The application of a new PID autotuning method for the steam/water loop in large scale ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a 'forbidden region' on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller's parameters can be obtained by locating the frequency response of the controlled system at the edge of the 'forbidden region'. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method

An integral sliding-mode parallel control approach for general nonlinear systems via piecewise affine linear models

The fundamental problem of stabilizing a general nonaffine continuous-time nonlinear system is investigated via piecewise affine linear models (PALMs) in this article. A novel integral sliding-mode parallel control (ISMPC) approach is developed, where an uncertain piecewise affine system (PWA) is constructed to model a nonaffine continuous-time nonlinear system equivalently on a compact region containing the origin. A piecewise sliding-mode parallel controller is designed to globally stabilize the PALM and, consequently, to semiglobally stabilize the original nonlinear system. The proposed scheme enjoys three favorable features: (i) some restrictions on the system input channel are eliminated, thus the developed method is more relaxed compared with the published approaches; (ii) it is convenient to be used to deal with both matched and unmatched uncertainties of the system; and (iii) the proposed piecewise parallel controller generates smooth control signals even around the boundaries between different subspaces, which makes the developed control strategy more implementable and reliable. Moreover, we provide discussions about the universality analysis of the developed control strategy for two kinds of typical nonlinear systems. Simulation results from two numerical examples further demonstrate the performance of the developed control approach

Review of sliding mode control application in autonomous underwater vehicles

973-984This paper presents a review of sliding mode control for autonomous underwater vehicles (AUVs). The AUVs are used under water operating in the presence of uncertainties (due to hydrodynamics coefficients) and external disturbances (due to water currents, waves, etc.). Sliding mode controller is one of the nonlinear robust controllers which is robust towards uncertainties, parameter variations and external disturbances. The evolution of sliding mode control in motion control studies of autonomous underwater vehicles is summarized throughout for the last three decades. The performance of the controller is examined based on the chattering reduction, accuracy (steady state error reduction), and robustness against perturbation. The review on sliding mode control for AUVs provides insights for readers to design new techniques and algorithms, to enhance the existing family of sliding mode control strategies into a new one or to merge and re-supervise the control techniques with other control strategies, in which, the aim is to obtain good controller design for AUVs in terms of great performance, stability and robustness

Design Nonlinear Model Reference with Fuzzy Controller for Nonlinear SISO Second Order Systems

Model reference controller is considering as one of the most useful controller to specific performance of systems where the desired output is produced for a given input. This system used the difference between the outputs of the plant and the desired model by comparing them to produce the signals of the control. This paper focus on design a model reference controller (MRC) combined with (type-1 and interval type-2) fuzzy control scheme for single input-single output (SISO) systems under uncertainty and external disturbance. The model reference controller is designed firstly without fuzzy scheme based on an optimal desired model and Lyapunov stability theory. Then a (type-1 and Interval type-2) fuzzy controller Takagi-Sugeno type is combine with the suggested MRC in order to enhance the performer of it, the common parts between the two fuzzy systems such as: fuzzifier, inference engine, fuzzy rule-base and defuzzifier are illustrated. In this paper the proposed controller is applied to controla (SISO) inverted pendulum sustem and the Matlab R2015 software is used to carry out two simulation cases for the overall controlled scheme. The obtained results for the two cases show that the proposed MRC with both fuzzy control schemes have acceptable performance, but it have better performance with the interval type-2 fuzzy scheme

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

Design of Adaptive Sliding Mode Fuzzy Control for Robot Manipulator Based on Extended Kalman Filter

In this work, a new adaptive motion control scheme for robust performance control of robot manipulators is presented. The proposed scheme is designed by combining the fuzzy logic control with the sliding mode control based on extended Kalman filter. Fuzzy logic controllers have been used successfully in many applications and were shown to be superior to the classical controllers for some nonlinear systems. Sliding mode control is a powerful approach for controlling nonlinear and uncertain systems. It is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances, provided that the bounds of these uncertainties and disturbances are known. We have designed a new adaptive Sliding Mode Fuzzy Control (SMFC) method that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full nonlinear robot model as well as the joint positions and velocities. These estimates are used by the SMFC to generate the input torques. The combination of the EKF and the SMFC is shown to result in a stable adaptive control scheme called trajectory-tracking adaptive robot with extended Kalman (TAREK) method. The theory behind TAREK method provides clear guidelines on the selection of the design parameters for the controller. The proposed controller is applied to a two-link robot manipulator. Computer simulations show the robust performance of the proposed scheme

Adaptive interval type-2 fuzzy logic systems for vehicle handling enhancement by new nonlinear model of variable geometry suspension system

This research examines the emerging role of adaptive interval type-2 fuzzy logic systems (AIT2FLS) versus adaptive type-1 fuzzy logic system (AT1FLS) in vehicle handling by a new nonlinear model of the variable geometry suspension system (VGS) as a vehicle active suspension system. A proper controller is needed in order to have soft response and robustness against challenging vehicle maneuvers. Two controllers, including AT1FLS and AIT2FLS have been used in the paper. The proposed AIT2FLS can efficiently handle system uncertainties, especially in the presence of most difficult challenging vehicle maneuvers in comparison with AT1FLS. The interval type-2 fuzzy adaptation law adjusts the consequent parameters of the rules constructed on the Lyapunov synthesis approach. For this purpose, the kinematic equations are obtained for the vehicle double wishbone suspension system and they are substituted in a nonlinear vehicle handling model with eight degrees of freedoms (8DOFs). Thereby, a new nonlinear model for the analysis of VGS is obtained. The results indicate that between the two controllers, the proposed AIT2FLS has better overall vehicle handling, robustness and soft response