dynamic model of the manipulator by employing a parallel processing approach. The parallelism inherent in the ...
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The authors present a practical solution to the problem of real-time robot control including the nonlinear dynamic model of the manipulator by employing a parallel processing approach. The parallelism inherent in the adaptive controllers is exploited to obtain an efficient implementation that reduces the overall computation time to within the limit acceptable for real-time control. The distributed algorithm is implemented on a network of transputers for the six-joint PUMA 560 arm.< >
The identification of polynomial and rational NARMAX models is studied and a unified least squares algorithm is introduced. The identification of two fluid loading systems, a wave flume system in unidirectional and di...
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The identification of polynomial and rational NARMAX models is studied and a unified least squares algorithm is introduced. The identification of two fluid loading systems, a wave flume system in unidirectional and directional sea states are included to illustrate the results.
Most manufacturing oriented simulation languages only offer the facility to model the system in one mode: discrete or continuous. However if a real, single mode system is examined more closely, some characteristics of...
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Most manufacturing oriented simulation languages only offer the facility to model the system in one mode: discrete or continuous. However if a real, single mode system is examined more closely, some characteristics of the opposing mode can usually be found. It is to what extent that the opposing mode can be neglected that determines in which mode the system can be successfully modelled. For many systems it is not satisfactory to approximate to a single mode system, and a mixed-mode systems simulation language would be required. A genuine mixed-mode simulation language would be able to model both discrete and continuous systems individually, in addition to combined continuous/discrete systems, and would therefore be a more useful general purpose simulation tool. It has been postulated that all systems should be treated as mixed-mode, with those with only discrete characteristics, or those with only continuous characteristics treated only as exceptions instead of the rule.< >
This particular study concerns the application of the generalised predictive control (GPC) algorithm using the concept of prespecified set-points in clinical anaesthesia. The study reveals that the SISO GPC version be...
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This particular study concerns the application of the generalised predictive control (GPC) algorithm using the concept of prespecified set-points in clinical anaesthesia. The study reveals that the SISO GPC version behaves very well under these circumstances and displays good robustness properties when mismatch conditions between the actual and assumed set-point profiles are considered. Moreover, when the scheme is extended to enhance a multivariable structure involving simultaneous control of muscle relaxation together with unconsciousness using blood pressure measurements, the levels of interaction from one channel to the other one can be considerably reduced without great compromise on other characteristics such as transient response and speed making therefore this whole concept of set-point prespecification an attractive candidate for future clinical trials.< >
In this paper, a growth criterion is derived using statistical inference for model sufficiency. This criterion is developed for recursive estimation or sequential learning with neural networks. A growing Gaussian radi...
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In this paper, a growth criterion is derived using statistical inference for model sufficiency. This criterion is developed for recursive estimation or sequential learning with neural networks. A growing Gaussian radial basis function (GaRBF) network trained by the extended Kalman filter (EKF) algorithm on-line, called incremental network is developed. Incremental network is similar to the resource allocating network (RAN). The criterion for growth is based on the network prediction error and the expected uncertainty in the network output. The criterion is computed within the EKF estimation end hence no additional computations are required. This is in contrast to the need for search in the RAN formulation. The incremental network performance on a function interpolation problem is shown to be superior in convergence speed and approximation accuracy than the RAN networks and a fixed size RBF network.< >
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