PID controller tuning for a multivariable glass furnace process by genetic algorithm

Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction. © 2015 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelber

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Benchmarking of Advanced Control Strategies for a Simulated Hydroelectric System

This paper analyses and develops the design of advanced control strategies for a typical hydroelectric plant during unsteady conditions, performed in the Matlab and Simulink environments. The hydraulic system consists of a high water head and a long penstock with upstream and downstream surge tanks, and is equipped with a Francis turbine. The nonlinear characteristics of hydraulic turbine and the inelastic water hammer effects were considered to calculate and simulate the hydraulic transients. With reference to the control solutions addressed in this work, the proposed methodologies rely on data-driven and model-based approaches applied to the system under monitoring. Extensive simulations and comparisons serve to determine the best solution for the development of the most effective, robust and reliable control tool when applied to the considered hydraulic system

A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach

Controllers, observers, and applications thereof

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed

Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system

For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence, one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic Gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system

Fault Tolerant Flight Control of Unmanned Aerial Vehicles

Safety, reliability and acceptable level of performance of dynamic control systems are the major keys in all control systems especially in safety-critical control systems. A controller should be capable of handling noises and uncertainties imposed to the controlled process. A fault-tolerant controller should be able to control a system with guaranteed stability and good or acceptable performance not only in normal operation conditions but also in the presence of partial faults or total failures that can be occurred in the components of the system. When a fault occurs in a system, it suddenly starts to behave in an unanticipated manner. Thereby, a fault-tolerant controller should be designed for being able to handle the fault and guarantee system stability and acceptable performance in the presence of faults/damages. This shows the importance and necessity of Fault-Tolerant Control (FTC) to safety-critical and even nowadays for some new and non-safety-critical systems. During recent years, Unmanned Aerial Vehicles (UAVs) have proved to play a significant role in military and civil applications. The success of UAVs in different missions guarantees the growing number of UAVs to be considerable in future. Reliability of UAVs and their components against faults and failures is one of the most important objectives for safety-critical systems including manned airplanes and UAVs. The reliability importance of UAVs is implied in the acknowledgement of the Office of the Secretary of Defense in the UAV Roadmap 2005-2030 by stating that, ”Improving UA [unmanned aircraft] reliability is the single most immediate and long-reaching need to ensure their success”. This statement gives a wide future scenery of safety, reliability and Fault-Tolerant Flight Control (FTFC) systems of UAVs. The main objective of this thesis is to investigate and compare some aspects of fault tolerant flight control techniques such as performance, robustness and capability of handling the faults and failures during the flight of UAVs. Several control techniques have been developed and tested on two main platforms at Concordia University for fault-tolerant control techniques development, implementation and flight test purposes: quadrotor and fixedwing UAVs. The FTC techniques developed are: Gain-Scheduled Proportional-Integral-Derivative (GS-PID), Control Allocation and Re-allocation (CA/RA), Model Reference Adaptive Control (MRAC), and finally the Linear Parameter Varying (LPV) control as an alternative and theoretically more comprehensive gain scheduling based control technique. The LPV technique is used to control the quadrotor helicopter for fault-free conditions. Also a GS-PID controller is used as a fault-tolerant controller and implemented on a fixedwing UAV in the presence of a stuck rudder failure case