For the ARMAX system with unknown coefficients the optimal adaptive control isdesigned so that the following requirements are met simultaneously:1)the transfer function from areference signal to the system output in t...For the ARMAX system with unknown coefficients the optimal adaptive control isdesigned so that the following requirements are met simultaneously:1)the transfer function from areference signal to the system output in the closed loop equals a prescribed rational function;2)under the constraint mentioned in 1)a quadratic loss function is minimized;3)the parameterestimate is strongly consistent.展开更多
A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which i...A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.展开更多
文摘For the ARMAX system with unknown coefficients the optimal adaptive control isdesigned so that the following requirements are met simultaneously:1)the transfer function from areference signal to the system output in the closed loop equals a prescribed rational function;2)under the constraint mentioned in 1)a quadratic loss function is minimized;3)the parameterestimate is strongly consistent.
文摘A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.
