مجله کنترل
سال شانزدهم شماره 1 (بهار 1401)

This paper proposes a novel structure of model predictive control algorithm for piecewise affine systems as a particular class of hybrid systems. Due to the time consuming and computational complexity of online optimization problem in MPC algorithm, the explicit form of MPC which is called Explicit MPC (EMPC) is applied in order to control of buck converter. Since the EMPC solves the optimization problem only once and in offline manner, this strategy is suitable for hybrid systems with fast dynamics. As opposed to typical EMPC that is uses only the first element of optimal input vector, the proposed strategy uses all entries of the control sequence with optimal weighting factors. In proposed EMPC, two separate optimization problems are solved at each algorithm step. The first one is related to EMPC optimization problem and the second optimization problem is concerned to finding optimal weighting factors so as to minimize the error signal at each step. The convergence property of the proposed EMPC towards to the desired value has been proved and the simulation results shows the better performance of the proposed EMPC strategy than the typical one, if the weighting factors and control horizons are adjusted properly.

The idea of using multi-model controllers has been established on the decomposition of complicated nonlinear systems into multi local models, designing the local controllers, and their composition for the system input control. Since the proper performance and the final system close loop stability are vital in multi-model controllers designing, the main problem in multi-model controllers is the number of the local models and their position not paying attention to which will result in redundancy, estimation complexity and the decreased performance of the system. Determining a specific margin based on nonlinear model characteristics is a good criterion for the classification. The first margin largely depends on the preliminary knowledge of designing the wrong selection of which will add to the redundancy of the local models and the problem’s complexity. In this article, the maximum stability margin parameter which is the main feature in each sub system and the best choice for the local controllers clustering has made the classification possible and also guaranteed close loop stability of the system. Based on the gap metric and the maximum stability margin, an optimal solution for the number of the local controllers and their position can be obtained by the use of genetic algorithm. The cost function is defined by the complementary sensitivity function and the sensitivity function and guarantees the maximum stability margin, the optimal performance, and the close loop stability of the system, respectively. Another challenge in designing multi-model controllers is the transient performance degradation when switching from one local model to another. Soft/hard switching has been suggested as a solution by the researchers before. In this article, given that the nonlinear system input depends on the online controller error signal and feedback coefficient of the offline controller, transient performance degradation in the switching phase will be solved. To evaluate the presented multi-model controller design procedure, a sandwich plate nonlinear vibration suspension with uncertainty in basic equations is proposed. Lagrange Reilly-Ritz method is used to derive the nonlinear equations of the plate.

In this paper, a new predictive filter for alignment of the inertial navigation system with a nonlinear model is presented, and its stability is analyzed. The stability is analyzed according to the Lyapunov method. The Lyapunov function is selected as a quadratic cost function. This method provides sufficient conditions for the stability of the estimated state against measurement uncertainty and noise. The proposed method is used to improve the initial alignment accuracy of the inertial navigation system with a large misalignment azimuth angle. The measurement model of this system is nonlinear and has a modeling error. In this method, the model error is estimated and compensated in the filter algorithm; therefore, the error of the state estimation is reduced in the updating step. By performing various simulations of this method on the real data of microelectromechanical (MEMS) sensor and comparing it with EKF and UKF, it is observed that the proposed method has higher accuracy and convergence speed than EKF and UKF. The new filter proves to have asymptotic stability.

This paper presents a new control method based on Lyapunov function approach for simultaneous control of grid currents, circulating currents and modules voltages of a modular multilevel converter. Unlike existing methods that use nested and multi-loop control structures, the proposed method has a single-loop structure and ensures the global asymptotic stability of the closed-loop system. In this regard, first, the dynamic equations of the converter and the grid are extracted using the averaging technique, and then the coordinates of the steady-state operating point are calculated. Next, using the obtained operating point coordinates and the dynamic equations of the main system, the error dynamics are computed. Finally, these equations and the Lyapunov function, based on the error signals, are used for the analytic calculation of the control inputs. The simulation results confirm the efficiency of the proposed control method.

In this study, a comprehensive quality measure criterion is developed to evaluate the performance of the identified models for nonlinear hybrid systems using support vector regression-based techniques. The proposed quality measure criterion includes all the factors that affect the quality of the identified models, namely identification error, quality of the switching signal, and model complexity. Using the proposed criterion, the resulting models of hybrid systems identification can be efficiently compared and the best model with acceptable complexity, tolerable identification error, and desirable switching signal quality will be selected. This quality measure criterion prevents selecting the complex models relying on the Occam’s Razor theorem. Besides, it provides the possibility of comparing the effects of different kernel functions on the identified models considering the aforementioned factors.

In this paper, a sensorless flatness based controller with nonlinear observer for non-ideal boost converter is proposed. Losses are modeled with a voltage source series with input and a current source parallel to output. By a one-loop control structure with flatness property, both output voltage and inductor current is regulated. But, flatness based control is a model based method and requires extra sensors for obtaining data of all system parameters. For reducing numbers of sensors, a nonlinear observer is used to estimate output current and input voltage. So, sensors of these two parameters are eliminated. Simulation and experimental results are given to validate the proposed controller and robustness of proposed controller to variation of system parameters is obvious. In addition, simulation results of proposed controller are compared with a two-loop controller including PI and energy control

Flapping wing micro aerial vehicle (FWMAV) has a multi-body and periodic dynamics, which is influenced by unsteady aerodynamics. These features make it more difficult to control. Ignoring of the wing inertia, dynamics averaging, and using a simple aerodynamic model are the simplifying assumptions for conventional model-based control, although they may result in inaccurate control. To overcome these difficulties, a multi-body control is proposed based on a model-free adaptive variable structure control (MFAVSC) approach. This is one of the first frameworks for multi-body control of FWMAV. This is the first proposed method for the FWMAV control, which considers multi-body, nonlinear and time-varying dynamics as well as main aerodynamic characteristics in an integrated framework. MFAVSC takes advantage of input/output data, while not including any explicit model information. At first, the nonlinear FWMAV dynamics is transformed into an equivalent dynamic linearization description with a concept called pseudo-partial derivative (PPD). After estimating the PPD matrix, model-free adaptive control law is designed based on the optimal criteria. Then, it is augmented by a variable structure control term to guarantee the stability, as well as speeding up its convergence. Finally, simulation results demonstrate the effectiveness of the proposed scheme to trajectory control of the FWMAV