مجله کنترل
سال پنجم شماره 4 (زمستان 1390)

This paper presents a fractional-order adaptive controller based on sliding mode control for synchronization of commensurate fractional-order chaotic systems. The adaptive controller is a fractional PID controller, which the coefficients will be tuned according to a proper adaptation mechanism. Fractional PID coefficients are updated using the gradient method when a proper sliding surface is chosen. To illustrate the effectiveness and performance of the controller, the proposed controller is implemented on chaotic fractional-order Genesio Tessi and Coullet systems. Performance of fractional-order PID adaptive controller (PαIλDμ) based on synchronization error, and control signal is compared with the conventional adaptive controller (PID) and sliding mode controller (SMC). The simulation results show the efficiency of the proposed controller.

in this paper, a new adaptive predictive functional controller based on unstructured identification using Laguerre functions is presented and this algorithm is used to control of the superheated steam temperature in a thermal power plant. Combining predictive functional control with Laguerre series for constructing plant model leads to lower computational efforts and higher tracking precision. There is no requirement for accurate model in controller design. The designed controller can be adapted to big process variations and has high performance for controlling nonminimum phase and time delayed systems. The proposed algorithm is applied to superheated steam temperature control in a thermal power plant which has great variations in model with changing load unit. The proposed method is compared to a PID controller witch has two set of adjusted coefficients with load variations. Simulation results show better performance for the applied control method.

Nowadays, we are in the beginning of a revolution in technology. The revolution is entrance of quantum theory to the world of technology. An essential task for the quantum technology is the development of the general principles of quantum control theory. Thus, it is required that control engineers enter to this topic. In this paper, a survey and comparision on the dynamics and models of quantum systems such as BLM for open loop control and MME, SME and LQSDE for close loop control are done. In addition, their applications are considered. Also, a survey on quantum network is done that link the control engineering to quantum theory. Quantum network theory analyzes the subsystems interconnections and total model. This paper followed by the concept of stability based on the quantum version of small gain theorem. Also, the concept ofquantum systems controllability and investigation of controllability, which determines possibility of atomic or molecular transformation especially in open loop control, are considered. Finally, conclusions and discussion on the mentioned topics have been done.

Anti lock braking system (ABS) is one of the most important safety devises in vehicles during the severe braking. In this system, by regulating the wheel longitudinal slip at its optimum value, the maximum braking force is generated and therefore the minimum stopping distance for the vehicle is achieved. The hard nonlinearity due to the saturation of tire forces and modeling uncertainties are the main difficulties arising in the design of ABS. Also, the system states including the longitudinal speed and the wheel slip are not directly measurable and have to be estimated. In this paper, a nonlinear optimization based controller is analytically designed for ABS and is combined with a nonlinear estimator based on Unscented Kalman Filter (UKF). This estimation algorithm directly uses nonlinear equations of the system and does not require the linearization and differentiation. Here, the performance of the designed controller in the presence of states estimation and parametric uncertainties is analytically investigated. The simulation results indicate the efficiency of the proposed controller in tracking the optimal longitudinal wheel slip.

The process of empowering muscles in order to make them to a normal and common value is an expensive and prolonged work. There are some commercial exercise machines used for this purpose called rehabilitation systems. However, these machines have limited use because of some reasons. In this paper, an algorithm and an improved rule are presented for designing a rehabilitation intelligent system of lower limbs by a 6-DOF Stewart parallel robot. Impedance control and adaptive control are used to control of robot. Estimation and optimization of control parameters will be done by NN and GA, respectively. Thereafter, the results of simulations are presented by defining a physiotherapy standard mode on desired trajectory. MATLAB Simulink is used for simulations. It shows that proposed algorithm in comparation with other similar methods isa low-cost method and needs to less energy and force with high accuracy.

Shape Memory Alloys are widely used in the industry. Different researchers introduce successful models for phase transformation. In this work firstly the Lagoudas assumptionsis corrected and an enhanced model for phase transformation of these smart materials is introduced. Secondly we compare results of this model with cosine Liang-Rogers model. Results show a good consistency between this model and cosine Liang-Rogers model. Afterwards for the first time switching control for these operators is investigated. For this reason, special application of smart material is selected and a common Lyapunov function for all subsystems is selected to prove stability of the system under any arbitrary switching. Subsequently robust adaptive switching control is applied on it. Lastly, a time delay problem is supposed and it is shown that even by considering time delay for this switched system, it is globally asymptotically stable.

This paper presents an adaptive identifier based algorithm for online estimation of damping factor and frequency of an exponentially damped sinusoidal signal. The algorithm is based on an adaptive identifier which is a theoretical tool for the estimation of the parameters of an unknown linear system. The governing equations of the proposed algorithm are composed of a second order linear filter and two update laws that provide indirect estimation of the frequency and damping factor. Local stability analysis of the proposed method is presented using averaging theory. Simulation results confirm the analytical derivations and also the desirable performance of the proposed algorithm.