Novel fuzzy logic controllers with self-tuning capability

Two controllers which extend the PD+I fuzzy logic controller to deal with the plant having time varying nonlinear dynamics are proposed. The adaptation ability of the first self tuning PD+I fuzzy logic controller (STPD+I_31) is achieved by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed motions while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller using same method of comparison study

Self-tuning run-time reconfigurable PID controller

Digital PID control algorithm is one of the most commonly used algorithms in the control systems area. This algorithm is very well known, it is simple, easily implementable in the computer control systems and most of all its operation is very predictable. Thus PID control has got well known impact on the control system behavior. However, in its simple form the controller have no reconfiguration support. In a case of the controlled system substantial changes (or the whole control environment, in the wider aspect, for example if the disturbances characteristics would change) it is not possible to make the PID controller robust enough. In this paper a new structure of digital PID controller is proposed, where the policy-based computing is used to equip the controller with the ability to adjust it's behavior according to the environmental changes. Application to the electro-oil evaporator which is a part of distillation installation is used to show the new controller structure in operation

Performance evaluation for SE113 flow control system plant using self-tuning fuzzy PI controller

The aim of this project is to evaluate the dynamic process performance of SE113 Flow Control System Plant using self-tuning Fuzzy PI controller. The experimental data is used to model the process and the control analysis is done using Self-Tuning Fuzzy PI Controller. The performance evaluation is based on the percent overshoot, rise time and settling time of the process. The overall performance is compared with the conventional Proportional-Integral control method. The results had shown that self-tuning Fuzzy PI controller simplify the tediousness in tuning the controller and enhance the capability of PI controller

Design of self-tuning minimum effort active noise control with feedback inclusion architecture

This paper presents the development of a self-tuning controller design of minimum effort active noise control (ANC) for feedforward single-input single-output (SISO) architecture which includes the feedback acoustic path in the controller formulation. The controller design law is derived for suitable self-tuning implementation and the self-tuning controller is evaluated in a realistically constructed ANC simulation environment. The self-tuning controller design involves a two-stage identification process where the controller is replaced by a switch. This switch is closed and opened in sequence generating two transfer functions which are then used in constructing the controller specified by a minimum effort control law. The implementation requires an estimate of the secondary path transfer function which can be identified either online or offline. The controller design and implementation are evaluated in terms of the level of cancellation at the observer through simulation studies for various values of modified effort weighting parameter in the range ⩽0γ⩽1. It was found that the optimal controller designed using this technique which is constrained only by the accuracy of the two models identified using recursive least squares algorithm, yields good cancellation level

Self-Tuning Adaptive-Controller Using Online Frequency Identification

A real time adaptive controller was designed and tested successfully on a fourth order laboratory dynamic system which features very low structural damping and a noncolocated actuator sensor pair. The controller, implemented in a digital minicomputer, consists of a state estimator, a set of state feedback gains, and a frequency locked loop (FLL) for real time parameter identification. The FLL can detect the closed loop natural frequency of the system being controlled, calculate the mismatch between a plant parameter and its counterpart in the state estimator, and correct the estimator parameter in real time. The adaptation algorithm can correct the controller error and stabilize the system for more than 50% variation in the plant natural frequency, compared with a 10% stability margin in frequency variation for a fixed gain controller having the same performance at the nominal plant condition. After it has locked to the correct plant frequency, the adaptive controller works as well as the fixed gain controller does when there is no parameter mismatch. The very rapid convergence of this adaptive system is demonstrated experimentally, and can also be proven with simple root locus methods

Implementation of self-tuning control for turbine generators

PhD ThesisThis thesis documents the work that has been done towards the development of a 'practical' self-tuning controller for turbine generator plant. It has been shown by simulation studies and practical investigations using a micro-alternator system that a significant enhancement in the overall performance in terms of control and stability can be achieved by improving the primary controls of a turbine generator using self-tuning control. The self-tuning AVR is based on the Generalised Predictive Control strategy. The design of the controller has been done using standard off-the-shelf microprocessor hardware and structured software design techniques. The proposed design is thus flexible, cost-effective, and readily applicable to 'real' generating plant. Several practical issues have been tackled during the design of the self-tuning controller and techniques to improve the robustness of the measurement system, controller, and parameter estimator have been proposed and evaluated. A simple and robust measurement system for plant variables based on software techniques has been developed and its suitability for use in the self-tuning controller has been practically verified. The convergence, adaptability, and robustness aspects of the parameter estimator have been evaluated and shown to be suitable for long-term operation in 'real' self-tuning controllers. The self-tuning AVR has been extensively evaluated under normal and fault conditions of the turbine generator. It has been shown that this new controller is superior in performance when compared with a conventional lag-lead type of fixed-parameter digital AVR. The use of electrical power as a supplementary feedback signal in the new AVR is shown to further improve the dynamic stability of the system. The self-tuning AVR has been extended to a multivariable integrated self-tuning controller which combines the AVR and EHG functions. The flexibility of the new AVR to enable its expansion for more complex control applications has thus been demonstrated. Simple techniques to incorporate constraints on control inputs without upsetting the loop decoupling property of the multivariable controller have been proposed and evaluated. It is shown that a further improvement in control performance and stability can be achieved by the integrated controller.Parsons Turbine Generators Ltd

Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects

The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. Two experiments have been performed, in which the performance of the neuro fuzzy control strategy has been compared with conventional PID control strateg