A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time Domain Tuning Based on Integral Performance Indices

A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input-output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI{\lambda}D{\mu} controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.Comment: 30 pages, 20 figure

Control of Plant Wide Processes Using Fractional Order Controller

Fractional order PID controller is gaining popularity because the presence of two extra degrees of freedom, which have the potential to meet up the extra degrees in terms of uncertainty, robustness, output controllability .In other words, the fractional order PID controller is the generalization of the conventional PID controller. In the current study, the fractional order PID controller is designed and implemented for the complex and plant-wide processes. Distillation is the most effective separation process in the chemical and petroleum industries but with a drawback of energy intensivity To reduce the energy consumption two distillation columns can be combined into one column, which is known as dividing wall distillation column (DWC).Though the control of DWC has been addressed but it requires further R&D efforts considering the complexity in control of this process In this work the DWC is controlled by the advanced control strategy like fractional order PID controller. One of the challenging field in the process control is to design control system for the entire chemical plant. We have presented the control system for the HDA plant by implementing the fractional order PID controller. Both the discussed processes are multi-input-multi-output (MIMO) system and these processes are difficult to tune because of the presence of the interaction between the control loops. For the DWC process, the traditional simplified decoupler is used, while for the HDA plant process the equivalent transfer function model is used to handle the MIMO system. For tuning of the fractional-order PID controllers the optimization techniques have been used. The DWC controllers have been tuned by the ev-MOGA multi objective algorithm and the HDA plant controllers are tuned by the cuckoo search method

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.This research was funded by Basque Government and UPV/EHU projects

Advanced Robust Control Design For High Speed Tilting Trains

Tilting is a worldwide accepted technology concept in railway transportation. The particular benefit from tilting trains use is reduction in journey times due to speed increase on track corners (while maintaining acceptable passenger comfort), a point that facilitates improved customer service. An additional benefit is cost effectiveness due to the train running on existing rail tracks. Many countries opted to using tilting trains as means of fast public transportation (UK, USA, Canada, Sweden, Norway, Switzerland, Germany, Japan). The industrial norm of tilting high speed trains is that of precedence tilt whereby preview tilt enabling signals are used to provide the required information to the vehicles (it can also use a combination of track database information or GPS but the concept is the same). Precedence tilt tends to be complex (mainly due to the signal interconnections between vehicles and the advanced signal processing required for monitoring). Research studies of earlier than precedence schemes,i.e. the so-called nulling-type schemes whereby local-per-vehicle signals are used to provide tilt (a disturbance rejection-scheme although tends to suffer from inherent delays in the control feedback), are still an important research aim due to the simple nature and most importantly due to the more straightforward fault detection compared to precedence. Use of nulling-type tilt has been supported by recent studies in this context. The research presented in this thesis highly contributes to simplified single-inputsingle-output robust tilt control using the simplest rail vehicle tilt structure, i.e. an Active Anti-Roll Bar. Proposed are both robust conventional (integer-type) control approaches and non-conventional (non-integer) schemes with a rigorous investigation of the difficult to achieve deterministic/stochastic tilt trade-off. Optimization has been used extensively for the designs. A by-product of the work is the insight provided into the relevant tilting train model Non Minimum Phase characteristics and its link to uncertainty for control design. Work has been undertaken using Matlab, including proper assessment of tilt ride quality considerations