Journal of Control
Year:2018 | Volume:12 | Issue:2 |Papers Count:6

An interactor matrix plays an important role in the multivariable linear and nonlinear control systems theory. This paper proposes a method to obtain the interactor matrix for nonlinear multivariable systems. The only existing algorithm works only on square systems; moreover, it cannot guarantee providing the interactor matrix for these systems. The proposed method of this paper improves the above algorithm so that both mentioned defects are solved. The modified algorithm uses the infinite zeros structure for the nonlinear system and then it obtains the structure of interactor matrix. The effectiveness of the introduced method has been shown using various examples.

In this paper, an online learning algorithm based on approximate dynamic programming is proposed to approximately solve the nonlinear continuous time differential graphical games with infinite horizon cost functions and known dynamics. In the proposed algorithm, every agent employs a critic neural network (NN) to approximate its optimal value and control policy and utilizes the proposed weight tuning laws to learn its critic NN optimal weights in an online fashion. Critic NN weight tuning laws containing a stabilizer switch guarantees the closed-loop system stability and the control policies convergence to the Nash equilibrium. In this algorithm, there is no requirement for any set of initial stabilizing control policies anymore. Furthermore, Lyapunov theory is employed to show uniform ultimate boundedness of the closedloop system. Finally, a simulation example is presented to illustrate the efficiency of the proposed algorithm.

In this paper, the tracking control of a robotic arm mounted on a wheeled mobile platform is considered. A nonlinear neural adaptive robust control algorithm is proposed for the output feedback tracking control of a wheeled mobile manipulator without measuring system velocities to deal with the unmodeled system dynamics, parametric uncertainties and external disturbances. A Lyapunov-based stability analysis shows that tracking and observation errors are Uniformly Ultimately Bounded (UUB) and converge to a small ball containing the origin. A Radial Basis Function Neural Network (RBFNN) is employed to compensate for the uncertainties of mobile manipulator dynamics. Nonparametric uncertainties and NN approximation errors are also compensated by an adaptive robust controller. In addition, hyperbolic tangent function is employed in the design of the output feedback controller to reduce the risk of actuators saturation and to produce smoother control signals. Finally, simulation results demonstrate the effectiveness of the proposed controller well.

This paper investigates asynchronous controller design problem for a class of continuous-time Markov jump linear systems. The mentioed asynchronous phenomenon is a case in which the system and the controller Markov chainsare not matched, however they are relevant according to certain probabilities. This phenomenon describes a realistic and practical situation which arises as a result of inaccurate observation of the system’ s Markov chain. The proposed design scheme considers the closed-loop system as a unified Markov jump linear system and utulizes the multiple Lyapunov function approach. By this approach, firstly, the stabilizability of the closed-loop system is ensured and then the asynchronous state-feedback controller is synthesizesed. The designed controller is formulated in terms of linear matrix inequalities; which are easy to check. A numerical example illustrates the usefulness of the developed method.

A dual stator winding induction motor (DSWIM) is a brushless squirrel-cage induction motor that contains a stator with two isolated three-phase windings wound with dissimilar number of poles. Generally, each stator winding is fed by an independent three-phase inverter. A direct vector control is a suitable method for controlling the DSWIM drive. In the vector control method, the estimation of the rotor flux is difficult at low speeds. In this paper, a direct vector control is proposed based on the rotor flux compensation. The achievement of this proposed control method is to maintain the standard performance of the motor drive at low speeds to reduce the power loss of the inverter unit compared to the conventional methods. In the proposed control method, the rotor flux is compensated with a PI controller. The proposed control scheme is based on the independent control of the rotor flux and the electromagnetic torque in the direct and orthogonal axises (d and q-axis), respectively. The rotor flux is compensated via reformed of the reference rotor flux. Also in this paper, for the first time, the reduction in number of switching elements of the inverter unit for the DSWIM drive can be achieved by utilizing five-leg and nine-switch power electronic converters. The advantages of using these proposed structures in the DSWIM drive are the reduction of the capital cost and also the reduction of power loss in the inverter unit.

In this paper a systematic methodology to design a modified incremental conductance and a model predictive control (MPC) for maximum power point tracking of a photovoltaic system is presented. The PV system includes a PV module that supplies a DC link and also an energy storage system using a buck DC-DC converter. The incremental conductance (INC) method with two modifications is employed for maximum power point tracking (MPPT) within P-V characteristic curve according to changes in weather condition. To avoid a finite set control signal, the average model of the PV system is analytically calculated and subsequently the model is linearized around MPP. Designing an MPC with continuous control set, its performance respect to finite control set MPC is compared. The simulations demonstrate that the proposed controller with augmented integrator could track the MPP faster and with less steady state error.