Design of output constraints for model-based non-square controllers using interval operability

A steady-state interval operability methodology is introduced here for multivariable non-square systems with fewer inputs than output variables to be used in the design of model-based constrained controllers (MPC, DMC). For such systems, set-point control is not possible for all the outputs and interval control is needed. The proposed iterative approach enables the selection of the needed interval constraints systematically, so that the tightest possible control is achieved without rendering the control problem infeasible. The application of this methodology to high-dimensional industrial problems characterizing processes of Air Products and Chemicals and DuPont shows that very significant reduction of the constrained region can be achieved from the steady-state point of view. Ratios of the initial to the calculated volume of the constrained regions examined range between 10⁴ and 10⁸.     

Linear metrics for entropy estimation

An entropy estimator constructed with respect to specially selected metrics is studied. It is shown that the estimator converges almost everywhere and the decrease in its variance is of an power order. For symmetric Bernoulli measures, the bias of the estimator is found.     

Linear estimation for random delay systems

This paper is concerned with the linear estimation problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time-stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal linear filter is presented based on the Kalman filtering technique. However, the optimal filter is time-varying, stochastic, and does not converge to a steady state in general. Then an alternative suboptimal filter with deterministic gains is developed under a new criteria. The estimator performance in terms of their error covariances is provided, and its mean square stability is established. Finally, a numerical example is presented to illustrate the efficiency of proposed estimators.     

Super-twisting estimation of a virtual output for extremum-seeking output feedback control of bioreactors

In biotechnological processes such as fed-batch reactors the lack of reliable and robust on-line sensors and the limited number of actuators make the task of operating at optimal conditions very difficult. We present a feedback controller that aims at regulating the substrate concentration at an optimum value such that biomass production is enhanced while by-product formation is not favored. We use a virtual output that is estimated using a bank of weighted super-twisting observers to drive an output-feedback extremum-seeking controller. The only online measurements needed are the biomass concentration and the oxygen and carbon dioxide mass transfer rates. Simulations on a fed-batch bioreactor model show its applicability.     

基于模型的输出反馈网络控制系统反馈调度研究

针对基于模型的网络控制系统缺乏应对动态变化的网络负载问题,设计反馈调度器,依据实际的网络拥塞情况,调整基于模型的网络控制系统的状态更新时间.为应对状态不完全可测的情况,在控制结构中使用了状态观测器,并证明了所提出系统在可变更新时间情况下的稳定性.仿真结果验证了稳定性条件的正确性和新网络控制系统结构的有效性.     

Linear quadratic output tracking and disturbance rejection

This article introduces the problem of linear quadratic tracking (LQT) where the objective is to design a closed-loop control scheme such that the output signal of the system optimally tracks a given reference signal and rejects a given disturbance. Different performance indices that have been used to address the tracking problem are discussed and an appropriate new form is introduced. It is shown that a solution to the proposed optimality index exists under very mild conditions of stabilisability and detectability of the plant state-space equations. The solution is formulated based on converting the LQT problem to a standard linear quadratic regulation problem. The method is applied to two examples, a first-order plant and a third-order plant, and their simulation results are presented and discussed.     

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

This paper proposes a novel wavelet neural network algorithm for the continuous and noninvasive dynamic estimation of blood pressure(BP). Unlike prior algorithms, the proposed algorithm capitalizes on the correlation between photoplethysmography(PPG) and BP. Complete BP waveforms are reconstructed based on PPG signals to extract systolic blood pressure(SBP) and diastolic blood pressure(DBP). To improve the robustness, Daubechies wavelet is implemented as the hidden layer node function for the neural network. An optimized neural network structure is proposed to reduce the computational complexity. Further, this paper investigates an inhomogeneous resilient backpropagation(IRBP) algorithm to calculate the weight of hidden layer nodes. The IRBP improves the convergence speed and reconstruction accuracy. Multiparameter intelligent monitoring in Intensive Care(MIMIC) databases, which contain a variety of physiological parameters captured from patient monitors, are used to validate this algorithm. The standard deviation σ between reconstructed and actual BP signals is 4.4797 mm Hg, which satisfies the American National Standards of the Association for the Advancement of Medical Instrumentation. The reconstructed BP waveform can be used to extract the SBP and DBP, whose standard deviations σ are 2.91 mm Hg and 2.41 mm Hg respectively.     

Parameter estimation in time-triggered and event-triggered model-based control of uncertain systems

In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs). Stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented. The updated model is used to control the real system the next time feedback information is unavailable. Additionally, new estimation models are proposed that offer better convergence properties than typical state-space parameter estimation methods; common assumptions such as availability of persistently exciting inputs and estimation of only a canonical form of the system are relaxed. The implementation of upgraded models in MB-NCSs results in better usage of the network by allowing longer intervals without the need for measurement updates.     

Robust model-based motion tracking through the integration of search and estimation

A computer vision system has been developed for real-time motion tracking of 3-D objects, including those with variable internal parameters. This system provides for the integrated treatment of matching and measurement errors that arise during motion tracking. These two sources of error have very different distributions and are best handled by separate computational mechanisms. These errors can be treated in an integrated way by using the computation of variance in predicted feature measurements to determine the probability of correctness for each potential matching feature. In return, a best-first search procedure uses these probabilities to find consistent sets of matches, which eliminates the need to treat outliers during the analysis of measurement errors. The most reliable initial matches are used to reduce the parameter variance on further iterations, minimizing the amount of search required for matching more ambiguous features. These methods allow for much larger frame-to-frame motions than most previous approaches. The resulting system can robustly track models with many degrees of freedom while running on relatively inexpensive hardware. These same techniques can be used to speed verification during model-based recognition.     

A comparison of nonlinear observers for output feedback model-based control of seeded batch crystallization processes

This paper presents a comparative analysis of various nonlinear estimation techniques when applied for output feedback model-based control of batch crystallization processes. Several nonlinear observers, namely an extended Luenberger observer, an extended Kalman filter, an unscented Kalman filter, an ensemble Kalman filer and a moving horizon estimator are used for closed-loop control of a semi-industrial fed-batch crystallizer. The performance of the nonlinear observers is evaluated in terms of their closed-loop behavior as well as their ability to cope with model imperfections and process uncertainties such as measurement errors and uncertain initial conditions. The simulation results suggest that the extended Kalman filter and the unscented Kalman filter provide accurate state estimates that ensure adequate fulfillment of the control objective. The results also confirm that adopting a time-varying process noise covariance matrix further enhances the estimation accuracy of the latter observers at the expense of a slight increase in their computational burden. This tuning method is particularly suited for batch processes as the state variables often vary significantly along the batch run. It is observed that model imperfections and process uncertainties are largely detrimental to the accuracy of state estimates. The degradation in the closed-loop control performance arisen from inadequate state estimation is effectively suppressed by the inclusion of a disturbance model into the observers.     

Linear estimation of ARMA processes

The method of estimating ARMA parameters first discussed by Mayne and Firoozan is investigated. It is shown that if, at the first stage, the order of the fitted autoregression is allowed to depend on the number of time points, in a reasonable manner, then it will still be true that the final estimate of the parameter vector will converge, almost surely, to the true value. This is to be compared to the result in the original paper where the order is fixed and it is shown that, as the sample size increases, the estimate converges to a value which, if the order of the autoregression is high enough, will be arbitrarily near to the true value. Some comments are made on other extensions, on the law of the iterated logarithm, on the central limit theorem and on the choice of the order of the fitted autoregression. The innovation sequence need not be Gaussian and for the convergence result only a natural condition relating to prediction needs be imposed.     

Linear output feedback compensation of rank-one systems

A new method is presented for arbitrarily assigning the closed-loop poles of a linear multi-input/multi-output system using linear output feedback. The method employs a new notion of rank-one system representations and involves the use of state transformation matrices which preserve such representations while reducing the degree of the feedforward matrix     

Linear dynamic output feedback: Invariants and stability

A full invariant under linear dynamic output feedback is derived. Some of its applications to the design of internally stable feedback control systems are considered.     

Linear dynamic filtering with noisy input and output

State estimation problems for linear time-invariant systems with noisy inputs and outputs are considered. An efficient recursive algorithm for the smoothing problem is presented. The equivalence between the optimal filter and an appropriately modified Kalman filter is established. The optimal estimate of the input signal is derived from the optimal state estimate. The result shows that the noisy input/output filtering problem is not fundamentally different from the classical Kalman filtering problem.     

Iterative least-squares parameter estimation for ARMA pulse response and output disturbance

The parameters of an ARMA system and of additive ARMA output disturbance with mutually different poles were estimated from a record of the disturbed output on a single pulse input. Alternating the steps of iterative inverse filtering with iterations according to generalized least squares appeared very economical for this system. This procedure converged for second-order simulations. Statistics of the estimates were evaluated from these simulation results for two signal-to-noise ratios.