In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed.
In this way, a better optimal solution can be obtained with a smaller possibility degree of the constraint, However, a smaller possibility degree implies a smaller reliability of the uncertain constraint.
In general, if it can be assumed that the estimation error in the link capacities is such that the resulting uncertain constraint set in (112) can be a subset of a given uncertainty set, then it can be shown that by adopting the robust convex optimization theory [24, 25], an optimal solution can still be found.
It is easy to see that, if one swaps the role of the objective function with that of the uncertain constraint, (RKP) immediately fits within these settings, which allows to adopt the scheme above.
Compared with CCP, the TSP can hardly account for the violating risk of uncertain system constraints.
Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties.
This paper presents an integrated robust fault detection and fault-tolerant control architecture for transport-reaction processes modeled by quasi-linear parabolic partial differential equations with uncertain variables, control constraints and control actuator faults.
This paper presents a methodology for the design of an integrated robust fault detection and isolation (FDI) and fault-tolerant control (FTC) architecture for distributed parameter systems modeled by nonlinear parabolic Partial Differential Equations (PDEs) with time-varying uncertain variables, actuator constraints and faults.
Figure 1 illustrates the difference between a deterministic and probabilistic interpretation of an uncertain upper-bound constraint on the flux of reaction j.
