Can Özsoy

Experimental modeling of a pneumatic system

To know system model, structure and real parameters very well will increase the success of the control to be applied on it. This paper presents an experimental modelling study on a pneumatic system. PRBS signals are applied to the servovalves and system responses as position and chamber pressures are recorded. Later these data are digitally processed and input-output data for identification models are created. Pneumatic system model consists of a combination of two pressure models which each of them has two inputs and one output, defining pressure changes in the chambers and a velocity model which has one input-one output, defining piston motion. Parameters of the pressure models and the velocity model that are installed considering the mathematical equations of the system are identified by using Adaptech Midsys identification software. So, discrete-time models are obtained. Later they are converted into continuous-time models. During the identification, extended least squares method, parameter adaptation algorithm with decreasing gain and canonical parametrization of Guidorzi have been applied. As a result, parameters of the pneumatic system such as friction coefficients, moving mass, valve coefficients etc. are identifiable.

Pole-Placement Position Control of a Hydraulic Robot Arm

This paper discusses a single-input single-output discrete-time model developed to control joint position of a manipulator design for painting purposes. The input of the discrete-time model is the voltage into the control valve, and the output is the joint displacement. The model parameters are identified by using input-output data collected from the actual system. Recursive least-squares, square root, V-D factorization and variable forgetting factor methods were used for the estimation, and a good match between the model and actual system responses was obtained. The best estimation results were found by the U-D factorization algorithm according to the sum of the squared errors. Since identification results indicate a non-minimum phase system, a pole-placement self-tuning controller is designed for the purpose of joint trajectory control. The control signal computed off-line is applied to the electrohydraulic drive system and the perfect performance of the pole-placement controller is shown by the experimental studies.

Design of MPCs for a fixed wing UAV

Purpose The purpose of the paper is to examine a fixed wing unmanned aerial vehicle (UAV) as it undergoes five flight scenarios under straight and level, level climb, level turn, climbing turn and level steady heading sideslip conditions in a desired and controlled manner using constrained multi input multi output (MIMO) model predictive controllers (MPCs). Design/methodology/approach An MPC strategy based on the lateral and longitudinal linear models is proposed for the flight control design. Simulations are carried out for the nonlinear closed-loop aircraft Simulink model available from the University of Minnesota UAV research group with the implemented MIMO MPCs designed in this paper. Findings The results of the simulations show that the MIMO MPCs can achieve satisfactory performance and flying qualities under three different test conditions in terms of existing unmeasured outputs and unmeasured output disturbances. Practical Implications The MPCs designed in this paper can be implemented to UAVs. Therefore, the implementation is considered as an advanced research. Originality/value The proposed MPC design in this paper provides more flexibility in terms of tracking complex trajectories comparing with the classical controllers in the literature. Besides they provide to change more than one reference of the states at any time.

Hovering stability of a model helicopter

Purpose The purpose of this paper is to investigate the stability of a small scale six-degree-of-freedom nonlinear helicopter model at translator velocities and angular displacements while it is transiting to hover with different initial conditions. Design/methodology/approach In this study, model predictive controller and linear quadratic regulator are designed and compared within each other for the stabilization of the open loop unstable nonlinear helicopter model. Findings This study shows that the helicopter is able to reach to the desired target with good robustness, low control effort and small steady-state error under disturbances such as parameter uncertainties, mistuned controller. Originality/value The purpose of using model predictive control for three axes of the autopilot is to decrease the control effort and to make the close-loop system insensitive against modeling uncertainties.

Modelling of the raw mixing process in cement industry

This paper represents the identification of a raw blending system in a cement factory for advanced process control. Three different linear multivariable stochastic ARX (AutoRegressive with eXogenous input) models are proposed in which the inputs are the feed rates of the raw material components (low-grade limestone and iron ore) and the outputs are the iron oxide and/or lime module of the raw meal. The ARX models are parameterized giving the minimum number of parameters by the approach of R.P. Guidorzi (1975). The identification results show that these MISO and MIMO models are good models.

Mimo and siso self-tuning hybrid position/force control of robotic manipulators

This article presents a new approach for the hybrid position/force control of a manipulator by using self-tuning regulators (STR). For this purpose, the discrete-time stochastic multi-input multi-output (MIMO) and single-input single-output (SISO) models are introduced. The MIMO models output vector has the positions and velocities of the gripper expressed in the world (xyz) coordinate system as the components. The SISO model outputs are the hybrid errors consisting of the derivatives of the position and force errors at the joints. The inputs of both models are the joint torques. The unknown parameters of those models can be calculated recursively on-line by the square-root estimation algorithm (SQR). An adaptive MIMO and SISO self-tuning type controllers are then designed by minimizing the expected value of a quadratic criterion. This performance index penalizes the deviations of the actual position and force path of the gripper from the desired values expressed in the Cartesian coordinate system. An integrating effect is also included in the performance index to remove the steady-state errors. Digital simulation results using the parameter estimation and the control algorithms are presented and the performances of those two controllers are discussed.

Model predictive control of an unmanned aerial vehicle

Purpose The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV). Design/methodology/approach In this study, the constrained multivariable multiple-input and multiple-output (MIMO) model predictive controller (MPC) has been designed to control all outputs by manipulating inputs. The aim of the autopilot of UAV is to keep the UAV around trim condition and to track airspeed commands. Findings The purpose of using this control method is to decrease the control effort under the certain constraints and deal with interactions between each output and input while tracking airspeed commands. Originality/value By using constraint, multivariable (four inputs and seven outputs) MPC unlike the relevant literature in this field, the UAV tracked airspeed commands with minimum control effort dealing with interactions between each input and output under disturbances such as wind.

The air-jet texturing and twisting machine's and model predictive control based on state-space.

Abstract Abstract This paper presents the modeling and model predictive control based on state-space model of the air jet texturing and twisting machine. The system model shows the tension change in the twisting process. In designing of the Model Predictive Controller, two major objectives are considered. The first one is that the yarn tension in twisting process tracks a given reference tension; the second one is that the yarn tension with texturing process which is assumed as a step disturbance has to track the same ideal tension value. To verify the control algorithm performance, the proposed algorithm is compared with a PID controller designed with Ziegler-Nichols method. All computer simulations were completed in MIDSYS toolbox and MATLAB Simulink. The simulation study shows that the proposed model predictive control is an improvement over the PID controller for any control inputs.

Neural network modeling of a plate hot-rolling process

Abstract The force, torque and slab temperature models of each pass in the plate hot-rolling process are established in this paper. In the first, an experimental model of a plate hot-rolling is represented. The structure of this model is in the neural network forms and predicts the steady-state values of force, torque and slab temperature. In the second part, the proposed dynamical models are compared with the classical empiric models commonly used in the rolling practice. The experimental data obtained from Eregli Iron and Steel Factory in Turkey was used for developing both of the models. Copyright

The real-time position control of a pneumatic system with rapid prototyping approach

Abstract In this study, MATLAB and Simulink Real-Time Workshop© are used for developing real-time design and evaluating control algorithm because of their code flexibility and rapid prototyping features. The conventional PID algorithm performs to control the position of the pneumatic system. Optimal controller parameters are found experimentally under different working scenarios with the rapid prototyping approach in the real time.