In this study, based on the Newton-Raphson method, the unconditional optimization of the “thin cylinder-plane” corona electrode system of high-voltage devices with a non-uniform or weakly uniform electric field, widely used in various technological processes of electron-ion technology, is analyzed. For this purpose, the condition of a self-sustaining electric discharge was used for the electrode system under consideration.

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

In this study, we propose an inverse optimal control based model predictive control approach. In inverse optimal control strategy, we firstly construct a stabilizing feedback control law and then search a meaningful cost functional. In that respect, we develop an alternative to solving the Riccati equation in MPC for linear time invariant system models. The control law is established with an appropriate scalar matrix which is found by using Big-Bang Big-Crunch(BB-BC) optimization algorithm. Simulations are done on a liquid level control system and the performance of the proposed method is compared with the performances of the classical model predictive, linear quadratic regulator and classical discrete time PID controller methods. The performance of the proposed controller is much better than the other controllers in respect to various criteria.

The conventional sliding mode controller needs the exact knowledge of system state measurements. In this study, nonlinear second order systems with unmeasured system states and bounded external disturbances are considered. The sliding mode observer based on nonlinear observation error dynamics is considered and the observer gain is adjusted by using a support vector machine based plant model. From the output of the support vector machine model, k-step ahead predictions are obtained. Therefore, the value of k is first analyzed to search for a proper value. It is also shown with the simulations that the stability conditions are satisfied for the chosen observer gains. Computer simulations are presented to show the effect of the proposed gain adjustment mechanism on the performance of output feedback sliding mode controller. It is seen that the trajectory tracking performance is improved with respect to a conventional output feedback sliding mode control scheme having constant sliding mode observer gains.

In this study, nonlinear second order systems with unmeasured system states and bounded external disturbances are considered. The observer gain of the sliding mode observer is adjusted by using a support vector machine based plant model. Computer simulations are presented to show the effect of the proposed adjustment mechanism on the performance of the output feedback sliding mode controller. It is shown that the trajectory tracking performance is improved with respect to a conventional output feedback sliding mode control scheme having constant sliding mode observer gains.

Abstract Abstract Sliding mode controller with time-varying sliding surfaces is a method to improve robustness and transient response of a system. In this study, a special time-varying mechanism is considered in which the sliding surface depends on angular information and sliding surface parameters are obtained with respect to the given initial conditions. Support vector machine regression algorithms are used in order to obtain the parameters of the time-varying sliding surface for any initial condition chosen from the predefined phase space interval. The support vector machine is trained for a number of initial conditions and computer simulations are presented to show the average improvement on randomly chosen test data with respect to the conventional sliding mode controller and to the time-varying sliding mode controller for which the parameters are tuned with a genetic algorithm for each initial condition. It is seen that, by using support vector machine based parameter tuning for different initial conditions, both the transient response of the system and reaching time is improved with respect to the conventional sliding mode controller and similar performance indices can be obtained with the controller in which parameters are tuned with a genetic algorithm having a computational time burden.