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A path planning algorithm for an Autonomous Underwater Vehicle (AUV) performing docking operations in the presence of tidal current disturbances is presented. The path planner is composed a way-point generator (WPG), an Model Predictive Control based optimal planner (OP), and a tidal current estimator (TCE). The WPG defines the next way-point with respect to the vehicle position and the OP iteratively generates the trajectory over a prescribed future horizon whilst satisfying certain physical and logical constraints. The TCE estimates the tidal current disturbance speed enabling to adapt the AUV trajectory for reducing actuators power consumption. The proposed path planning policy has been evaluated in a set of simulation scenarios to illustrate the ability of the policy to drive the AUV by satisfying specifications and limiting power consumption.

A new polynomial systems method is defined for the restricted structure nonlinear predictive controllers design. The control algorithm is established for a discrete-time nonlinear system, described in the polynomial matrix linear parameter varying form. The low-order restricted structure controller is parameterized in terms of discrete transfer operators set that the designer chooses multiplied by a set of optimized gains. The controller within the feedback loop can have a general linear parameter varying form or something as simple as a PI structure. The predictive control multi-step cost-index, which is minimized, comprises weighted error, control signal costing, and gain magnitude terms. A simulation of an automotive example is used to evaluate the nonlinear restricted structure controller performance.

The use of Hybrid Predictive Control for model-based propulsion control applications is considered. A Variable Displacement Engine (VDE) control problem is considered, involving both continuous-time dynamics and discrete control actions in the form of activating/deactivating the engine cylinders. Hybrid Model Predictive Control is one of the most successful hybrid control schemes and builds upon predictive control methods developed for engine torque management. The ways in which preview information can be used to improve controller performance are considered, as well as simplifications to the hybrid control algorithms to reduce the computational burden. Several hybrid control design approaches are compared using a simulation of a VDE engine. The aim is to optimize the total system behaviour to provide good torque tracking, reduced fuel consumption and smooth cylinder switching. The main contribution is the demonstration that hybrid predictive control can provide a practical solution to an engine control application with the potential to enhance performance and with options to reduce complexity.

The control of an autonomous host vehicle at a crossroads intersection, in the presence of uncoordinated target vehicles, and without any crossing priority regulation is considered. The problem was spilt into two sub-problems, namely the priority and the path-planning problems. These problems are solved using a hierarchical controller. The lower control level is a linear controller to control the vehicle's speed and heading, to follow the reference signals provided by a middle-level algorithm. This middle-level controller is a Model Predictive Control (MPC) computed by modelling a unicycle model that is in a Linear Parameter-Varying (LPV) state-space model form. Different features are introduced for improving the prediction capability of the LPV-MPC. Prediction data computed by the MPC are used by the higher-level state-machine supervisor algorithm to determine when the host vehicle can safely cross the junction. The hierarchical controller was tested in simulation using a set of stressing scenarios. Reported results show the effectiveness of the proposed LPV-MPC in managing complex traffic scenarios with efficient compute.