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EN
In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisa-tion procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measure-ment noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.
EN
This paper presents the synthesis of a pneumatic control system for a selected configuration of the transport path for the delivery of rolling elements to spiral storage in inter-operational transport. The sequential control system sets the state of the manifolds to ensure a flow of workpieces to serve the subsequent storage. The essential module of the control system is the memory block. It is developed based on a storage filling sequence graph. The filling level of the storages can be monitored in one or two points using sensors. The rolling element displacement control sensors work together with appropriately designed systems to execute the delay of the rising and falling edge input signal. By using a two-level control of the filling level of the storages, it is possible to control the emptying status of the storages as a function of the technological time of removal of the items from the storage between the two control points. Control systems were synthesised and verified using Festo’s FluidSim computer programme.
EN
This paper presents an example of solving the parameter identification problem in the case of a robot with two degrees of freedom. In this study, a weighted recursive least squares algorithm was generalised to a case of nonlinear parameterisation in which the identified parameters did not satisfy the linear model. The generalisation involved linearising the model in the neighbourhood of current values of the parameter estimates. It was assumed that the estimates were updated every N steps of signal sampling. This method of identification can be applied whenever the parameters concerning a model need to be determined at the time of measurement. This is particularly useful in adaptive control when the plant parameters vary over time.
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