Vehicle Lateral Motion Control Based on Estimated Stability Regions

This paper presents a novel vehicle lateral stability control method based on an estimated lateral stability region on the phase plane of vehicle yaw rate and lateral speed, which is obtained through a local linearization method. Since the estimated stability region does not only describe vehicle local stability, but also define the oversteering and understeering characteristics, the proposed control method can achieve both local stability and vehicle handling stability. Considering the irregular geometric shape of the estimated stability region, a stability analysis algorithm is designed to determine the distance between vehicle states and stability region boundaries. State estimation or measurement errors are also incorporated in the distance calculation. Based on the calculated shortest distance between vehicle states and stability boundaries, a direct yaw moment controller is designed to maintain vehicle states stay within the stability region. CarSim® and Simulink® co-simulation is applied to verify the control design through a cornering maneuver. The simulation results show that the proposed control method can make the vehicle stay within the stability region successfully and thus always operate in a safe manner.

Battery Capacity Estimation From Partial-Charging Data Using Gaussian Process Regression

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a novel diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which is capable of estimating the battery capacity using voltage vs. time measurements over short periods of galvanostatic operation. The approach uses Gaussian process regression to map from voltage values at a selection of uniformly distributed times, to cell capacity. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data through the lens of Incremental Capacity (IC) or Differential Voltage (DV) analysis. This overcomes both the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. Rather, GP-ICE gives insight into which portions of the voltage range are most informative about the capacity for a particular cell. We apply GP-ICE to a dataset of 8 cells, which were aged by repeated application of an ARTEMIS urban drive cycle. Within certain voltage ranges, as little as 10 seconds of charge data is sufficient to enable capacity estimates with ∼ 2% RMSE.

Vehicle Rollover Prevention Through a Novel Active Rollover Preventer

To assist vehicle rollover prevention and enhance vehicle roll motion safety, this paper proposes a novel active rollover preventer (ARPer) system, which consists of an in-wheel motor system moving along an orbit at the back of a vehicle. The roll and lateral dynamics of the vehicle equipped with the ARPer are modeled and mechanics analysis of the ARPer is presented as well. Based on the developed models, a Lyapunov nonlinear controller is designed for tracking a desired roll angle and a yaw rate when the impending rollover is detected. For a typical fishhook maneuver, two simulation cases are studied for different vehicle roof cargo loads, which represents different vehicle rollover properties without control. The CarSim®-Simulink co-simulation results show that compared with active front steering and differential braking control strategies, the APRer can successfully prevent the rollover propensity and maintain the vehicle lateral stability simultaneously.

A Receding Horizon Switching Control Resilient to Communication Failures for Connected Vehicles

This paper focuses on networked control systems subject to network-induced constraints, namely transmission delays and packet dropping. The proposed framework is based on a switching control logic which selects the optimal control action in a finite set of strategies tailored to a specific scenario. The switching logic relies on a receding horizon optimization — which resembles model predictive control — and does not require any prior knowledge on the condition of the network. This strategy is tested on a platoon of connected vehicles engaged in cooperative adaptive cruise control which communicate over an imperfect DSRC network. The main objective consists in avoiding unsafe scenarios where the network is subject to the aforementioned failures; results show the proposed approach achieves the objective whereas a nominal controller would lead the platoon to crash.

Air-Fuel Ratio Control of Lean-Burn SI Engines Using Fuzzy Sliding-Model Technique

Minimization of the carbon dioxide and harmful pollutants emissions and maximization of fuel economy for the lean-burn spark ignition (SI) engines significantly rely on precise air-fuel ratio (AFR) control. However, the main challenge of AFR control is the large time-varying delay which exists in lean-burn engines. Since the system is usually subject to external disturbances and uncertainties, a high level of robustness in the AFR control design has to be considered. Herein, a fuzzy sliding-mode control (FSMC) technique is proposed to track the desired AFR in the presence of periodic disturbances. The proposed method is model free and does not need any system characteristics. Based on the fuzzy system input-output data, two scaling factors are first employed to normalize the sliding surface and its derivative. According to the concept of the if-then rule, an appropriate rule table for the logic system is designed. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated under various operating conditions.

Online Hybrid Model Predictive Controller Design for Cruise Control of Automobiles

In the proposed study, a Hybrid Model Predictive Controller is introduced for cruise control of an automobile model. The presented model consists of the engine, the gearbox, and the transmission dynamics, where the aerodynamics force and elastic friction between the tires and road are taken into account. Through Piecewise Linearization of nonlinearities in the system; (torque)-(throttle)-(angular velocity) of engine and (aerodynamic drag force)-(automobile velocity), a comprehensive piecewise linear model for the system is obtained. Then combined with the switch and shift between engaged gears in gearbox, the Piecewise Affine (PWA) model for the vehicle dynamics is acquired. As far as the control design is concerned, the cruise control problem for tracking a desired speed fashion is addressed by a MPC-based controller design. The proposed control approach is based on the online model predictive control, applied on the obtained PWA dynamics. The highlighted novelties of the presented research work are summarized as: first a more complete model is examined due to the consideration of a realistic model for engine. This improvement makes the polyhedron regions of the PWA system dependent to both state variable (i.e., velocity) and input signals (i.e., throttle and engaged gear) which brings the complexity to the design of control procedure. Second, due to the switch in the dynamics and dependence of our PWA model to discrete input (gear shift), the desperate need to solve the optimization problem through mixed integer programming, which needs high computation effort specially for our system, seems inevitable. We triumph over this challenge through introducing “possible gear shift scenario” sets. Hence, by constraining the optimization problem to the introduced logical sets, the problem still remains convex optimization type and the computation volume is reduced. In addition, we hired branch and bound method which allowed us to have large problems to be solved in a tractable amount of time and computation resources. At last, some simulations are presented to exhibit the performance of the proposed method.

Using a Redundant Planar Hip Exoskeleton to Reduce Human-Device Interface Forces

Robotic exoskeletons have the potential to improve gait rehabilitation. Currently, most exoskeletons use revolute joints that must be exactly aligned with the user’s joints to prevent uncomfortable shear forces at the human-device interface. This paper presents an alternative design for a planar hip exoskeleton based on a planar Stewart platform. In theory, this mechanism does not require exact knowledge of the human hip joint center of rotation to prevent large shear forces. The total human-device system has four degrees of freedom if the human soft tissue is neglected, which does complicate the control of the system compared to a rotational exoskeleton. To find a mapping between the desired human hip angle and the four actuated joints, the task priority method is used. To determine how well the proposed device can guide the hip through a step, dynamic simulations were conducted and compared to the results for a rotational exoskeleton. The compliance in the human soft tissue was included in the simulations because it can play a significant role in both the motion of the system and the human-device forces. Both the ideal case of exact hip joint alignment and the more likely case of hip joint misalignment were considered. In addition, the effects of differing levels of human effort were compared. In all cases, both exoskeletons were well able to guide the human hip in the desired motion. In addition, the novel exoskeleton has significantly lower shear forces at the thigh human-device connection point.

Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments

Path planning for autonomous vehicles in arbitrary environments requires a guarantee of safety, but this can be impractical to ensure in real-time when the vehicle is described with a high-fidelity model. To address this problem, this paper develops a method to perform trajectory design by considering a low-fidelity model that accounts for model mismatch. The presented method begins by computing a conservative Forward Reachable Set (FRS) of a high-fidelity model’s trajectories produced when tracking trajectories of a low-fidelity model over a finite time horizon. At runtime, the vehicle intersects this FRS with obstacles in the environment to eliminate trajectories that can lead to a collision, then selects an optimal plan from the remaining safe set. By bounding the time for this set intersection and subsequent path selection, this paper proves a lower bound for the FRS time horizon and sensing horizon to guarantee safety. This method is demonstrated in simulation using a kinematic Dubin’s car as the low-fidelity model and a dynamic unicycle as the high-fidelity model.

Experimental and Novel Analytical Trajectory Optimization of a 7-DOF Baxter Robot: Global Design Sensitivity and Step Size Analyses

In this paper, we present a novel nonlinear analytical coupled trajectory optimization of a 7-DOF Baxter manipulator validated through experimental work utilizing global optimization tools. The robotic manipulators used in network-based applications of industrial units and even homes, for disabled patients, spend significant lumped amount of energy and therefore, optimal trajectories need to be generated to address efficiency issues. We here examine both heuristic (Genetics) and gradient based (GlobalSearch) algorithms for a novel approach of “S-Shaped” trajectory (unlike conventional polynomials), to avoid being trapped in several possible local minima along with yielding minimal computational cost, enforcing operational time and torque saturation constraints. The global schemes are utilized in minimizing the lumped amount of energy consumed in a nominal path given in the collision-free joint space except an impact between the robot’s end effector and a target object for the nominal operation. Note that such robots are typically operated for thousands of cycles resulting in a considerable cost of operation. Due to the expected computational cost of such global optimization algorithms, step size analysis is carried out to minimize both the computational cost (iteration) and possibly cost function by finding an optimal step size. Global design sensitivity analysis is also performed to examine the effects of changes of optimization variables on the cost function defined.

Closed-Loop Control of the Frequency Response of the Virtual Brain Model

The brain is a highly complex network and analyzing brain connectivity is a nontrivial task. Consequently, the neuroscience community created a large-scale, customizable, mathematical model which simulates brain activity called The Virtual Brain (TVB). Using TVB, we seek to control electroencephalography (EEG) measured brain states using auditory inputs, through TVB. A safe non-invasive brain stimulation method is binaural beats (BB) which arise from the brain’s interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. A third phantom BB, whose frequency is equal to the difference of the two presented tones, is produced. This paper details the development and proof-of-concept testing of a simulation environment for an EEG-based closed-loop control of TVB using BB. Results suggest that the connectivity networks, constructed from simulated EEG, may change with certain BB stimulation frequency. In this work, we demonstrate that a linear controller can successfully modulate TVB connectivity.