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The novelty of our work is in the bounds on the controls, and the facts that (i) one does not need to compute any Lie derivatives to apply our controls, (ii) the controls have no distributed terms, and (iii) we do not require any differentiability conditions on the available controls for the subsystems.
This paper describes the computation of reach sets for discrete-time linear control systems with time-varying coefficients and ellipsoidal bounds on the controls and initial conditions.
This paper presents a scheme of designing strategies of nonlinear target control synthesis under joint quadratic and hard bounds on the controls and hard bounds on the disturbances.
The "reach" sets are presented in terms of level sets to solutions of appropriate types of Hamilton-Jacobi-Bellman equation, which depend on the presence or absence of additional hard bounds on the controls.
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It has been shown that if the objective functional is linear in the control and the only constraints are the upper and lower bounds on the control, then the problem will sometimes have bang bang (on off) optimal solutions.
· Bounds on the control variable 0 ≤ u (t ) ≤ 1.
Mathematically this can be expressed as to find: (34) min u J ; with J = 1 n ξ ∑ i = 1 n ξ v i (t f ) − v Ti 2 Subject to: · The reduced order model dynamics (29 - 31 29 - 31ds on the control variables, − 1 ≤ u k (t ) ≤ 1. Similarly to the previous case, a multistart approach of the FSQP method was selected to study the possible multimodal nature of the problem.
The control laws are bounded, taking into consideration the amplitude bounds of the control angles characterizing the flapping wings movement.
Positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered.
Among the pending problems of control synthesis under set-membership uncertainty lies the case when the bounds on the disturbances and the controls are generated by different types of inequalities.
The class models optimal control and engineering design problems with bounds on the state and control variables and has wide applicability.
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