Quadruple Tank Process Research Articles

Asynchronous periodic event-triggered control with dynamical controllers

In this work, we study a networked control system under a periodic event-triggered control strategy. In addition, the input and the output of the system are sampled with different rates, which enables to obtain a compromise between performance and waste of communication resources. Stability analysis and L2-gain analysis are carried out through Lyapunov–Krasovskii techniques. Simulation results of a quadruple-tank process show the benefits of the approach.

Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

We examine the stability problem for delayed neutral-type neural networks (NNs) with interval time-varying delay signals under the effects of leakage term by constructing a suitable Lyapunov–Krasovskii functionals (LKFs) with the triple- and four-integral terms and using the famous Jensen inequality, Wirtinger single integral inequality (WSII), and Wirtinger double integral inequality (WDII), combined with the reciprocally convex approach (RCC) for the stability of addressing NNs. Therefore, the major contribution of this study lies in a consideration of new integral inequalities and improved LKFs, fully taking the relationship between the terms in the Leibniz–Newton formula within the framework of linear matrix inequalities (LMIs). Moreover, we assume that the lower bound of interval time-varying delay is not restricted to zero. Using several examples, we show that the proposed stability criterion is less conservative than previous results. Also, the proposed technique is applied to benchmark problem that is associated with reasonable issues to showing feasibility on a real-world problem, including transporting time delay signals and leakage delay as a process variable in the quadruple-tank process system.

A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process

In process industries model-plant mismatch is a significant problem. Quadruple tank process (QTP) can be configured both in minimum phase and non-minimum phase (NMP). However, in NMP, the control of QTP poses a challenge. This paper addresses that and presents a novel robust wavelet based non-minimum phase control (NMPC) strategy for the challenging QTP using genetic algorithm to find the optimised value of the manipulated variables in NMPC at every sampling time. The QTP is modelled based on wavelet neural network. The simulation results indicate that significant improvements have been achieved both in modelling and control strategies for a QTP system compare to conventional approaches such as the Levenberg-Marquardt.

Tuning of PID controller using optimization techniques for a MIMO process

In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.

A non-minimum phase robust nonlinear neuro-wavelet predictive control strategy for a quadruple tank process

In process industries model-plant mismatch is a significant problem. Quadruple tank process (QTP) can be configured both in minimum phase and non-minimum phase (NMP). However, in NMP, the control of QTP poses a challenge. This paper addresses that and presents a novel robust wavelet based non-minimum phase control (NMPC) strategy for the challenging QTP using genetic algorithm to find the optimised value of the manipulated variables in NMPC at every sampling time. The QTP is modelled based on wavelet neural network. The simulation results indicate that significant improvements have been achieved both in modelling and control strategies for a QTP system compare to conventional approaches such as the Levenberg-Marquardt.

Quantitative feedback control for multivariable model matching and disturbance rejection

Summary This article extends two recent contributions in the field of quantitative feedback theory to the multivariable case. They concern the model matching and the measured disturbance rejection problems. The model matching problem is a tracking control problem with specifications given as acceptable deviations from an ideal response. The measured disturbance rejection problem balances feedback and feedforward actions to reject disturbances. Both perspectives present advantages over classical quantitative feedback theory techniques in certain situations. This paper develops the necessary tools to solve both control problems in the case of multi-input multi-output plants. In particular, it shows how to derive nonconservative controller bounds for each of the single-input single-output control problems in which the overall multivariable problem is divided. The result is a systematic controller design methodology for multi-input multi-output plants with structured uncertainty. The application of the technique to the well-known quadruple-tank process illustrates the benefits of the method. Copyright © 2016 John Wiley & Sons, Ltd.

Dual mode adaptive fractional order PI controller with feedforward controller based on variable parameter model for quadruple tank process

The Quadruple Tank Process (QTP) is a well-known benchmark of a nonlinear coupled complex MIMO process having both minimum and nonminimum phase characteristics. This paper presents a novel self tuning type Dual Mode Adaptive Fractional Order PI controller along with an Adaptive Feedforward controller for the QTP. The controllers are designed based on a novel Variable Parameter Transfer Function model. The effectiveness of the proposed model and controllers is tested through numerical simulation and experimentation. Results reveal that the proposed controllers work successfully to track the reference signals in all ranges of output. A brief comparison with some of the earlier reported similar works is presented to show that the proposed control scheme has some advantages and better performances than several other similar works.

Reduction of Interaction in Multivariable System using LabVIEW

Background/Objectives: The aim of this paper is to design a decoupled PID controller for quadruple tank process. The major advantage of quadruple-tank process is that the multivariable zero can be positioned to both side of 's' Plane. Methods/Statistical Analysis: In this paper, the control of liquid levels in Quadruple tank process with non-linear dynamics is considered. The dynamic behavior of the system can be achieved in both minimum and a non-minimum phase by varying valve position. The process is simulated and the simulation results are presented to analyze the performance of PID controller. Findings: The multivariable process is decomposed into several independent single-loop sub-systems. The performance of the system is compared with and without decoupler. The Decoupled PID controller results show the improvement in performance. Applications/Improvements: The interaction between the tanks is reduced by designing the decoupler. The Decoupled PID controller results show the improvement in performance. The decoupled control provides control method for multivariable process using independent loop controllers.

Reference Tracking using a Non-Cooperative Distributed Model Predictive Control Algorithm

In this paper, a non-cooperative distributed model predictive control (DMPC) algorithm for tracking constant references is developed and evaluated. As such, an augmented model is employed (i.e. the control loop is embedded with integrators) and the augmented state contains the state increments and the error between the reference and the predicted output. The algorithm is tested in real life experiments on the quadruple tank process with non-minimum phase behaviour. The experimental results show acceptable performance index for the DMPC method when compared with the centralized approach.

A hybrid biogeography-based particle swarm optimisation technique for controlling modified quadruple tank process

The paper presents a biogeography-based optimisation (BBO), particle swarm optimisation (PSO) and proposed hybrid biogeography-based optimisation-particle swarm optimisation (BBO-PSO) for optimising proportional integral (PI) controller parameters for the level control of bottom two tanks in a modified quadruple tank process (MQTP). The simulation results are presented to prove the effectiveness of the proposed hybrid algorithm for minimising the objective function. The hybrid BBO-PSO has significant performance improvements in servo, regulatory and robustness responses.

Decentralized Adaptive Fuzzy Output-Feedback Control of Switched Large-Scale Nonlinear Systems

In this paper, a decentralized adaptive fuzzy output-feedback control problem is discussed for a class of switched large-scale nonlinear systems without the measurements of the system states. Fuzzy logic systems are used to approximate the unknown nonlinear functions, and a switched observer is designed to avoid the conservativeness caused by adoption of a common observer for all subsystems. A common coordinate transformation of all subsystems is constructed to get rid of individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Meanwhile, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied in this paper, the classical average dwell time method cannot be directly applied. Based on this, a switched observer-based decentralized adaptive fuzzy output-feedback control technique is set up by exploiting the average dwell time method and backstepping. It is proved that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals with average dwell time, while the tracking errors converge to a small neighborhood of the origin. The effectiveness of the proposed control scheme is illustrated by its applications to a two-inverted-pendulum system and a quadruple-tank process system.

Global stability analysis of switched nonlinear observers

This paper considers the problem of simultaneous estimation of the system states and the strategy of commutation for a larger class of nonlinear switched systems. First, a hybrid high gain observer is considered to get the exact estimation of the continuous states where the strategy of switching is previously known. Then, an extension to a larger class of nonlinear hybrid systems with arbitrary switching is made. Stability analysis is widely discussed for the two cases to provide a finite-time convergence of the estimation errors. The effectiveness of the proposed hybrid high gain observer has been proved by applying it to a quadruple tank process.

Design of robust fractional-order controllers and prefilters for multivariable system using interval constraint satisfaction technique

Design of robust controller for multivariable systems is very challenging and their automation is a key concern in control system design. In this paper, a new, simple, and reliable automated fractional-order multivariable Quantitative Feedback Theory (QFT) controllers design methodology is proposed. A fixed structure fractional-order multivariable QFT controller has been synthesized by solving QFT quadratic inequalities of robust stability and tracking specifications. The quadratic inequalities (constraints) are posed as Interval Constraint Satisfaction Problem (ICSP). The constraints are solved by constraint solver-RealPaver. The synthesis problem consists of obtaining a controller that ensures stability and meets a given set of performance specifications, in spite of disturbance and model uncertainties. In addition to perform the above tasks, a MIMO controller also has to perform the difficult task of minimizing interaction between the various control loops. Unlike existing manual or convex optimization based QFT design approaches, the proposed method gives a controller which meets all performance requirements in QFT, without going through the conservative and sequential design stages for each of the multivariable sub-systems. In the design method, we pose the prefilter design problem as an ICSP and solve it using the well-established constraint solver. The designed controllers and prefilters are tested for a quadruple tank process and their efficacy is demonstrated.

Fractional-Order Sliding Mode Controller Design for a Modified Quadruple Tank Process via Multi-Level Switching

The paper deals with the level control of a Modified Quadruple Tank Process (MQTP). In that, the interaction is introduced between the bottom two tanks which causes an additional non-linear dynamical component of the conventional Quadruple Tank Process (QTP). Since the process has an inbuilt and imposed uncertainties, a robust Sliding Mode Controller (SMC) is initially designed to drive the system to the desired operating point via the sliding surface. Further, the conventional sliding surface is altered with fractional order dynamics. It makes a Fractional-Order SMC (FrSMC) and is proposed for both finite time convergence and counteract to the uncertainties present in the system. The undesirable chattering effect is reduced by introducing a novel exponential Multi-Level Switching (MLS) technique in order to protect control valve. Simulation results show the efficacy of the proposed Multi-Level Switching FrSMC (MFrSMC) in terms of fast convergence with high robustness when compared to conventional SMC.

Selecting the Number of Principal Components on the Basis of Performance Optimization of Fault Detection and Identification

A novel method for selecting the number of principal components (PCs) is proposed in this paper. This method adopts a new fault detection performance index and a new fault identification performance index to complete the selection. The new fault detection performance index is developed to represent the fault detection ability using the concept of detectable fault coverage, and the new fault identification performance index is proposed for the purpose of judging whether the fault identification result is correct or not. Furthermore, detailed mathematical analysis is given to explain the influence of the number of PCs on the fault identification performance index. The Monte Carlo numerical model and the quadruple-tank process model are used to demonstrate the advantages of our proposed indices and to illustrate the implementation of our proposed method.

Two-Level Robust Optimal Control of Large-Scale Nonlinear Systems

Finding an optimal control strategy for a nonlinear uncertain system is a challenging problem in the area of nonlinear controller design. In this paper, a two-level control algorithm is developed for robust optimal control of large-scale nonlinear systems. For this purpose, using a decomposition/coordination framework, the large-scale nonlinear system is first decomposed into several smaller subsystems, at the first level, where a closed-form solution as a feedback of states and interactions is obtained to optimize each subsystem. At the second level, a substitution-type prediction method, as a coordination strategy, is used to compensate the nonlinear terms of the system and to predict the interaction between subsystems. The coordinator mainly evaluates the next update for the coordination parameters and continues to exchange information with the first level, so that the overall optimum solution is obtained. This approach is applicable to any large-scale nonlinear uncertain system with unstructured bounded uncertainties. The effectiveness and performance of the proposed approach are investigated through simulation of a nonlinear quadruple-tank process.

Unknown inputs observers design for a class of nonlinear switched systems

The main topic of this paper is the problem of constructing unknown inputs observers for MIMO nonlinear switched systems. The idea is to switch between different observers to estimate the unknown states and the non-measured inputs for every transient’s periods with the choice of various values of the gain to reduce the error estimation. Theoretical stability analysis is derived to ensure the convergence of the designed observer. Instead, simulation results on a quadruple tank process are given in order to assess the efficiency of the proposed hybrid observers.

Bond-graph-based controller design for the quadruple-tank process

The quadruple-tank process has been proposed as a benchmark for multivariable control system design. This paper addresses the design in the bond-graph domain of a robust controller having the volumetric flows of two pumps as manipulated variables and the level of the two lower tanks as the regulated outputs. The basic control objectives are expressed in terms of desired closed-loop energy and power-dissipation functions and captured in the bond-graph domain by means of a so-called target bond-graph. A basic controller design performed via bond-graph prototyping yields a primary control law which is further robustified against parameter uncertainties, measurement deviations and faults using the diagnostic bond-graph concept. This results in an additional closed-loop consisting of a PI-law which is represented by a physically meaningful bond-graph subsystem. The design methodology is first developed on a simpler two-tank SISO-control problem and then straightforwardly extended to the multivariable problem with the help of some causal manipulations on the four-tank bond-graph model.

Multivariable Pid Control Via Ilmis: Performances Assessment

Abstract This paper addresses the problem of performance assessment for MIMO PID controllers. An efficient comparative analysis is established using two Iterative Linear Matrix Inequality (ILMI) approaches. To evaluate the robustness and the performances of each ILMI approach, four tests are carried out on the closed-loop system including White Gaussian noise, set point changes, parametric perturbation, and multiple time-delays influence. Different performance criterion such as IAE, ISE, ITAE and ITSE are also presented to evaluate the closed-loop system performances. The quadruple-tank process as a benchmark of minimum/non-minimum phase systems is applied to show the effectiveness and utility of the proposed analysis. The comparative analysis between the two PID controllers leads to nearly similar performance results in parametric perturbation and noise attenuation but different results in terms of set point changes, multiple time-delays influence and performance indices

Actuator fault diagnosis and fault-tolerant control: Application to the quadruple-tank process

The paper focuses on an important problem related to the modern control systems, which is the robust fault-tolerant control. In particular, the problem is oriented towards a practical application to quadruple-tank process. The proposed approach starts with a general description of the system and fault-tolerant strategy, which is composed of a suitably integrated fault estimator and robust controller. The subsequent part of the paper is concerned with the design of robust controller as well as the proposed fault-tolerant control scheme. To confirm the effectiveness of the proposed approach, the final part of the paper presents experimental results for considered the quadruple-tank process.