Optimal control of Takagi–Sugeno fuzzy-model-based systems representing dynamic ship positioning systems

Orthogonal function approach (OFA) and the hybrid Taguchi-genetic algorithm (HTGA) are used to solve quadratic finite-horizon optimal controller design problems in both a fuzzy parallel distributed compensation (PDC) controller and a non-PDC controller (linear state feedback controller) for Takagi–Sugeno (TS) fuzzy-model-based control systems for dynamic ship positioning systems (TS-DSPS). Based on the OFA, an algorithm requiring only algebraic computation is used to solve dynamic equations for TS-fuzzy-model-based feedback and is then integrated with HTGA to design quadratic finite-horizon optimal controllers for TS-DSPS under the criterion of minimizing a quadratic finite-horizon integral performance index, which is also converted to algebraic form by the OFA. Integration of OFA and HTGA in the proposed approach enables use of simple algebraic computation and is well adapted to the computer implementation. Therefore, it facilitates design tasks of quadratic finite-horizon optimal controllers for the TS-DSPS. The applicability of the proposed approach is demonstrated in the example of a moored tanker designed using quadratic finite-horizon optimal controllers.     

无刷直流电机的电流跟踪控制

基于无刷直流电机数学模型,采用Simulink建立了无刷直流电机调速系统的仿真模型,研究了两种电流跟踪控制技术。系统采用双闭环控制,速度环采用PI调节器,电流环分别采用滞环比较电流跟踪控制和三角波比较电流跟踪控制。对两种电流跟踪控制方案进行了仿真比较,得出的结论为高性能无刷直流电机控制器的设计提供了参考。     

Optimal fuzzy controller based on non-monotonic Lyapunov function with a case study on laboratory helicopter

This paper presents a new approach to design an observer-based optimal fuzzy state feedback controller for discrete-time Takagi–Sugeno fuzzy systems via LQR based on the non-monotonic Lyapunov function. Non-monotonic Lyapunov stability theorem proposed less conservative conditions rather than common quadratic method. To compare with optimal fuzzy feedback controller design based on common quadratic Lyapunov function, this paper proceeds reformulation of the observer-based optimal fuzzy state feedback controller based on common quadratic Lyapunov function. Also in both methodologies, the dependence of optimisation problem on initial conditions is omitted. As a practical case study, the controllers are implemented on a laboratory twin-rotor helicopter to compare the controllers' performance.     

A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range PMSM drive

This paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.     

Air management in a diesel engine using fuzzy control techniques

Air management for diesel engines is a major challenge from the control point of view because of the highly nonlinear behavior of this system. For this reason, linear control techniques are unable to provide the required performance, and nonlinear controllers are used instead. This article discusses two fundamental steps when designing a control system. Firstly, a methodology to identify Takagi–Sugeno (T–S) structures using experimental data is proposed. Secondly, the design of a fuzzy controller in PDC structure (Parallel Distributed Compensation) is presented. The parameters of this controller are obtained from a LMI (Linear Matrix Inequalities) minimization problem.     

Variable structure surge control for constant speed centrifugal compressors

This paper presents the application of active control of surge in constant speed centrifugal compressors based on the Moore–Greitzer (MG) model. Different controllers are developed for a compression system equipped with a close-coupled valve (CCV) and a throttle control valve (TCV). The combination of the two valves helps suppress surge and assists in overcoming the drawbacks of each valve when it is used individually. The presented controllers are evaluated based on many performance indices. Accordingly, a case-based variable structure controller is developed that succeeds to stabilize the system for various operating conditions and to extend the stable range well beyond the surge line. A flow extension of 76% and a stable throttle level of 0.1, with substantially decreased pressure settling times, are reported. The developed controller performs significantly better compared to other nonlinear controllers, such as the backstepping controller.     

Stability conditions of fuzzy systems and its application to structural and mechanical systems

This paper provided the stability conditions and controller design for a class of structural and mechanical systems represented by Takagi–Sugeno (T–S) fuzzy models. In the design procedure of controller, parallel-distributed compensation (PDC) scheme was utilized to construct a global fuzzy logic controller by blending all local state feedback controllers. A stability analysis was carried out not only for the fuzzy model but also for a real mechanical system. Furthermore, this control problem can be reduced to linear matrix inequalities (LMI) problems by the Schur complements and efficient interior-point algorithms are now available in Matlab toolbox to solve this problem. A simulation example was given to show the feasibility of the proposed fuzzy controller design method.     

Iterative performance improvement of fuzzy control systems for three tank systems

This paper suggests the performance improvement of fuzzy control systems (FCSs) for three tank systems using iterative feedback tuning (IFT). The stable design of Takagi–Sugeno–Kang fuzzy controllers is guaranteed by means of a stability theorem based on LaSalle’s global invariant set theorem formulated for a class of multi input-multi output (MIMO) nonlinear processes. An IFT algorithm characterized by setting the step size to guarantee the FCS stability is proposed. The theoretical approaches are applied in a case study that deals with the IFT-based stable design of fuzzy controllers dedicated to the level control of a cylindrical three tank system as a representative MIMO system. A set of experimental results for a laboratory setup illustrates the performance improvement.     

Fuzzy Logic Based Approach to Design of Flight Control and Navigation Tasks for Autonomous Unmanned Aerial Vehicles

This paper proposes a fuzzy logic based autonomous navigation controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed under the main navigation system for the control of the altitude, the speed, and the heading, through which the global position (latitude–longitude) of the air vehicle is controlled. A SID (Standard Instrument Departure) and TACAN (Tactical Air Navigation) approach is used and the performance of the fuzzy based controllers is evaluated with time based diagrams under MATLAB’s standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed six-degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.     

T–S fuzzy model with nonlinear consequence and PDC controller for a class of nonlinear control systems

In this paper a new Takagi–Sugeno (T–S) fuzzy model with nonlinear consequence (TSFMNC) is presented which can approximate a class of smooth nonlinear systems, nonlinear dynamical systems and nonlinear control systems. It is also proved that Takagi–Sugeno fuzzy controller with nonlinear consequence (TSFCNC) can be used to approximate a class of nonlinear state-feedback controllers using the so-called parallel distributed compensation (PDC) method. The inverted pendulum problem has been simulated with TSFCNC and compared with Takagi–Sugeno fuzzy controller with linear consequence (TSFCLC) and the results show that TSFCNC performs better than TSFCLC. A real-life example of dynamic positioning of ship is simulated and the results also show that TSFCNC performs better than TSFCLC.     

Output feedback controller design of Takagi–Sugeno fuzzy systems

This paper presents new systematic design methods of two types of output feedback controllers for Takagi–Sugeno (T–S) fuzzy systems, one of which is constructed with a fuzzy regulator and a fuzzy observer, while the other is an output direct feedback controller. In order to use the structural information in the rule base to decrease the conservatism of the stability analysis, the standard fuzzy partition (SFP) is employed to the premise variables of fuzzy systems. New stability conditions are obtained by relaxing the stability conditions derived in previous papers. The concept of parallel distributed compensation (PDC) is employed to design fuzzy regulators and fuzzy observers from the T–S fuzzy models. New stability analysis and design methods of output direct feedback controllers are also presented. The output feedback controllers design and simulation results for a nonlinear mass-spring-damper mechanical system show that these methods are effective.     

Multiple fuzzy model-based temperature predictive control for HVAC systems

In this paper, a multiple model predictive control (MMPC) strategy based on Takagi–Sugeno (T–S) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented. The overall control system is constructed by a hierarchical two-level structure. The higher level is a fuzzy partition based on AHU operating range to schedule the fuzzy weights of local models in lower level, while the lower level is composed of a set of T–S models based on the relation of manipulated inputs and system outputs correspond to the higher level. Following this divide-and-conquer strategy, the complex nonlinear AHU system is divided into a set of T–S models through a fuzzy satisfactory clustering (FSC) methodology and the global system is a fuzzy integrated linear varying parameter (LPV) model. A hierarchical MMPC strategy is developed using parallel distribution compensation (PDC) method, in which different predictive controllers are designed for different T–S fuzzy rules and the global controller output is integrated by the local controller outputs through their fuzzy weights. Simulation and real process testing results show that the proposed MMPC approach is effective in HVAC system control applications.     

Control system design and input shape for orientation of spherical wheel motor

This paper presents control system design of a multi degrees-of-freedom (DOF) spherical wheel motor (SWM) in a class of ball-joint-like direct drive actuators to control orientation of the shaft. Three controllers (model based open-loop (OL), two closed-loop (CL) controllers) based on a push-pull torque model have been developed from rotor dynamics and magnetic field model referred to here as Distributed Multipole (DMP) model which provides accurate torque computation. The model based OL controller along with three control input shapes has been examined for the inclination control. Their results offer physical intuition, practical effectiveness, and also demonstrate the accuracy of magnetic field and torque computation. Then, two feedback controllers, a PD controller with and without the observer, have been developed for regulating its rotor inclination and experimentally evaluated against the OL controller. Finally, the performance on each controller has been compared to show the effect of the controllers on transient response. The experimental results verify control system design and demonstrate the motion capability of the SWM. While the experimental results illustrate the ability to control, they also reveal constraints and limitations of the controllers and provide insights for future design of control systems for the SWM.     

Comparison between linear and nonlinear control strategies for variable speed wind turbines

The purpose of this work is to compare some linear and nonlinear control strategies, with the aim of benefiting as well as possible of wind energy conversion systems. Below rated wind speed, the main control objective is to perform an optimal wind power capture while avoiding strong loads on the drive train shafts. To explicitly take into consideration the low speed shaft flexibility, a two-mass nonlinear model of the wind turbine is used for controllers synthesis. After adapting a LQG controller based on the linearized model, nonlinear controllers based on a wind speed estimator are developed. They take into account the nonlinear dynamic aspect of the wind turbine and the turbulent nature of the wind. The controllers are validated upon an aeroelastic wind turbine simulator for a realistic wind speed profile. The study shows that nonlinear control strategies bring more performance in the exploitation of wind energy conversion systems.     

Multiple models switching control based on recurrent neural networks

This paper presents a novel approach in designing adaptive controller to improve the transient performance for a class of nonlinear discrete-time systems under different operating modes. The proposed scheme consists of generalized minimum variance (GMV) controllers and a compensating controller. GMV controllers are based on the known nominal linear multiple models, while the compensating controller is based upon a recurrent neural network. The adaptation law of network weight is derived from Lyapunov stability theory. A suitable switching control strategy is applied to choose the best controller by the performance indices at every sampling instant. Simulations are discussed in order to illustrate the merits of the proposed method.     

Modeling and speed control design of an ethanol engine for variable speed gensets

This paper presents the development of a discrete dynamic mean value engine model (MVEM) suitable for the design of speed controllers of ethanol fueled internal combustion engines (ICE), to be used in variable speed gensets. Two MVEMs are developed for the ICE: the Time Based model and the Crank Based model. The speed controller design is held through the discretization and linearization of the Crank Based MVEM. This model is used due to the advantages over the time based MVEM especially with respect to the transport delay which becomes constant. Two approaches for the ICE speed control are investigated: (i) a single loop gain-scheduled proportional integral (PI) controller and (ii) a dual loop control based on an internal gain-scheduled Manifold Absolute Pressure (MAP) feedback loop and an outer loop composed of a gain-scheduled PI controller. The control design is developed in the frequency domain and its stability is ensured by the phase and gain margins. In addition, an integral anti-windup and a feed forward action are also proposed to improve the behavior during control law saturation, improve transient responses and disturbance rejection capability. Experimental results on a 50 kW generator set are provided to validate the controllers and to demonstrate the performance of the system.     

PID controller design for nonlinear systems represented by discrete-time local model networks

This paper deals with proportional–integral–derivative (PID) controller design for nonlinear systems represented by local model networks. The proposed method is based on the concept of parallel distributed compensators where the scheduling of the local model network is adopted for the PID parameters. The proposed design method for nonlinear PID controllers considers closed-loop stability by means of a Lyapunov stability criterion as well as closed-loop performance. All PID parameters are determined by a multi-objective genetic algorithm (multiGA), which handles the trade-off between stability and performance. A simulation example demonstrates the effectiveness of the proposed method.     

High performance of brain emotional intelligent controller for DTC-SVM based sensorless induction motor drive

This paper introduces the application of the induction motor (IM) drive brain emotional intelligent controller (BEIC). Intelligent regulation, modelled on the human brain, is capable of generating impulses and is used as a controller. A Model Reference Adaptive System is developed using stator current and stator voltages, which are further developed with BEIC to approximate the rotor rpm. This paper proposes that speed estimation using BEIC for direct torque control (DTC) of IM drive. The experimental work is conducted on a hardware-in-loop mechanism using a real-time digital simulator (Op-RTDS-OP5600). The simulation and test results are discussed. The proposed method is compared to the DTC-SVM-based IM drive speed control with the existing controllers.

     

Finite-time stability and stabilisation for a class of nonlinear systems with time-varying delay

This paper is concerned with the problems of finite-time stability (FTS) and finite-time stabilisation for a class of nonlinear systems with time-varying delay, which can be represented by Takagi–Sugeno fuzzy system. Some new delay-dependent FTS conditions are provided and applied to the design problem of finite-time fuzzy controllers. First, based on an integral inequality and a fuzzy Lyapunov–Krasovskii functional, a delay-dependent FTS criterion is proposed for open-loop fuzzy system by introducing some free fuzzy weighting matrices, which are less conservative than other existing ones. Then, the parallel distributed compensation controller is designed to ensure FTS of the time-delay fuzzy system. Finally, an example is given to illustrate the effectiveness of the proposed design approach.     

Feedback controller design for variable voltage variable speed induction motor drive via Ant Colony Optimization

This paper deals with the development of feed back controller design for a voltage controlled induction motor drive employing an enhanced optimization algorithm derived from the principles of foraging of natural ants. A linearized incremental model of a voltage controlled induction motor drive is shown to exhibit parameter variation at different operating points of the drive system. A PI-controller derived at a typical operating point using traditional methods does not give satisfactory performance for a wide bandwidth of load and reference speed changes. The newly developed Ant Colony Optimization technique enforces continuous exploration of the solution space and identifies optimal controller structure. The development of optimization algorithm and its application to feed back controller design for a variable voltage induction motor drive is well documented in this paper. Experimental and simulation results are presented to validate the efficacy of the optimized controller.