Metaheuristics-Based Multi-Objective Design of Robust Process Control Systems

A program for handling multiple phases of the design cycle in process control system design

Rotorcraft flight control systems present design challenges which often exceed those associated with fixed-wing aircraft. First, large variations in the response characteristics of rotorcraft result from the wide range of airspeeds of typical operation (hover to over 100 kts). Second, the assumption of vehicle rigidity often employed in the design of fixed-wing flight control systems is rarely justified in rotorcraft where rotor degrees of freedom can have a significant impact on the system performance and stability. Proposed herein is a methodology for the design of robust rotorcraft flight control systems utilizing Quantitative Feedback Theory (QFI'), a technique which accounts for variability in the dynamic response of the controlled element in the design of robust control systems. It was developed to address a Multiple-Input-Single-Output (MISO) design problem, but was extended to the address the Multiple-Input-Multiple-Output (MIMO) flight control system (FCS) design problem of a UH-60 Black Hawk Helicopter. Inherent conservatism in this design technique leads to limitations in its utility. A modified QFI' approach proved a viable method for designing robust MIMO flight control systems. An analysis of the two flight control system design methodologies is presented.

This paper presents a rigorous mathematical development for the design of robust intelligent type-2 fuzzy control systems with regards to the disturbance rejection problem. The majority of the papers in the literature uses only type-1 fuzzy systems for the modeling of both plant ant controller. In the control structure presented in this paper, we use type-2 fuzzy systems, which are more generalized than their type-1 counterparts and particularly known for handling uncertainties. This work extends and generalizes the previous results for the design of robust intelligent control systems. The final robust criteria are given based on linear matrix inequalities (LMIs), and two examples are provided to demonstrate the developed methodologies. The simulation results also show the potential of type-2 fuzzy controllers in improving robustness compared to type-1. The mathematically-sound general framework proposed in this paper provides a systematic tool for the design of robust intelligent controllers, and hence it is expected to widen their applications.

Process Control Algorithms for Microprocessors

Robust controller design methodology for multivariable chemical processes: Structured perturbations

In the H/sub /spl infin// approach to the design of robust control systems, the H/sub /spl infin// norm of certain weighted transfer function matrices of a control system is an important index of system robustness. The design procedure of a robust control system is essentially a procedure of minimizing that H/sub /spl infin// norm index. This paper proves that the normality of certain transfer function matrices of a control system is a sufficient condition for minimum H/sub /spl infin// index. Moreover, for the 2-input 2-output case, it is not only sufficient, but also necessary. Thus, the normalization of certain transfer function matrices of a system guarantees the best system robustness property in the sense of minimum H/sub /spl infin// index. This serves as the basis of a new approach to robust control system design-the optimization of parametrized normal matrix (OPNORM) method. It is also shown here that the weighting function in the H/sub /spl infin// norm index has certain interesting significance in the design of open-loop system eigenloci. >

Many advanced methods proposed for control of robot manipulators are based on the dynamic models of the robot systems. Model-based control design needs a correct dynamic model and precise parameters of the system. Practically speaking, however, every dynamic model has some degrees of incorrectness and every parameter associates with some degrees of identification error. The incorrectness and errors eventually result positioning or trajectory tracking errors, and even cause the system to be unstable. In the past two decades, intensive research activities have been devoted on the design of robust control systems and adaptive control systems for the robot in order to overcome the control system drawback caused by the model errors and uncertain parameters, and a great number of research results have been reported, for example, (Hsia, 1989), (Kou, and Wang, 1989), (Slotine and Li, 1989), ( Spong, 1992), and (Cheah, Liu and Slotine, 2006). However, almost parts of results associate with complicated control system design approaches and difficulties in the control system implementation for industrial robot manipulators. Recently, neural network technology attracts many attentions in the design of robot controllers. It has been pointed out that multi-layered neural network can be used for the approximation of any nonlinear function. Other advantages of the neural networks often cited are parallel distributed structure, and learning ability. They make such the artificial intelligent technology attractive not only in the application areas such as pattern recognition, information and graphics processing, but also in intelligent control of nonlinear and complicated systems such as robot manipulators (Sanger, 1994), (Kim and Lewis, 1999), (Kwan and Lewis, 2000), (Jung and Yim, 2001) (Yu and Wang, 2001). A new field in robot control using neural network technology is beginning to emerge to deal with the issues related to the dynamics in the robot control design. A neural network based dynamics compensation method has been proposed for trajectory control of a robot system (Jung and Hsia, 1996). A combined approach of neural network and sliding mode technology for both feedback linearization and control error compensation has been presented (Barambones and Etxebarria, 2002). Sensitivity of a neural network performance to learning rate in robot control has been investigated (Clark and Mills, 2000). In the following, we present a simple control system consisting of a traditional controller and a neural network controller with parallel structure for trajectory tracking control of 16

This paper presents the real-time control strategies developed to regulate both the ammonia and nitrate concentration in the effluent of the new Vitoria WWTP (Spain). Nitrate control aims at the optimal use of the denitrification potential at any moment. For this purpose, the proposed control algorithm continuously adapts the internal recycle flow in order to maintain a desired nitrate set-point in the anoxic zone. Ammonia control aims at maintaining the required average concentration of ammonia in the effluent by manipulating the Dissolved Oxygen (DO) set-point. The control strategies have been based on a hierarchical structure where a high-level or supervisory control selects the set-point of the low-level or conventional controllers. The design of the controllers was carried out using the Quantitative Feedback Theory QFT for the design of robust control systems. Moving average values of some variables have been introduced in order to eliminate the perturbations associated with the daily 24-hour profiles. The controllers have been verified using long-time dynamic simulations based on a mathematical model previously calibrated in pilot plant. Influent load and temperature used in the simulations correspond to the real values measured in the full-scale WWTP during 12 months. The results obtained in the simulations show the good performance and stability of the control strategies independently from external disturbances. A short-time experimental verification of the controllers in pilot plant with real wastewater is also presented.

This paper discusses the use of the computer algebra system Maple for demonstrating and applying process control principles. Control engineers and technicians must be familiar with the simple proportional-integral-derivative (PID) controller for single loop control, and with Bode plot analysis for selecting tuning constants and stability range for the controller. Two Maple procedures are discussed: PID() and Bode(). Procedure PID() provides the open loop plant response and the closed loop response to set point changes for a single loop with a PID controller in continuous or sampled environments. Bode() provides amplitude ratio (AR) and phase plots with appropriate scales and grid lines. These procedures demonstrate how the symbolic as well as numerical and graphical capabilities of Maple can be incorporated in procedures that can facilitate the analysis and design of control systems.

Integrated optimum design of structures and control systems is studied by using H 2 and $$ H_{\infty }$$ robust control formulations. It is derived that conventional simultaneous optimization approach by using these robust control laws can be approximated by a decoupled optimization approach in which the structures are optimized by shaping the structural singular values and then the controllers can be designed, namely, decoupled, sequential or successive design approach. It is shown that the proposed decoupled optimization approach can be used to design optimum robust structures and has certain advantages over the conventional simultaneous optimization procedures such as it avoids the drawbacks of pure robust control laws and faster, especially if the number of degrees of freedom (DOF) of the associated structure is large. The bounds for achievable robustness measures are also obtained. Following, simultaneous and decoupled optimization approaches are applied to active control of two structures. The optimization results are presented, and it is concluded that the proposed decoupled optimization approach yields the achieved global minimum much faster than the simultaneous optimization approach.

In the paper, the impact of atmospheric turbulence on the Unmanned Aerial Vehicles (UAV) motion is studied. The models of Dryden turbulence are discussed, and the airplane models of lateral directional under the atmospheric turbulence are deducted, the application of linear matrix inequality (LMI) is used to design a kind of robust flight control system based on H∞ control theory. The effect of the roll angle control of Robust flight controller is compared to that of PID controller and several characteristics of the state points in full-flight envelope are selected to validate the robust flight control system. The analysis shows that the designed controller is better than PID controller to attenuate the disturbance, and the flight control system has good dynamic performance and satisfactory robustness.

10 - Robust design of intelligent control systems to mitigate earthquake-induced vibrations under uncertain conditions

This paper deals with analysis and design of controllers for automatic control loops with dead times. The design of robust control systems and the examination of uncertain dead times are carried out by graphical representation of controller and plant parameters. Predictive controllers are also in this scope, and problems of uncertain parameters in this case are also examined.

Two algorithms are described that are useful in the computer-aided design implementation of the robust controller design scheme proposed by Horowitz (1963) and Horowitz and Sidi (1972).

Systems having transfer functions of the form where P1(s), P2(s) and Q(s) are polynomials, are called quasirational distributed systems (QRDS). They are encountered in processes modeled by hyperbolic partial differential equations. QRDS can have an infinity of right half‐plane zeros which causes large phase lags and can result in poor performance of the closed‐loop system with PID controllers. Theory on the asymptotic location of zeros of quasipolynomials is used to predict the nonminimum phase characteristics of QRDS and formulas are presented for factoring QRDS models into minimum and non‐minimum phase elements.A generalized Smith predictor controller design procedure for QRDS, based on this factorization, is derived. It uses pole placement to obtain a controller parameterization that introduces free poles which are selected to satisfy robustness specifications. The use of pole placement allows for the design of robust control systems in a transparent manner. Controller selection is generally better, simpler and more direct with this procedure than searching for optimal PID controller settings.