In this paper, a methodology to design controllers able to cope with different load conditions on an Ethernet network is introduced. Load conditions induce time-varying delays between measurements and control. To face these variations an interpolated, delay-dependent gain scheduling law is used. The lack of synchronization is solved by adopting an event-based control approach. The dual-rate control action computation is carried out at a remote controller, whereas control actions and measurements are taken out locally at the controlled process site. Stability is proved in terms of probabilistic linear matrix inequalities. TrueTime simulations in an Ethernet case show the benefit of the proposal, which is later validated on an experimental test-bed Ethernet environment.

In many industrial control applications the control action updating can be faster than the output measurement, leading to multirate controllers. In this paper, some dual rate operations are used to model the controller as well as the controlled plant. The controller design is model-based and depends on the input to be tracked. The controller is split into two parts acting at different sampling rates and its design is approached based on the characteristics of each available sampling rate. The control target is to reach the similar performances to those the faster single rate controller would achieve. Model-based cancellation controllers are designed using this approach and promising results are obtained.

In this contribution, we suggest two proposals to achieve fast, real-time lane-keeping control for Autonomous Ground Vehicles (AGVs). The goal of lane-keeping is to orient and keep the vehicle within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While nonlinear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some control laws such as Model Predictive Control (MPC) in real time. Therefore, our first proposal is to use a Linear Parameter Varying (LPV) model to describe the AGV’s lateral dynamics, as a trade-off between computational complexity and model accuracy. Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KFs) are usually needed for sensor fusion. Our second proposal is to use a Dual-Rate Extended Kalman Filter (DREFKF) to alleviate the cost of updating the internal state of the filter. To check the validity of our proposals, an LPV model-based control strategy is compared in simulations over a circuit path to another reduced computational complexity control strategy, the Inverse Kinematic Bicycle model (IKIBI), in the presence of process and measurement Gaussian noise. The LPV-MPC controller is shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it is seen that Dual-Rate Extended Kalman Filters (DREKFs) constitute an interesting tool for providing fast vehicle state estimation in an AGV lane-keeping application.