Presence Of Plant Uncertainties Research Articles

A robust optimization approach for dynamic input allocation

This paper addresses the problem of dynamic input allocation in the presence of plant uncertainties. The current state of the art shows how to design an Allocator as the cascade of an Optimizer and an Annihilator to achieve steady-state input optimality and output invisibility simultaneously. This work proposes a novel algorithm based on polynomial factorization to design a dynamic Annihilator. The critical aspect of this approach lies in the assumption of the perfect plant knowledge, making the Annihilator not robust to uncertainties. A robustification process is introduced by optimizing its design parameters. This approach is formulated as a model-matching problem aiming to reduce the output mismatch induced by the allocation scheme while maintaining steady-state optimality. As the numerical simulations highlight, this method applies to linear and nonlinear allocation problems.

Adaptive rejection of narrow-band disturbances in the presence of plant uncertainties—A dual Youla–Kucera approach

The stability of adaptive disturbance rejection schemes using Youla–Kucera (YK) parametrization and the internal model principle (IMP) in the presence of plant model uncertainties is investigated. The problem is approached by using the dual Youla–Kucera parametrization for the description of the plant model uncertainties. The known disturbance case is discussed first, emphasizing the need of over parametrization of the Youla–Kucera filter used for control in order to both solving the IMP and the stability problems. Then this solution is extended for the case of unknown disturbances leading to the use of a parameter adaptation algorithm with projection. A stability analysis of the adaptive scheme is provided. Simulation results on relevant examples and experimental evaluation on an active noise attenuation system illustrate the possibilities of this approach for handling significant plant-model mismatch.

Automated synthesis of multivariate QFT controller and pre-filter for a distillation column with multiple time delays

In the refining and chemical industry, distillation is one of the prime processes and is a multivariable control problem, which involves interaction amongst several control loops and exhibit dead time in the dynamic behavior. The analysis and controller design of processes inheriting dead time is a challenging task, and the presence of plant uncertainties and disturbances makes it challenging to exert quality control over time. In this paper, a template and bounds free approach is explored for the simultaneous synthesis of quantitative feedback theory (QFT) based robust controller and pre-filter for Wood and Berry distillation column with multiple time delays. This presented method simplifies the QFT controller synthesis for MIMO systems and eliminates the need of ‘Schauder fixed point theory’ to express the m×m MIMO system into analogous m2 MISO plants before the QFT controller synthesis. Using the presented approach, an optimal MIMO QFT controller and pre-filter are synthesized in a single step, which else conventionally follows a long sequential design process. In the presented approach, the QFT controller design problem has been posed as an optimization problem and is solved using genetic algorithm. The work has been compared with the work already reported in literature. The designed multivariate QFT controller efficiently mitigate the effect of parametric uncertainties, satisfy the design requirements in a very tight envelope, minimize the interaction amongst the control loops and offers improved the disturbance rejection.

Optimal design of QFT controller for pneumatic servo actuator system using multi-objective genetic algorithm

Loop shaping is the principle step for synthesising the quantitative feedback theory (QFT) based robust controllers. The controller assures performance robustness in the presence of plant uncertainties. This paper explores a template and bounds free approach for the automated synthesis of low order fixed structure QFT controller for a highly uncertain pneumatic servo actuator system. In this work, the loop-shaping problem has been posed as a multi-objective optimisation problem and solved using the multi-objective variant of the genetic algorithm. At the end of the design process, a set of Pareto optimal solutions (POS) are obtained, to aid the decision maker in choosing an ideal solution from the POS, use of level diagrams has been explored. The simulation of the results and time and frequency domain analysis has been carried out using MATLAB and the results obtained clearly unveil that the designed QFT controller offers robust behaviour over a range of plant's parametric uncertainty.

Multivariable extremum-seeking by periodic switching functions with application to Raman optical amplifiers

A periodic switching function based extremum-seeking sliding-mode control strategy is addressed for uncertain nonlinear systems. We generalize previous results to a more challenging multivariable scenario, where the diagonal dominance condition is relaxed and can now be understood as a triangular dominance condition, which is significantly less restrictive. As an essential advantage over earlier results in the literature, the complete global convergence proof (with respect to the closed-loop initial conditions) of the overall multi-input multi-output (MIMO) closed-loop system is provided, rather than local stability results. The practical application of the proposed multivariable controller is exemplified through the optimization problem of the output signal power spectrum of Raman optical amplifiers. From a practical point of view, the extremum-seeking scheme enables the automatic adjustment of the control pump power signals in the presence of plant uncertainties, guaranteeing a flat power spectrum of the downstream data signals. Using a multi-objective approach, we develop the optimization algorithm to achieve this goal by simultaneously minimizing the output ripple and the deviation from some desired power level. The convergence analysis and control design are carried out in the presence of uncertainties, and the algorithm performance is tested through numerical examples, where a particular optical communication system with 32 channels is considered. This numerical example uses model parameters acquired experimentally and a representative fiber model, such that the results are close to what is expected to find in a practical implementation.

Design of a Robust Controller by LQG/LTR Formalism for Francis hydro Turbine Driving a Synchronous Generator

This paper presents the design and application of a robust controller by Linear-Quadratic-Gaussian method with Loop-Transfer-Recovery (LQG \LTR) at the same time to carefully attain performance and robustness objectives. To improve Stability, the robust controller has been shown to provide good performance in normal operations conditions. Objectives cannot be suitable unless the controller can perpetuate such quality in the presence of plant uncertainties or any working conditions in the hydroelectric power plants. The approach is based to synthesizing a robust controller minimizing a quadratic criterion (controller LQG) while using the Loop Transfer Recovery (LTR), to restore robustness properties of the Estimator. In this study, we applied this robust control law on the model of a Francis hydro turbine. Computer simulations are carried out to establish a n d compare the performance and robustness of using the Infinite horizon control ( H ), internal model control (IMC), Proportional Integral Derived (PID) and LQG/LTR controllers.

Robustness analysis and design of fractional order Iλ Dμ controllers using the small gain theorem

ABSTRACTIn this paper, a simple method is proposed to tune the parameters of Fractional Integral-Fractional Derivative (FIFE) Iλ Dμ controllers based on the Bode diagram. The proposed technique provides a practical approach for tuning FIFE controllers to compensate stable plants. Using the small gain theorem and based on the sensitivity functions analysis, it is proved that by applying the designed FIFE controller the robustness of the compensated system in the presence of plant uncertainties is improved in comparison to the PI controller in a similar structure. Moreover, the closed-loop phase margin and gain crossover frequency are adjustable by tuning the free controller parameters. Simulation results are presented to demonstrate the simplicity of application and effectiveness of the tuned controller.

Adaptive output regulation of right-invertible MIMO LTI systems, with application to vessel motion control

The problem of robust output regulation for MIMO linear time-invariant systems with harmonic exogenous inputs and parameter uncertainties is addressed. We discuss two methods to handle stability in the presence of plant uncertainties, that take full advantage of all controls and all available measurements. One method, appeals to a general notion of “robust” minimum-phase. The other method, that addresses the case of unstructured uncertainties, reposes on the solution of appropriate LMIs. In both cases, the critical issue is to show how adaptive tuning of the internal model can be implemented, so as to obtain a controller that guarantees asymptotic convergence of the regulated output to zero. A numerical example of vessel motion control is given to illustrate one of the proposed approaches.

Adaptive Fuzzy Sliding Mode Control of Under-actuated Nonlinear Systems

A new extension of the conventional adaptive fuzzy sliding mode control (AFSMC) scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output (MIMO) systems, is presented. In particular, the assumption for non-zero diagonal entries of the input gain matrix of the plant is relaxed. In other words, the control effect of one actuator can propagate from a subgroup of canonical state equations to the rest of equations in an indirect sense. The asymptotic stability of the proposed AFSM control method is proved using a Lyapunov-based methodology. The effectiveness of the proposed method for the case of under-actuated systems is investigated in the presence of plant uncertainties and disturbances, through simulation studies.

Time delay finite time control of unified power flow controller for power flow reference tracking

This paper presents a time delay finite time controller (TDFTC) applied to a unified power flow controller (UPFC) to achieve rapid and finite time reference signal tracking of power flow in presence of plant uncertainties. The TDFTC is a nonlinear control based on the time delay disturbance estimator and the Lyapunov's stability theory to estimate the disturbances and cancel them. The TDFTC overcomes the drawback of traditional linear PI control, which is typically tuned for one specific operating condition, and reduces chattering by using lower design gains. In this paper, first a state space complete dynamic model of UPFC is established based on Kirchhoff's equations and Park's transformation and then TDFTC law is developed for the UPFC system. The proposed control is validated via a detailed simulation on a two bus system. Simulation results show power, effectiveness, robustness and accuracy of the proposed controller.

Vibration attenuation of spacecraft in the presence of parametric and unmodeled dynamics uncertainties using collocated and noncollocated control: A comparative study

µ-control synthesis offers the advantage of maintaining robust performance and stability in the presence of plant uncertainties. Expressions relating the 2-norm of the vector of regulated outputs and closed-loop modal damping to the weights used for µ-control synthesis are derived in this paper. By using these expressions, the amount of damping added to each mode can be controlled individually while maintaining equal importance for each controlled mode. Hence, the selection of weights for synthesizing a suitable µ-controller is greatly simplified. Furthermore, a comparative study of µ-controllers designed by the proposed procedure and positive position feedback (PPF) controllers designed by analytically derived optimal parameters is also presented. The techniques are compared for their vibration attenuation, energy utilization, and stability characteristics, in the presence of parametric and unmodeled dynamics uncertainties. Results for both time domain and frequency domain simulations are presented. As in some cases (sandwich plate structures etc.) the noncollocation of actuator/sensor becomes crucial for achieving good performance, the above-mentioned characteristics are evaluated for both collocated and noncollocated sensor locations.

Yet another tutorial of disturbance observer: robust stabilization and recovery of nominal performance

This paper presents a tutorial-style review on the recent results about the disturbance observer (DOB) in view of robust stabilization and recovery of the nominal performance. The analysis is based on the case when the bandwidth of Q-filter is large, and it is explained in a pedagogical manner that, even in the presence of plant uncertainties and disturbances, the behavior of real uncertain plant can be made almost similar to that of disturbance-free nominal system both in the transient and in the steady-state. The conventional DOB is interpreted in a new perspective, and its restrictions and extensions are discussed.

Offset-Free One-Step Ahead State Predictor for Power Electronic Applications Using Robust Proportional–Integral Observer

This paper suggests an offset-free one-step ahead state predictor for power electronic applications such as power converters and motor drives in the presence of plant uncertainties. The proposed method is devised through the robust proportional-integral observer technique, considering the full state as the output, and it has the two advantages: 1) to adopt the integrator of the state estimation error for ensuring the offset-free state prediction performance and 2) to introduce the nonlinear damping term so that it suppresses the disturbances originated from a plant-model mismatch. Performing the realistic simulations based on the PowerSIM software, it is shown that the proposed method provides an efficient one-step ahead state prediction performance despite the plant uncertainties.

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to provide the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID.

A robust estimator-based optimal algebraic approach to steam generator feedwater control system

Feedwater control systems are used to maintain the steam generator water level within prescribed narrow limits and to provide constant supply of steam during power demand variations. Current feedwater control systems are often found to be unsatisfactory during startup and low power operations. A robust nonlinear estimator-based optimal algebraic control is developed for feedwater control systems to solve the water level tracking problem during power demand variations. It is shown that the proposed control provides an optimal and robust water level tracking with a single automatic controller over the complete range of power operations in the presence of plant uncertainties and noisy measurements.

Robust tuning of dynamic matrix controllers for first order plus dead time models

Dynamic Matrix Control is a widely used Model Predictive Controller in industrial processes. The successful implementation of Dynamic Matrix Control in practical applications requires appropriate tuning of the controller parameters. Three different cases are considered. In the first case, a tuning formula is developed that ensures the nominal closed loop desired performance. However, this formula may fail in the presence of plant uncertainty. Therefore a lower bound for the tuning parameter is derived to secure the robust stability of the uncertain first order plus dead time plant. Finally, a tuning boundary is derived which gives the lower and upper permissible bounds for the tuning parameter that guarantee the robust performance of the uncertain first order plus dead time plant. The tuning procedure is based on the application of Analysis of Variance, curve fitting and nonlinear regression analysis. The derived results are validated via simulation studies and some experimental results.

Performance limitations for networked control systems with plant uncertainty

There has recently been significant interest in performance study for networked control systems with communication constraints. But the existing work mainly assumes that the plant has an exact model. The goal of this paper is to investigate the optimal tracking performance for networked control system in the presence of plant uncertainty. The plant under consideration is assumed to be non-minimum phase and unstable, while the two-parameter controller is employed and the integral square criterion is adopted to measure the tracking error. And we formulate the uncertainty by utilising stochastic embedding. The explicit expression of the tracking performance has been obtained. The results show that the network communication noise and the model uncertainty, as well as the unstable poles and non-minimum phase zeros, can worsen the tracking performance.

H∞/Predictive output control of a three-axis gyrostabilized platform

In the presence of plant uncertainties, utilizing an appropriate controller for a smooth output tracking and elimination of high-frequency disturbances, especially in accurate systems is very important. In this paper, a controller is proposed based on the robust and optimal theory to achieve a combination of such characteristics in the face of model parameter variations and unknown disturbances. The proposed controller has been simulated on a three-axis gyro-stabilized MIMO platform and comparison results with a NLPID controller simulation are provided.

A component-level failure detection and identification algorithm based on open-loop and closed-loop state estimators

This study presents a component-level failure detection and identification (FDI) algorithm for a cascade mechanical system subsuming a plant driven by an actuator unit. The novelty of the FDI algorithm presented in this study is that it is able to discriminate failure occurring in the actuator unit, the sensor measuring the output of the actuator unit, and the plant driven by the actuator unit. The proposed FDI algorithm exploits the measurement of the actuator unit output together with its estimates generated by open-loop (OL) and closed-loop (CL) estimators to enable FDI at the component's level. In this study, the OL estimator is designed based on the system identification of the actuator unit. The CL estimator, which is guaranteed to be stable against variations in the plant, is synthesized based on the dynamics of the entire cascade system. The viability of the proposed algorithm is demonstrated using a hardware-in-the-loop simulation (HILS), which shows that it can detect and identify target failures reliably in the presence of plant uncertainties.

Combined identification and rejection of periodic disturbances in the presence of plant uncertainty

The paper proposes an algorithm for the rejection of periodic disturbances of unknown frequency. The frequencies of the components of the disturbance are estimated using an orthogonal signals generator based on second-order generalized integrators. A novel feature of the algorithm is that internal signals of the frequency estimator are combined linearly to produce the control signal, resulting in a very simple algorithm. For a sinusoidal disturbance, the entire scheme is governed by a single adaptive parameter. It is shown that, within the assumptions of an averaging analysis, the adaptive system is globally stable and completely rejects the disturbance, even in the presence of plant uncertainty. Bounds on the uncertainty can be computed a priori based on bounds on the disturbance frequency. Simulations demonstrate the properties of the algorithm in a variety of conditions.