The finite-time P-PI controller has not been applied to a control problem having large observation noises. In this paper, the controller is applied to a position control of a magnetic levitation system. We show the effectiveness of the finite-time P-PI controller by computer simulation and experiments. Finally, we compare the controller with a traditional P-PI controller.

 In nonlinear control theory, multilayer minimum projection method is proposed for Control Lyapunov Function(CLF) design. The method generates a global control Lyapunov function from local control Lyapunov functions. However, the automatic generation method from local functions is not developed. In this paper, we consider the control problem including learning from exploring space.The learning from exploring is defined in discrete space, however the control problem is defined in continuous space. Thus, we need to relate the discrete space to the continuous space. This paper focuses on the CLF design including learning from exploring by Q-learning. Our goal is to develop a CLF design method.

 This paper addresses the design of a distributed non smooth tracking controller for a class of uncertain diffusive processes with homogeneous boundary conditions of Neumann type. Uncertainty in the diffusivity parameter, along with the presence of a sufficiently smooth distributed disturbance, characterize the considered class of processes.

The paper considers a dynamical controller with a linear PI structure complemented by a certain non.smooth integral term which gives the algorithm its main robustness properties. A constructive Lyapunov-based stability analysis is illustrated which leads to simple tuning conditions for the controller parameters guaranteeing the asympto tic tracking in the space L2 of sufficiently smooth reference profiles.

The main contribution of the present paper over recent related works (cfr. [19], [20]) is the relaxation of a quite restrictive assumption involving the external perturbation and, at the same time, the achievement of the control goal with milder information demand on the systems state. The good performance of the proposed control systems are verified by means of computer simulations.

 The aim of this work is to observe and control a biological nitrogen removal process. The paper illustrates the use of a linearized model in the design, an observer (software sensor) and a nonlinear feedback control technique based on a phenomenological model of the process. This model describes the complete dynamics of autotrophic and heterotrophic biomasses, biodegradable organic and nitrogenous matters. The control approach structure is combined with the estimation algorithm, for the on-line reconstruction of unmeasured biological states of the bioprocess. The efficiency of both the control and estimation are demonstrated via computer simulations.