Abstract Model-error control synthesis is a nonlinear robust control approach that mitigates the effects of modeling errors and disturbances on a system by providing corrections to the nominal control input directly. In this paper model-error control synthesis is applied to the spacecraft attitude control problem, where the model-error vector is computed using a receding-horizon approximation. ...

A methodology is proposed to generate minimum-time optimal velocity profiles for a vehicle with prescribed acceleration limits along a specified path. The necessary optimality conditions are explicitly derived, allowing the construction of the optimal solution semi-analytically. A receding horizon implementation is also proposed for the on-line implementation of the velocity optimizer. Robustne...

In some situations, when an external disturbance occurs, humans can rock stably backward and forward by lifting the toe or the heel to keep the upright balance without stepping. Many control schemes have been proposed for standing balance control under external disturbances without stepping. But, in most of them researchers have only considered a flat foot phase. In this paper a framework is pr...

We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path integral form of the optimal control. The resultin...

This paper deals with the application of receding horizon methods to hover and forward flight models of an experimental tethered flying wing developed at Caltech. The dynamics of the system are representative of a vertical landing and take off aircraft, such as a Harrier around hover, or a thrust-vectored aircraft such as F18-HARV or X-31 in forward flight. The adopted control methodology is a ...

Model-Error Control Synthesis employs an optimal real-time nonlinear estimator to determine model error corrections to a nominal controller. Control compensation is achieved by using the estimated model error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. In this pape...

In this paper we develop and illustrate methods for estimating the degree of suboptimality of receding horizon schemes with respect to infinite horizon optimal control. The proposed a posteriori and a priori methods yield estimates which are evaluated online along the computed closed–loop trajectories and only use numerical information which is readily available in the scheme.

We propose a receding horizon control strategy that readily handles systems that exhibit interval-wise total energy constraints on the input control sequence. The approach is based on a variable optimization horizon length and contractive final state constraint sets. The optimization horizon, which recedes by N steps every N steps, is the key to accommodate the interval-wise total energy constr...

We present a receding horizon control scheme that utilizes a discrete time version of a nonlinear, iterative, projection-based optimization routine. The routine utilizes a projection operator to ensure feasible system trajectories at each iteration of the optimization resulting in valid control signals even when constraints on computation time prevent full convergence. An additional feature of ...