Decentralized adaptive suboptimal LQ control in Microsoft Excel VBA

Suboptimal linear quadratic (LQ) control has been popular very much because of its very good results in several tasks of control. It allows us to implement it on many systems even on unstable systems which it stabilizes. Adaptation enlarges the area of the usage especially in the case when standard controllers with fixed parameters gives unsatisfactory results. In this paper, the main attention is dedicated to the usage of the modified instrumental variable technique as the identification part of the self-tuning controllers, and to the implementation in Microsoft Excel. So this approach was verified by simulation in Microsoft Excel Visual Basic for Applications (VBA) on two input two output (TITO) systems. © 2019, Springer International Publishing AG, part of Springer Nature

An Adaptive Vibration Control Procedure Based on Symbolic Solution of Diophantine Equation

In this paper, the adaptive control based on symbolic solution of Diophantine equation is used to suppress circular plate vibrations. It is assumed that the system to be regulated is unknown. The plate is excited by a uniform force over the bottom surface generated by a loudspeaker. The axially-symmetrical vibrations of the plate are measured by the application of the strain sensors located along the plate radius, and two centrally placed piezoceramic discs are used to cancel the plate vibrations. The adaptive control scheme presented in this work has the ability to calculate the error sensor signals, to compute the control effort and to apply it to the actuator within one sampling period. For precise identification of system model the regularized RLS algorithm has been applied. Self-tuning controller of RST type, derived for the assumed system model of the 4th order is used to suppress the plate vibration. Some numerical examples illustrating the improvement gained by incorporating adaptive control are demonstrated

Řízení tepelného systému s použitím neuronové sítě a genetického algoritmu

Predictive Controller of a laboratory thermal process is presented in the paper. Process model is approximated by a neural network. On-line optimization is done by a genetic algorithm. Control algorithm is tested on the laboratory thermal process and compared to the standard control methods like predictive controller with the transfer and state-space linear model and the quadratic programming optimization method or a PI controller.V článku je prezentováno řízení tepelného systému. Model je vytvořen pomocí neuronové sítě. Online optimalizace je prováděna genetických algoritmem. Výsledky jsou porovnány se standardními metodami, kterými jsou prediktivní regulátor s přenosem a stavovým popisem a kvadratickým programováním a PI regulátor