Mathematical model for robust control of an irrigation main canal pool

This paper describes the formulation and development of a mathematical model for high-performance robust controller design techniques, based on a complete identification for control procedure, of an irrigation main canal pool (true plant), which is characterized by the exhibition of large variations in its dynamic parameters when the discharge regime changes in the operating range [Q_min, Q_max]. Real-time field data has been used. Four basic steps of the proposed procedure have been defined in which all the stages, from the design of the experiments to the model validation, are considered. This procedure not only delivers a nominal model of the true plant, but also a reliable estimate of its model uncertainty region bounded by the true plant models under minimum and maximum operating discharge regimes (limit operating models). The model uncertainty set, defined by the nominal model and its uncertainty region, is characterized by its being as tight as possible to the true irrigation main canal pool. The obtained results are very promising since this kind of models facilitates the design of robust controllers, which allow improving the operability of irrigation main canal pools and also substantially reduce water losses.     

Simple fractional order controller combined with a Smith predictor for temperature control in a steel slab reheating furnace

This paper proposes a simple fractional order controller combined with a Smith predictor scheme for controlling the temperature of a steel slab reheating furnace. The dynamic model of the preheating zone of this process is obtained from an identification procedure applied in an industrial furnace. This identification procedure yields a second order plus time delay transfer function which undergoes large time delay changes. A fractional order integral controller combined with a Smith predictor is therefore designed. Simulated results compare the performances of the proposed fractional order controller with a standard PI controller, also combined with a Smith predictor, an LQR controller, and an H _∞ robust controller, in the case of the nominal process, and when the time delay varies. Four performance indexes have been used in this comparison: three related to the output performance (settling time, overshooting, and integral absolute error (IAE)), and a fourth one related to the control effort (TV). The analysis of these indexes shows that the simple fractional order controller provides lower values of the compared indexes when time delay becomes much higher than the nominal value.     

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.     

A self-tuning Smith predictor applied to the control of blood pressure: simulation studies

Significant time-delays occur in the response of blood pressure to drug therapy. The classical Smith predictor algorithm used to eliminate such delays is extended here to the principle of self-tuning under mis-match conditions. Two delay-free controller algorithms are investigated, being a self-tuning pole-placement and a self-tuning PID structure. Extensive simulation studies using the well-known Slate model have demonstrated the good transient performance obtained with this self-tuning Smith principle in various combinations, and under realistic variability of patient dynamics.     

Neural network-based Smith predictor design for the time-delay in a tele-operated control system

This article presents a methodology for compensating for the time-delay effects in tele-operated control systems. Compensation can be carried out by a neural network. A tele-operated system consists of a master robot to give commands, and a slave robot to work with the environment. The positional command by the master robot is transferred to the slave robot, and the contact force from the environment is transferred back to the master robot. The structure of the Smith predictor is modified by replacing the linear estimator with a neural network whose structure is based on the radial basis function (RBF). The RBF network identifies the slave model to deal with the nonlinearities in the system. Simulation studies have been conducted, and experimental studies of one-directional force control were performed to confirm the simulation results.     

Fuzzy-PID复合控制在温室节水灌溉中的应用

为解决温室节水灌溉的需求,综合考虑温室环境因子对灌溉量的影响,通过系统辨识的方法,建立被控对象土壤湿度的数学模型.针对湿度控制大滞后惯性环节的特点,设计了一种Fuzzy-PID复合控制的方法.该方法将模糊控制器和PID控制器并行结合,并采用梯形隶属函数的模糊切换算法进行两者的切换,实现两种控制方法的优势互补.实验仿真结果表明:Fuzzy-PID复合控制响应时间短、超调量小、稳态过程没有振荡,不仅具有较好的动态性能,而且具有比较理想的稳态品质,适用于温室节水灌溉控制.     

Application to a drinking water network of robust periodic MPC

In this paper the application of a novel robust predictive controller for tracking periodic references to a section of Barcelona's drinking water network is presented. The system is modeled using a large scale uncertain differential-algebraic discrete time linear model in which it is assumed that a prediction of the water demand is available and that it is affected by unknown and bounded uncertainties. The control objective is to satisfy the water demand while trying to follow a given reference of the level of the tanks of the network. The controller considered has been modified to account for algebraic equations and large scale models and it joins a dynamic trajectory planner and a robust predictive controller in a single layer to guarantee that the closed-loop system converges asymptotically to a neighborhood of optimal reachable periodic trajectory satisfying the constraints for all possible uncertainties even in the presence of sudden changes in the reference. To demonstrate these properties three different simulation scenarios have been considered.     

Application of robust control techniques to a mobile robot system

This article deals with the stabilization problem of a class of nonlinear second-order multivariable dynamical systems with bounded uncertainties. A robust discontinuous controller is proposed to guarantee asymptotic stability of this class of systems. Then, to guarantee the continuity of the control, a quasi-saturation function is introduced into the control law. Guaranteed eventual stability-in-the-large (ESL) of the dynamical systems is proved with the continuous, robust controller, which is much different from the standard “computed-torque control.” The controller requires known bounding functions that are easier to determine as well as more general than the bounds needed in other approaches. An application is given to trajectory-following control for a two-link mobile robot. © 1992 John Wiley & Sons Inc.     

Conical tank level control using fractional order PID controllers: a simulated and experimental study

In this paper, simulated and experimental results on the conical tank level control are presented. PI/PID controllers of integer order (IO) as well as of fractional order (FO) are studied and compared. The tuning parameters are obtained first by using root locus (RL) and Ziegler and Nichols methods, for comparison purposes. Next, particle swarm optimization (PSO) is employed to determine the optimal controllers'' parameters using as fitness function the integral of the absolute value of tracking error (IAE). From the experimental results it is concluded that PI/FOPI are the controllers presenting the lowest IAE indexes, whereas PID/FOPID controllers present the lowest energy consumption by the control signal.     

Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor

Microsystem Technologies - The present work deals with comparative and robustness analysis of grey wolf optimization (GWO) based fractional order proportional–integral derivative (FOPID)...     

Linearized min‐max robust model predictive control: Application to the control of a bioprocess

This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameter uncertainties. First, this work focuses on the design of a robust nonlinear model predictive control (RNMPC) law subject to model parameter uncertainties implying solving a min‐max optimization problem. Secondly, a new approach is proposed, consisting in relating the min‐max problem to a more tractable optimization problem based on the use of linearization techniques to ensure a good trade‐off between tracking accuracy and computation time. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC and nonlinear model predictive controllers. Its efficiency and its robustness against parameter uncertainties and/or perturbations are illustrated through numerical results.     

Optimal control of a water distribution network in a supervisory control system

This paper deals with the use of optimal control techniques in water distribution networks. An optimal control tool, developed in the context of a European research project is described and the application to the city of Sintra (Portugal) is presented.     

基于GPRS网的节水灌溉远程控制系统研究

提出了一种基于GPRS网的节水灌溉远程控制系统.利用Winsock网络编程技术和GPRS网的资源实现了数据远程无线传输与现场控制;采用ADO数据库访问技术和SQL数据库实现了数据管理与处理.现场计算机控制软件采用模糊控制理论,可实现无人化灌溉管理作业;重点研究了客户机、中心服务器和下位机之间的数据通信流程、通信帧协议及软件设计.该系统为科学实现农田节水提供了一条途径.     

Application of a robust model reference adaptive control algorithm to a nonlinear automotive actuator

Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.     

Integrated design and control for robust performance: Application to an MSMPR crystallizer

The interplay of different types of performance constraints in an integrated design and control problem is studied by means of a case study. This integrated problem is based on a recent method for robust process design [M. Mönnigmann, W. Marquardt, Normal vectors on manifolds of critical points for parametric robustness of equilibrium solutions of ODE systems, J. Nonlinear Sci. 12 (2002) 85–112; M. Mönnigmann, W. Marquardt, Steady state process optimization with guaranteed robust stability and feasibility, AIChE J. 49 (12) (2003) 3110–3126; M. Mönnigmann, W. Marquardt, Steady state process optimization with guaranteed robust stability and flexibility: application to HDA reaction section, Ind. Eng. Chem. Res. 44 (2005) 2737–2753; W. Marquardt, M. Mönnigmann, Constructive nonlinear dynamics in process systems engineering, Comput. Chem. Eng. 29 (2005) 1265–1275; M. Mönnigmann, Constructive nonlinear dynamics methods for the design of chemical engineering processes, Ph.D. Thesis, RWTH Aachen University, 2003]. The design is found by means of a steady-state optimization problem accounting for process economics and performance requirements. In particular, the latter are represented by constraints which guarantee a user-specified performance of the design in spite of parametric uncertainties. For the first time, two types of dynamic performance constraints are used simultaneously within the adopted framework. These are constraints on time-domain performance indicators as well as on the asymptotic dynamic process behavior. Furthermore, the effect of uncertainty in both, design and model parameters, is accounted for. A key strength of the suggested framework is the direct quantification of the trade-offs between economics and dynamic performance requirements for a selection of uncertainty scenarios. A series of different integrated design and control problems are formulated and solved for a continuous mixed-suspension mixed-product removal (MSMPR) crystallizer. The process exhibits a complex nonlinear behavior and represents a challenging example. The results of the case study allow an in depth understanding of the interactions of design and control for the underlying process.     

Application of fractional order theory of magneto-thermoelasticity to an infinite perfect conducting body with a cylindrical cavity

A fractional model of the equations of generalized magneto-thermoelasticity for a perfect conducting isotropic thermoelastic media is given. This model is applied to solve a problem of an infinite body with a cylindrical cavity in the presence of an axial uniform magnetic field. The boundary of the cavity is subjected to a combination of thermal and mechanical shock acting for a finite period of time. The solution is obtained by a direct approach by using the thermoelastic potential function. Laplace transform techniques are used to derive the solution in the Laplace transform domain. The inversion process is carried out using a numerical method based on Fourier series expansions. Numerical computations for the temperature, the displacement and the stress distributions as well as for the induced magnetic and electric fields are carried out and represented graphically. Comparisons are made with the results predicted by the generalizations, Lord–Shulman theory, and Green–Lindsay theory as well as to the coupled theory.

     

The multistage homotopy analysis method: application to a biochemical reaction model of fractional order

In this paper, a new reliable algorithm called the multistage homotopy analysis method (MHAM) based on an adaptation of the standard homotopy analysis method (HAM) is presented to solve a time-fractional enzyme kinetics. This enzyme–substrate reaction is formed by a system of nonlinear ordinary differential equations of fractional order. The new algorithm is only a simple modification of the HAM, in which it is treated as an algorithm in a sequence of small intervals (i.e. time step) for finding accurate approximate solutions to the corresponding systems. Numerical comparisons between the MHAM and the classical fourth-order Runge–Kutta method in the case of integer-order derivatives reveal that the new technique is a promising tool for nonlinear systems of integer and fractional order.     

Mismatch in a predictor control scheme : some closed-form solutions

Problems of sub-plant mismatch, hitherto treated numerically, are considered. Closed-form solutions are obtained for the predictor scheme with sub-plant K/8, modelled by K'/8, where K'/8 takes arbitrary (positive) values not necessarily close to K. The case of a small mismatch in delay only of the same scheme is treated similarly. The paper concludes with a detailed treatment of sub-plant mismatch accompanied by simultaneous small mismatch in delay. This has implications, in particular, for the choice of sign of delay mismatch in the use of deliberate mismatch for the improvement of performance. The techniques of the paper are applicable to more general sub-plant accompanied, or not, by small mismatch in delay.     

Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center

Journal of Intelligent Manufacturing - The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim...