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Axial piston motors are core components in many hydro-mechanical systems as e.g. the powertrain of wheel loader vehicles. The optimal performance of the motor as well as the powertrain requires a fast and exact control of the motor’s swivel angle according to the a-priori unknown reference signal which is e.g. generated by the driver with a joystick or gas pedal operation. Therefore, it is essential that the swivel angle controller fully exploits the possible dynamics and the constrained resources of the nonlinear swivel angle adjustment system. In this work, a novel flatness-based feedforward control strategy for the swivel angle is presented, which is capable of considering the relevant state and input constraints in real-time, i.e. only by means of the currently available information and without relying on an online optimization. The proposed swivel angle controller is finally demonstrated and discussed with the results of both a simulation study and measurement data from a wheel loader vehicle.

The real-time generation of kinematic trajectories w.r.t. state and input constraints is discussed for the stabilization of the lateral, longitudinal and vertical motion of objects such as (automated) vehicles in the Frenet frame. The goal is to exploit the property of differential flatness in order to achieve optimal trajectories as well as a flexible and selectable prioritization-based constraints satisfaction. This aim is subject to the absence of an online optimization and a low complexity of the planner’s architecture. With regard to practical application, a discrete-time implementation of the planner is presented and benchmarked with two modern MPC approaches in terms of optimality and CPU-time consumption by means of simulation studies.

Axial piston motors and pumps are core components in many hydromechanical systems as for instance the powertrain of wheel loader vehicles. The optimal performance of the individual components as well as the entire powertrain requires a fast and exact control of both the motor’s swivel angle as well as the pressure difference over the pump by means of the currently available information and despite their respective a-priori unknown reference signal generated by the driver with a joystick or gas pedal operation. Therefore, it is essential that the swivel angle and pressure controllers fully exploit the possible dynamics and the constrained resources of the nonlinear swivel angle displacement and the pressure adjustment system, respectively. In this work, a flatness-based feedforward control strategy for the motor’s swivel angle as well as a flatness-based tracking two-degree-of-freedom controller for the pump pressure are proposed. For the realization of the flat feedforward and the tracking controller, a constrained online trajectory generation by means of a nonlinear (switched) state variable filter is designed for each. Therefore, both controllers are capable of considering the relevant state and input constraints in real-time and without relying on an online optimization. The proposed control algorithms are both discussed and their performance is illustrated with measurement data from a wheel loader vehicle.

The problem of constrained Online Trajectory Planning (OTP) for Flatness-based Feedforward Controllers (FFC) is discussed for online tracking problems with a-priori unknown reference signals for the system output. As plant, nonlinear minimum phase Single Input Single Output (SISO) systems with input constraints as well as polytopic constraints on the flat state are assumed. The goal is to achieve both, fast reference tracking and a prioritization-based constraint satisfaction despite avoiding an online optimization. Therefore, the OTP is set up by means of a switched State Variable Filter (SSVF). The switching results from the limitation of the filter’s highest derivative in accordance to the above requirements and is derived with a piecewise solution of an underlying optimization problem. The paper shows the derivation of the SSVF, discusses necessary stability conditions of the proposed concept, and finally demonstrates the SSVF’s performance by means of a simulation study. A comparison to a respective optimal control problem solution and model predictive controller (MPC) is furthermore provided.

The problem of stabilization by means of state feedback is discussed for linear single input single output systems while satisfying time-varying polytopic constraints on both the states and the control input. The core of the switched control concept derived in this work is a standard state feedback controller, stabilizing the system in the unconstrained case, whereas its control signal is optimally limited in order to achieve feasibility of the states and the input in the constrained case. For the derivation of the limiting function, feasible dynamic limits are designed which correspond to one additional control law per constraint. A switching logic for these limits is then constructed with the solution of an optimization problem. Thereby, an explicit order of constraints prioritization is taken into account in order to obtain a unique switching logic. Throughout the derivation, the property of differential flatness is exploited, which allows the reduction of the respective system to the control of a (constrained) integrator chain. The performance of the control approach as pure feedback as well as in a constrained two-degree-offreedom setup is finally discussed by means of simulation studies. Furthermore, a comparison of the achieved results with a model predictive control approach is carried out.