Papers by Keyword: Optimal Control

Abstract: Discrete mechanics and optimal control for constrained systems (DMOCC) is a new developed solution for mechanical control. The formulation of DMOCC is attributed to nonlinear equality constraints for the minimization of an appointed cost function. Traditionally, the equations are solved by standard sequential quadratic programming (SQP) algorithm, which suffers for relatively slow convergence speed. In this paper, active set algorithm is introduced to the numerical solution of DMOCC. By comparison of these two algorithms for the example of transferring of the rigid sphere, the efficiency of active set algorithm is validated.

Abstract: This paper describes a method for the synthesis of optimal control. The maximum principle is a simple way of creating the optimal management of multi-channel object. The optimality criterion is time.

Abstract: An optimization of PID controller based on indices of linear quadratic state and integration of error (IE) for Buck system is proposed. The optimal original disturbance suppression can be settled well by linear quadratic regulator (LQR) method, while assignment its weighted matrix Q and R is a matter. On the state space description of typical 2-order system, the equations among diagonal weighted matrix, parameters of PID, and characteristics of closed loop system have been established. With this, the steady, rapid and accurate nature of control system are expressed explicitly with the weighting matrix, and optimization of integral error can be easily solved due to integral velocity with constraint of given damping ratio. At last, LQR based optimal control systems with various Q for Buck are simulated, and simulations show the proposed approach have the dual optimality due to disturbance suppression.

Abstract: Adopting the integrated TOPSIS intelligent energy optimization control strategy, and compared to conventional single control strategy on energy consumption of greenhouse equipment under closed condition, this paper arrives at the best energy saving optimization control strategy with comprehensive benefits. The result shows that, integrated intelligent optimizing control was obviously more energy saving compared to those did not take optimization control. Specific results as follows: TOPSIS integration strategy with energy saving of 725.39kwh, energy-saving rate of 44.19%.This shows that the proposed integrated intelligent energy optimization control strategy and energy saving effect is remarkable.

Abstract: An improved cooperative method to control a nonlinear uncertain system is raised in this paper. First, the optimal control law is introduced to attain the desired dynamics of the linear part of original system. Then, a nonlinear PI controller is used to remove the influence of uncertain unit. It is proved by simulation that this method is effective to control this special system and is easy to be carried in applications.

Abstract: The strategy of unmanned aerial vehicle air combat can be described as a differential game problem. The analytical solutions for the general differential game problem are usually difficult to obtain. In most cases, we can only get its numerical solutions. In this paper, a Nash differential game problem is converted to the corresponding differential variational inequality problem, and then converted into optimal control problem via D-gap function. The nonlinear continuous optimal control problem is obtained, which is easy to get numerical solutions. Compared with other conversion methods, the specific solving process of this method is more simple, so it has certain validity and feasibility.

Abstract: The objective of this paper is to design a linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controllers for an active anti-roll bar system. The use of an active anti-roll bar will be analysed from two different perspectives in vehicle ride comfort and handling performances. This paper proposed the basic vehicle dynamic modelling with four degree of freedom (DOF) on half car model and are described that show, why and how it is possible to control the handling and ride comfort of the car, with the external forces also control strategies on the front anti-roll bar. By simulation analysis, the design model is validity and the performance under control of linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controller are achieved. Both two controllers are modeled in MATLAB/SIMULINK environment. It has to be determined which control strategy delivers better performance with respect to roll angle and the roll rate of half vehicle body. The result shows, however, that LQG produced better response compared to a LQR strategy.

Abstract: There has been wide interest in the control scheme of the electromagnetic levitation system due to its disadvantages of nonlinearity and open-loop uncertainty. A typical coil-ball levitation system is used in research. The forces of the ball are analyzed and a dynamic model of the whole electromagnetic levitation system is established. Based on the nonlinear state-space model, the coil-ball system is linearized and then a LQR control approach is proposed. Simulation results show that, compared with conventional pole assignment scheme, the electromagnetic levitation system under the proposed control approach gets a better performance, including smaller overshot and faster response.

Abstract: As the key equipment in coal-fired power plants, the optimal control problem of ball mill is an important factor affecting the efficient operation of the mill. In this paper, the output model, energy consumption model and unit consumption model of coal pulverizing system are built on the basis of analysis of ball mill running condition. The unit consumption model is used as the genetic algorithm fitness function. The outlet temperature, practical ventilation and coal load of ball mill are chosen as the optimization variables. The algorithm model is built to solve the multi-objective problem of ball mill.

Abstract: The present research work proposes a method of optimal control for a class of nonlinear singular system, which applies single-step design, Zubov’s method and optimal control theory. Firstly, it makes the nonlinear singular system normalizable, realizes feedback linearization and the pole placement in a single step. Then, it constructs a performance index that is calculated explicitly as an algebraic function of the controller parameters by solving Zubov’s partial differential equation. Lastly, standard optimization techniques are employed for the calculation of the optimal values of the adjustable parameters. So we obtain the single-step optimal design of normalizability , feedback linearization with the pole placement for nonlinear singular system. The proposed approach is finally applied in a SIS model with logistic growth. The simulation result shows the feasibility and the effectiveness of the proposed method.