Precise Position Adjustment of Automotive Electro-Hydraulic Coupling System with Parameter Perturbations

Based on the guaranteed cost theory, this paper proposes a robust controller for the automotive electro-hydraulic coupling system. However, parameter perturbation caused by the model linearization is a critical challenge for the nonlinear electro-hydraulic coupling system. Generally, the electrical brake booster system (E-Booster) can be separated into three parts, a permanent magnet synchronous motor (PMSM), a hydraulic model of the master cylinder, and the transmission mechanism. In this paper, the robust guaranteed cost controller (RGCC) could adjust accurately the pushrod position of the E-Booster and has strong robustness against internal uncertainties, and the linear extended state observer (LESO) was utilized to optimize E-Booster's dynamic performance. Thus, the tracking differentiator (TD) and LESO are used to improve the dynamic precision and reduce the hysteresis effect. The overshoot is suppressed by TD, and the disturbance caused by nonlinear uncertainty is restrained by LESO. Experiment results show that RGCC sacrifices 6% phase lag in the low-frequency domain for a 10% and 40% reduction in first and second-order respectively compared with the proportion integration differentiation (PID). Results demonstrate that RGCC has higher precision and stronger robustness in dynamic behaviour.

Algebraic Driver Steering Model Parameter Identification

Modeling driver steering behavior plays an ever-important role in nowadays automotive dynamics and control applications. Especially, understanding individuals' steering characteristics enables the advanced driver assistance systems (ADAS) to adapt to particular drivers, which provides enhanced protection while mitigating human-machine conflict. Driver-adaptive ADAS requires identifying the parameters inside a driver steering model in real-time to account for driving characteristics variations caused by weather, lighting, road, or driver physiological conditions. Usually, Recursive Least Squares (RLS) and Kalman Filter (KF) are employed to update the driver steering model parameters online. However, because of their asymptotical nature, the convergence speed of the identified parameters could be slow. In contrast, this paper adopts a purely algebraic perspective to identify parameters of a driver steering model, which can achieve parameter identification within a short period. To demonstrate the effectiveness of the proposed method, we first apply synthetic driver steering data from simulation to show its superior performance over an RLS identifier in identifying constant model parameters, including feedback steering gain, feedforward steering gain, preview time, and first-order neuromuscular lag. Then, we utilize real measurement data from human subject driving simulator experiments to illustrate how the time-varying feedback and feedforward steering gains can be updated online via the algebraic method.

Hybrid Model Predictive Control of Floating Offshore Wind Turbines with Artificial Muscle Actuated Mooring Lines

Floating offshore wind turbines (FOWTs) are subject to undesirable platform motion and significant increase in fatigue loads compared to their onshore counterparts. We have recently proposed using the Fishing Line Artificial Muscle (FLAM) actuators to realize active mooring line force control (AMLFC) for platform stabilization and thus load reduction, which features compact design and no need for turbine redesign. However, as for the thermally activated FLAM actuators, a major control challenge lies in the asymmetric dynamics for the heating and the cooling half cycle of operation. In this paper, for a tension-leg platform (TLP) based FOWT with FLAM actuator based AMLFC, a hybrid dynamic model is obtained with platform pitch and roll degrees of freedom included. Then a hybrid model predictive control (HMPC) strategy is proposed for platform motion stabilization, with preview information on incoming wind and wave. A move blocking scheme is used to achieve reasonable computational efficiency. FAST based simulation study is performed using the NREL 5 MW wind turbine model. Under different combinations of wind speed, wave height and wind directions, simulation results show that the proposed control strategy can significantly reduce the platform roll and tower-base side-to-side bending moment, with mild level of actuator power consumption.

Special Issue: Optimal Energy Management and Control in Connected and Automated Vehicles (CAVs)

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Sliding Mode Analysis of a Counterbalance Valve Induced Instability in an Electrohydraulic Drive

This paper analyzes dynamic effects of an electro-hydraulic drive which uses a counter-balance valve for rod volume compensation. It shows that local stability analysis is not sufficient in this particular case to get general statements of the system's chattering properties. A reduced-order switched system is proposed to gain deeper insights in system dynamics with saturation effects such as the end-stop of a valve poppet and solutions are compared numerically to the full-system dynamics which incorporates pressure built-up, piston and valve dynamics as well as motor dynamics. It is shown that in cases of e.g. fast valves with small cracking pressures undesirable chattering of the full system exists which can be easily understood in terms of the reduced-order system in form of sliding mode solutions. The paper also describes under which conditions such sliding modes exist, how they behave and how they can be interpreted in terms of the full system.

Time-Varying Formation Control for Heterogeneous Planar Underactuated Multi-Vehicle Systems

This paper presents a distributed control approach for time-varying formation of heterogeneous planar underactuated vehicle networks without global position measurements. All vehicles in the network are modeled as generic three degree of freedom planar rigid bodies with two control inputs, and are allowed to have non-identical dynamics. Feasible trajectories are generated for each vehicle using the nonholonomic constraints of the vehicle dynamics. By exploiting the cascaded structure of the planar vehicle model, a transformation is introduced to define the reduced order error dynamics, and then, a sliding-mode control law is proposed. Low level controller for each vehicle is derived such that it only requires relative position and local motion information of its neighbors in a given directed communication network. The proposed formation control law guarantees the uniform global asymptotic stability (UGAS) of the closed-loop system subject to bounded uncertainties and disturbances. The proposed approach can be applied to underactuated vehicle networks consisting of mobile robots, surface vessels and planar aircraft. Simulations are presented to demonstrate the effectiveness of the proposed control scheme.

Momentum Preserving Simulation of Cooperative Multi-Rotors with Flexible-Cable Suspended Payload

In this paper, a momentum-preserving integration scheme is implemented for the simulation of single and cooperative multi-rotors with a flexible-cable suspended payload by employing a Lie group based variational integrator (VI), which provides the preservation of the configuration manifold and geometrical constraints. Due to the desired properties of the implemented VI method, e.g. sypmlecticity, momentum preservation, and the exact fulfillment of the constraints, exponentially long-term numerical stability and good energy behavior are obtained for more accurate simulations of aforementioned systems. The effectiveness of Lie group VI method with the corresponding discrete systems are demonstrated by comparing the simulation results of two example scenarios for the single and cooperative systems in terms of the preserved quantities and constraints, where a conventional fixed-step Runge-Kutta 4 (RK4) and Variable-Step integrators are utilized for the simulation of continuous-time models. It is shown that the implemented VI method successfully performs the simulations with a long-time stable behavior by preserving invariants of the system and the geometrical constraints, whereas the simulation of continuous-time models by RK4 and Variable Step are incapable of satisfying these desired properties, which inherently results in divergent and unstable behavior in simulations.

Switched Fractional Order Model Reference Adaptive Control for First Order Plants: a Simulation-Based Study

This paper presents the results and analysis of an exhaustive simulation study where Switched Fractional Order Model Reference Adaptive Control (SFOMRAC) is used for first order plants, along with the analytical proof of boundedness and convergence of the scheme. The analysis is focused on the controlled system behavior through the integral of the timed squared control error (ITSE) and on the control energy though the integral of the squared control signal (ISI). Controller parameters such as fractional order, adaptive gain and switching time are varied along the simulation studies, as well as plant parameters and reference models. The results show that SFOMRAC controllers can be found for every plant and reference model used, such that both system behavior and control energy can be improved, compared to equivalent non switched fractional order and integer order control strategies.

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

The Cavitation Issue in Asymmetrical Axial-Piston Pumps

Pump-controlled single-rod hydraulic actuators have long been the subject of intensive research towards building valve-less, more efficient systems. The main challenge is to deal with the uneven flows into and out of the differential cylinders. Over the past few years, several hydraulic circuits providing flow compensation have been proposed using hydrostatic pumps with identical input and output flows. However, one alternative solution would be to use a pump, whose input/output flow ratio matches the area ratio of the differential cylinder. Typical design and prototyping of the so-called asymmetrical pumps have been well reported previously. In this paper, we theoretically study the flow behaviour in a common design of asymmetrical axial-piston pumps and demonstrate some serious internal flow characteristics that can drastically limit the performance and range of operation of these pumps. Cavitation is the main problem to be addressed, and cannot be overlooked because of the very nature of the pump design.