İlyas Eker

Sliding mode control with PID sliding surface and experimental application to an electromechanical plant

In this study, a sliding mode control system with a proportional+integral+derivative (PID) sliding surface is adopted to control the speed of an electromechanical plant. A robust sliding mode controller is derived so that the actual trajectory tracks the desired trajectory despite uncertainty, nonlinear dynamics, and external disturbances. The proposed sliding mode controller is chosen to ensure the stability of overall dynamics during the reaching phase and sliding phase. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. The chattering problem is overcome using a hyperbolic function for the sliding surface. Experimental results that are compared with the results of conventional PID verify that the proposed sliding mode controller can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances.

Second-order sliding mode control with experimental application

In this article, a second-order sliding mode control (2-SMC) is proposed for second-order uncertain plants using equivalent control approach to improve the performance of control systems. A Proportional + Integral + Derivative (PID) sliding surface is used for the sliding mode. The sliding mode control law is derived using direct Lyapunov stability approach and asymptotic stability is proved theoretically. The performance of the closed-loop system is analysed through an experimental application to an electromechanical plant to show the feasibility and effectiveness of the proposed second-order sliding mode control and factors involved in the design. The second-order plant parameters are experimentally determined using input-output measured data. The results of the experimental application are presented to make a quantitative comparison with the traditional (first-order) sliding mode control (SMC) and PID control. It is demonstrated that the proposed 2-SMC system improves the performance of the closed-loop system with better tracking specifications in the case of external disturbances, better behavior of the output and faster convergence of the sliding surface while maintaining the stability.

Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application

Principal Component Analysis (PCA) is a statistical process monitoring technique that has been widely used in industrial applications. PCA methods for Fault Detection (FD) use data collected from a steady-state process to monitor T(2) and Q statistics with a fixed threshold. For the systems where transient values of the processes must be taken into account, the usage of a fixed threshold in PCA method causes false alarms and missing data that significantly compromise the reliability of the monitoring systems. In the present article, a new PCA method based on variance sensitive adaptive threshold (T(vsa)) is proposed to overcome false alarms which occur in the transient states according to changing process conditions and the missing data problem. The proposed method is implemented and validated experimentally on an electromechanical system. The method is compared with the conventional monitoring methods. Experimental tests and tabulated results confirm the fact that the proposed method is applicable and effective for both the steady-state and transient operations and gives early warning to operators.

Experimental nonlinear identification of a two mass system

The paper presents nonlinear modeling and on-line identification of a two mass mechanical system driven by a DC motor together with real-time experiments made. The paper also describes how to obtain a simpler identification routine for the nonlinear systems. Linear and nonlinear models for the system are obtained for identification purposes and the major nonlinearities in the system such as the Coulomb friction and the dead-zone are investigated and integrated in the nonlinear model. Hammerstein nonlinear system approach is used for the identification of the nonlinear system model. On-line identification of the linear and nonlinear system models is performed using the Recursive Least Squares (RLS) method. Results of the real-time experiments are graphically and numerically presented, and advantages of the nonlinear identification approach are definitely revealed.

Experimental on-line identification of an electromechanical system

Identification of electromechanical systems operating in open-loop or closed-loop conditions has long been of prime interest in industrial applications. This paper presents experimental on-line identification of an electromechanical system represented by a digital input/output model. The paper also bridges the theory and practice gap for applied researchers. Studies are carried out by formulating the mathematical model using differential equations and experimental discrete-time identification using on-line plant input-output data. A recursive least-squares method is used to estimate the unknown parameters of the system. Discrete-time data for the parameter identification are obtained experimentally from a setup constructed in the laboratory. A root-mean-square error criterion is used for model validation. Results are presented which show variations in parameters of the electromechanical system. It is demonstrated that identified model output and actual system output match. All tests are performed with no previous results from finite element simulations.

Operation and control of a water supply system.

The control of water supply systems is becoming more important, since there are increasing requirements to improve operation. A need exists to model and simulate water supply systems so that their behavior can be fully understood and the total process optimized. This paper describes the simulation and control of a water supply system consisting of a sequence of pumping stations that deliver water through pipelines to intermediate storage reservoirs. The system is represented by dominant system variables that represent active and passive dynamical elements. The hydraulic models include the nonlinear coupling between flow rates and reservoir heads. The bisection numerical solution approach is used to obtain a roughness dependent friction coefficient. The whole system is simulated and the results are presented and compared with the real-time measured data. A water level controller using the robust polynomial H(infinity) optimization method by manipulating pump speed is obtained. The stochastic nature of the disturbance and loads is considered for controller design. The parametrized dynamic weighting functions of the design theory are selected to achieve the required control functions and robustness.

Experimental evaluation of a model reference adaptive control for a hydraulic robot: a case study

This paper presents the implementation of an explicit model reference adaptive control (MRAC) for position tracking of a dynamically unknown robot. An auto regressive exogenous (ARX) model is chosen to define the plant model and the control input is optimised in a H2 norm to reduce computational time and to simplify the algorithm. The theory of MRAC falls into a description of the various forms of controllers and parameter estimation techniques, therefore, applications may require very complicated solution methods depending on the selected laws. However, in this study, the proposed MRAC shows that applications may be as easy as classical control methods, such as PID, by guaranteeing the stability and achieving the convergency of the plant parameters. Despite the selected simple control model, simple optimisation method and drawbacks of the robot the experimental results show that MRAC provides an excellent position tracking compared with conventional control (PID). Many experimental implementations have been done on the robot and one of them is included in the paper.

Second-order integral sliding-mode control with experimental application.

In the present study, a second-order sliding-mode controller is proposed for single-input single-output (SISO) uncertain real systems. The proposed controller successively overcomes the variations caused by the uncertainties and external load disturbances although an approximate model of the system is used in the design procedure. An integral type sliding surface is used and the stability and robustness properties of the proposed controller are proved by means of Lyapunov stability theorem. The chattering phenomenon is significantly reduced adopting the switching gain with the known parameters of the system. Thus, the proposed controller is suitable for long-term application to the real systems. The performance of the proposed control scheme is validated by a real system experiments and the results are compared with the similar controllers presented in the literature.

Experimental online identification of a three-mass mechanical system

Plant identification has long been of prime interest in industrial applications. This paper presents experimental online identification of a DC motor in digital input/output model. Studies are carried out by formulating the mathematical model of the plant using differential equations, and discrete-time identification using online plant input-output data. A recursive least squares method is used to estimate the unknown parameters of the motor. Discrete-time data are obtained experimentally carrying out on a DC motor setup. A direct identification method is used in closed loop identification. The root-mean-square (RMS) error criterion is used for model validation. Results obtained in open loop and closed loop identifications are presented which show variations in machine parameters.

The design of robust multi-loop-cascaded hydro governors

In the present paper, a new multi-loop-cascaded governor is proposed for hydro turbine controls. A turbine model is obtained that covers the effects of the water hammer, travelling waves, inelastic water penstocks and head loss due to the friction. Plant parameter uncertainties are taken into account to investigate stability robustness. The polynomial H∞ robust control design method is used to design the multi-loop-cascaded governor. Water and load disturbance, and permanent oscillations in the power systems such as inter-area modes are included in the robust design procedure. Robust performance is achieved by using parameterised dynamic weighting functions of the design theory. The designed governor ensures that the overall system remains asymptotically stable for all norm-bounded uncertainties. Simulation results show that the system performance specifications and stability margins are improved significantly even in the presence of parameter uncertainties.