Robust Optimal Control Scheme Research Articles

Adaptive critic design-based robust neural network control for nonlinear distributed parameter systems with unknown dynamics

Abstract In this paper, an adaptive critic design (ACD)-based robust on-line neural network control design is developed for a class of parabolic partial differential equation (PDE) systems with unknown nonlinear dynamics. First, the Galerkin method is applied to the parabolic PDE system to derive a finite-dimensional slow one and an infinite-dimensional stable fast subsystem. The obtained slow system is an ordinary differential equation (ODE) system with unknown nonlinearities, which accurately describes the dynamics of the slow modes of the PDE system. Then, a novel ACD-based robust optimal control scheme is proposed for the resulting nonlinear slow system with unknown dynamics. An action neural network (NN) is employed to approximate all the derived unknown nonlinear terms and a robust control term is further developed to attenuate the NN reconstruction errors and disturbances. Especially, by developing novel critic signals and Lyapunov function candidate, together with the adaptive bounding technique, no a prior knowledge for the bounds of the disturbance term, the NN ideal weights of action NN and critic NN and the NN reconstruction errors is required. Finally, simulation results demonstrate the effectiveness of the proposed robust optimal control scheme.

A Novel Robust Optimal Active Control of Vehicle Suspension Systems

Using Lyapunov theory, Pontryagin's minimum principle, and affine quadratic stability, a novel robust optimal control strategy is developed for active suspension systems to enhance vehicle ride comfort and handling performance. The controller has a simple structure, making its suitable for real-time implementation. The required sensor configuration includes a six-axis IMU and four LVDTs. The proposed controller is suitable for on-road commercial vehicles where ride comfort over bump disturbances and handling performance are the most concerns. The effectiveness of the controller is verified through simulation results using IPG CarMaker software.

Finite-Time Anti-Disturbance Inverse Optimal Attitude Tracking Control of Flexible Spacecraft

We propose a new robust optimal control strategy for flexible spacecraft attitude tracking maneuvers in the presence of external disturbances. An inverse optimal control law is designed based on a Sontag-type formula and a control Lyapunov function. An adapted extended state observer is used to compensate for the total disturbances. The proposed controller can be expressed as the sum of an inverse optimal control and an adapted extended state observer. It is shown that the developed controller can minimize a cost functional and ensure the finite-time stability of a closed-loop system without solving the associated Hamilton-Jacobi-Bellman equation directly. For an adapted extended state observer, the finite-time convergence of estimation error dynamics is proven using a strict Lyapunov function. An example of multiaxial attitude tracking maneuvers is presented and simulation results are included to show the performance of the developed controller.

Model based robust control approach for batch crystallization product design

The paper presents a novel control approach for crystallization processes, which can be used for designing the shape of the crystal size distribution to robustly achieve desired product properties. The approach is based on a robust optimal control scheme, which takes parametric uncertainties into account to provide decreased batch-to-batch variability of the shape of the crystal size distribution. Both open-loop and closed-loop robust control schemes are evaluated. The open-loop approach is based on a robust end-point nonlinear model predictive control (NMPC) scheme which is implemented in a hierarchical structure. On the lower level a supersaturation control approach is used that drives the system in the phase diagram according to a concentration versus temperature trajectory. On the higher level a robust model-based optimization algorithm adapts the setpoint of the supersaturation controller to counteract the effects of changing operating conditions. The process is modelled using the population balance equation (PBE), which is solved using a novel efficient approach that combines the quadrature method of moment (QMOM) and method of characteristics (MOC). The proposed robust model based control approach is corroborated for the case of various desired shapes of the target distribution.