مجله کنترل
سال شانزدهم شماره 3 (پاییز 1401)

The two degrees of freedom servomechanism has many applications, including in gimbaled seekers. These mechanisms require closed-loop control to perform properly. In this paper, an observer-based multi-input-multi-output hybrid controller is designed for a two-degree-of-freedom servomechanism. Since in the model presented in this paper, disturbances on the mechanism are considered, so an extended state observer to estimate disturbance term to improve the controller performance. Also, due to the nonlinearity and two input- two output dynamics of these mechanisms, the use of combined nonlinear multivariate control methods to control the angle in these mechanisms will increase efficiency. For this purpose, nonlinear auxiliary control inputs are first determined in the first step. Then, in the second step, the nonlinear control input vector is determined using the multi-input-multi-output linear feedback method. In this step, a discrete time observer is used to estimate the uncertainty. The simulation results show that the proposed observer accurately estimates the disturbance and provides it to the controller. The controller designed using this information is able to control the output angles. Also, the results of the controller implementation designed in the processor in the loop test are presented.

In the present paper, a class of hybrid, nonlinear and non linearizable dynamic systems is considered. The noted dynamic system is generalized to a multi-agent configuration. The interaction of agents is presented based on graph theory and finally, an interaction tensor defines the multi-agent system in leader-follower consensus in order to design a desirable controller for the noted system. A general undirected, simple and connected graph topology is proposed for the system. Next, a nonlinear controller is designed for the multi-agent system to track a predefined reference trajectory and maintain the formation topology. An optimal controller, based on quasi-Newton optimization method is proposed in order to minimize a nonlinear cost function with indefinite variable sign hessian matrix. The convergence of previous optimization algorithms, namely the Newton optimization algorithm, regarding to variable sign hessian matrices fails. Thus, in the present paper, a quasi-newton optimization method is proposed based on eigenvalue modification to design a controller for the system. Afterward, the controller generalized for the multi-agent system and the performance of the controller is examined in a specific scenario of indefinite, variable hessian matrix problem. Consequently, the innovation of the present paper is proposed by considering the quasi-newton optimization method in order to overcome the disadvantages of traditional optimization methods in the problem of undefined hessian cost function. An example is provided in order to illustrate aforementioned claims and declared propositions.

In this paper, a new quantum hybrid controller for controlling the strongly eigenstate controllable systems, is designed. For this purpose, a Lyapunov control law is implemented when the target state is in reachable set of the initial state. On the other hand, if the target state is not in the reachable set of the given initial state, based on Grover algorithm, a new interface state that the target state is in its reachable set,  will be found  and the given initial state is transferred to the new interface state. Then, the new interface state is transferred to the target state, based on a designed Lyapunov control law.  A new algorithm, for implementing the presented method, is designed. Finally, a simulated example illustrates the applications and advantages of presented method.

In this paper, a predictive control based on the proposed hybrid model is designed to control the fluid height in a three-tank system with nonlinear dynamics whose operating mode depends on the instantaneous amount of system states. The use of nonlinear hybrid model in predictive control leads to a problem of mixed integer nonlinear programming (MINLP) which is very complex and time consuming to solve. One way to solve this problem is to approximate nonlinear equations with linear or piecewise affine (PWA) expressions. The linear approximation often has a large error in calculating the operating modes and the system states. The PWA approximation produces less error than the linear approximation, but its computational load is much higher. In this study, with the aim of reducing the computational load, a closed form model has been obtained for the equations of the three-tank system in each of the modes. The resulting system is an PWA, each mode being described by an PWA expression. Predictive control of this system is a mixed integer linear programming problem that can be solved by conventional solvers. To evaluate the performance of the proposed method and the possibility of using it online, the optimal control input sequence is calculated using MOSEK commercial solver in MPT toolbox, and at any sampling time only the first member of the sequence is applied to the precise modeled three-tank system in the Simulink/Stateflow environment. The simulation results indicate that the proposed controller performs the tracking efficiently and the constraints on the system states are also satisfied.

This paper deals with the optimal state observer of non-linear systems based on a new strategy. Despite the development of state prediction in linear systems, state prediction for non-linear systems is still challenging. In this paper, to obtain a future estimation of the system states, initially Taylor series expansion of states in their receding horizons was achieved to any specified order and then an analytic solution was developed for the prediction error problem, which resulted in a closed-form for non-linear optimal observer. In the proposed observer, the observer gain was optimally chosen among gains obtained from the analytic solution of the prediction error problem and satisfied the stability condition. Finally, the qualitative simulation results showed the effectiveness of the proposed method in the state observation.

In this manuscript, the laboratory inverted pendulum mounted on a cart has been controlled using 2-DOF self-tuning fuzzy PID controller. The robustness of the proposed method is investigated against the uncertainties in the system’s parameters and external disturbances. Moreover, the proposed method is tested against measurement noises. Stability of the proposed method is investigated using Bode diagrams in frequency domain. Performance of the 2Dof-fuzzy-PID controller is compared with the 2-DoF PID controller. The practical results indicates better performance of the proposed method.

Heading estimation is one of the main challenges in low-cost inertial navigation systems (INSs). Non-observability of heading angle with gravitational acceleration vector as well as inaccessibility of radio/satellite navigation in underwater vehicles increases the value of this challenge. Applying three-axis magnetometer and heading estimation from earth magnetic field components is one of the main approaches to accuracy enhancement of the low-cost inertial navigation systems. However, in order to achieve accurate heading estimation, the magnetometer must be appropriately calibrated for both sensor errors and presence of magnetic deviations. This paper aims to develop back-stepping algorithm for magnetometer calibration applied to measure the earth magnetic field components. In the proposed algorithm, the results of the prevalent spherical magnetic calibration are corrected based on vertical channel decomposition and magnetic field components in the horizontal plane. The algorithm is evaluated in the field tests executed on an Autonomous Underwater Vehicle (AUV).