Search results
Results From The WOW.Com Content Network
For example, for the four possible combinations of two driver cell contents, state 7 leads to states 5, 4, 1 and 0 and therefore , , , and are each 1 ⁄ 4 or 25%. Similarly, state 0 leads to states 0, 1, 0 and 1 and therefore and are each 1 ⁄ 2 or 50%. And so forth.
The asymptotic availability, i.e. availability over a long period, of the system is equal to the probability that the model is in state 1 or state 2. This is calculated by making a set of linear equations of the state transition and solving the linear system. The matrix is constructed with a row for each state.
Complete state controllability (or simply controllability if no other context is given) describes the ability of an external input (the vector of control variables) to move the internal state of a system from any initial state to any final state in a finite time interval. [1]: 737
State based control can be thought of as an extension of the unit operation concept in the process industry. [7] Processes are designed with unit operations. With state-based control the control design is divided into units and those units further divided into states, with the proper operating discipline for each state designed in.
In control theory, a state observer, state estimator, or Luenberger observer is a system that provides an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and provides the basis of many practical applications.
If the system is controllable, there is always an input u(t) such that any state x 0 can be transferred to any other state x(t). With that in mind, a feedback loop can be added to the system with the control input u ( t ) = r ( t ) − kx ( t ) , such that the new dynamics of the system will be
where y is an n × 1 vector of observable state variables, u is a k × 1 vector of control variables, A t is the time t realization of the stochastic n × n state transition matrix, B t is the time t realization of the stochastic n × k matrix of control multipliers, and Q (n × n) and R (k × k) are known symmetric positive definite cost matrices.
A discrete MPC scheme. MPC is based on iterative, finite-horizon optimization of a plant model. At time the current plant state is sampled and a cost minimizing control strategy is computed (via a numerical minimization algorithm) for a relatively short time horizon in the future: [, +].