Showing papers in "Journal of Dynamic Systems Measurement and Control-transactions of The Asme in 2017"

Torque Distribution Strategies for Energy-Efficient Electric Vehicles with Multiple Drivetrains

Abstract: The paper discusses novel computationally efficient torque distribution strategies for electric vehicles with individually controlled drivetrains, aimed at minimizing the overall power losses while providing the required level of wheel torque and yaw moment. Analytical solutions of the torque control allocation problem are derived and effects of load transfers due to moderate driving/braking and cornering conditions are studied and discussed in detail. Influences of different drivetrain characteristics on the front and rear axles are described. The results of the analytically-derived algorithm are contrasted with those from two other control allocation strategies, based on the off-line numerical solution of more detailed formulations of the control allocation problem (i.e., a multi-parametric non-linear programming problem). The solutions of the control allocation problem are experimentally validated along multiple driving cycles and in steady-state cornering, on an electric vehicle with four identical drivetrains. The experiments show that the computationally efficient algorithms represent a very good compromise between low energy consumption and controller complexity.

A Comparative Study of the Energy-Saving Controllers for Automotive Air-Conditioning/Refrigeration Systems

Abstract: With the extensive application of air-conditioning/refrigeration (A/ C-R) systems in homes, industry, and vehicles, many efforts have been put toward the controller development for A/C-R systems. Therefore, this paper proposes an energy-saving model predictive controller (MPC) via a comparative study of several control approaches that could be applied in automotive A/C-R systems. The on/off controller is first presented and used as a basis to compare with others. The conventional proportional-integral (PI) as well as a set-point controller follows. In the set-point controller, the sliding mode control (SMC) strategies are also employed. Then, the MPC is elaborated upon. Finally, the simulation and experimental results under the same scenario are compared to demonstrate how the advanced MPC can bring more benefits in terms of performance and energy saving (10%) over the conventional controllers. [DOI: 10.1115/1.4034505]

Mitigating the Effects of Sensor Uncertainties in Networked Multi-Agent Systems

Abstract: Networked multi-agent systems consist of interacting agents that locally exchange information, energy, or matter. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multi-agent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important class of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor uncertainties, which can arise due to low sensor quality, sensor failure, sensor bias, or detrimental environmental conditions. This paper presents a novel distributed adaptive control architecture for networked multi-agent systems with undirected communication graph topologies to mitigate the effect of sensor uncertainties. Specifically, we consider agents having identical high-order, linear dynamics with agent interactions corrupted by unknown exogenous disturbances. We show that the proposed adaptive control architecture guarantees asymptotic stability of the closed-loop dynamical system when the exogenous disturbances are time-invariant and uniform ultimate boundedness when the exogenous disturbances are time-varying. Two numerical examples are provided to illustrate the efficacy of the proposed distributed adaptive control architecture. [DOI: 10.1115/1.4035092]

Tracking Control of Linear Time-Invariant Nonminimum Phase Systems Using Filtered Basis Functions

Abstract: An approach for minimizing tracking errors in linear time-invariant (LTI) single-input single-output (SISO) discrete-time systems with nonminimum phase (NMP) zeros using filtered basis functions (FBF) is studied. In the FBF method, the control input to the system is expressed as a linear combination of basis functions. The basis functions are forward filtered using the dynamics of the NMP system, and their coefficients are selected to minimize the error in tracking a given desired trajectory. Unlike comparable methods in the literature, the FBF method is shown to be effective in tracking any desired trajectory, irrespective of the location of NMP zeros in the z-plane. The stability of the method and boundedness of the control input and system output are discussed. The control designer is free to choose any suitable set of basis functions that satisfy the criteria discussed in this paper. However, two rudimentary basis functions, one in time domain and the other in frequency domain, are specifically highlighted. The effectiveness of the FBF method is illustrated and analyzed in comparison with the truncated series (TS) approximation method. [DOI: 10.1115/1.4034367]

Nonlinear Observer for Tightly Integrated Inertial Navigation Aided by Pseudo-Range Measurements

Abstract: A modular nonlinear observer for inertial navigation aided by pseudo-range measurements is designed and analyzed. The attitude observer is based on a recent nonlinear complementary filter that uses magnetometer and accelerometer vector measurements to correct the quaternion attitude estimate driven by gyro measurements, including gyro bias estimation. A tightly integrated translational motion observer is driven by accelerometer measurements, employs the attitude estimates, and makes corrections using the pseudo-range and range-rate measurements. It estimates position, range bias errors, velocity and specific force in an Earth-fixed Cartesian coordinate frame, where the specific force estimate is used as a reference vector for the accelerometer measurements in the attitude observer. The exponential stability of the feedback interconnection of the two observers is analyzed and found to have a semi-global region of attraction with respect to attitude observer initialization, and local region of attraction with respect to translational motion observer initialization. The latter is due to linearization of the range measurement equations that is underlying the selection of injection gains by solving a Riccati equation. In typical applications the pseudo-range equations admit an explicit algebraic solution that can be easily computed and used to accurately initialize the position and velocity estimates. Hence, the limited region of attraction is not seen as a practical limitation of the approach. Advantages of the proposed nonlinear observer are low computational complexity and a solid theoretical foundation.

Periodic Signal Tracking for Lightly Damped Systems

Abstract: The focus of this paper is on the development of time-delay filters to accomplish tracking of periodic signals with zero phase errors. The class of problems addressed include systems whose dynamics are characterized by lightly damped modes. A general approach for the zero-phase tracking of periodic inputs is presented followed by an illustration of single harmonic tracking of underdamped second-order systems with relative degree two. A general formulation of the approach is then posed for higher-order systems and systems including zeros. The paper concludes with the illustration of enforcing constraints to desensitize the time-delay filter to uncertainties in the location of the poles of the system and forcing frequencies. A numerical practical design case based on a medical X-ray system is used to illustrate the potential of the proposed technique.

Observer-Based Boundary Control Design for the Suppression of Stick–Slip Oscillations in Drilling Systems With Only Surface Measurements

Abstract: We develop an observer-based boundary controller for the rotary table to suppress stick–slip oscillations and to maintain the angular velocity of the drill string at a desired value during a drilling process despite unknown friction torque and by using only surface measurements. The control design is based on a distributed model of the drill string. The obtained infinite dimensional model is converted to an ordinary differential equation–partial differential equation (ODE–PDE) coupled system. The observer-based controller is designed by reformulating the problem as the stabilization of an linear time-invariant (LTI) system which is affected by a constant unknown disturbance and has simultaneous actuator and sensor delays. The main contribution of the controller is that it requires only surface measurements. We prove that the equilibrium of the closed-loop system is exponentially stable, and that the angular velocity regulation is achieved with the estimations of unknown friction torque and drill bit velocity. The effectiveness of the controller is demonstrated using numerical simulations. [DOI: 10.1115/1.4036549]

Control-Oriented Model of Molar Scavenge Oxygen Fraction for Exhaust Recirculation in Large Diesel Engines

Abstract: Exhaust gas recirculation (EGR) systems have been introduced to large marine engines in order to reduce NOx formation. Adequate modeling for control design is one of the bottlenecks to design EGR c ...

Vehicle Positioning Based on Velocity and Heading Angle Observer Using Low-Cost Sensor Fusion

Abstract: The vehicle positioning system can be utilized for various automotive applications. Primarily focusing on practicality, this paper presents a new method for vehicle positioning systems using low-cost sensor fusion, which combines global positioning system (GPS) data and data from easily available in-vehicle sensors. As part of the vehicle positioning, a novel nonlinear observer for vehicle velocity and heading angle estimation is designed, and the convergence of estimation error is also investigated using Lyapunov stability analysis. Based on this estimation information, a new adaptive Kalman filter with rulebased logic provides robust and highly accurate estimations of the vehicle position. It adjusts the noise covariance matrices Q and R in order to adapt to various environments, such as different driving maneuvers and ever-changing GPS conditions. The performance of the entire system is verified through experimental results using a commercial vehicle. Finally, through a comparative study, the effectiveness of the proposed algorithm is confirmed. [DOI: 10.1115/1.4036881]

Implementation of Reversing Control on a Doubly Articulated Vehicle

Abstract: This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) and Volvo Trucks through an Industrial CASE award. The authors would like to acknowledge Leo Laine and Carl-Johan Hoel from Volvo Trucks for their collaboration and contributions to the research. The authors would like to acknowledge Denby Transport for their loan of the ‘Extra’ Eco-Link B-trailer for use in vehicle testing. The authors would like to acknowledge the members of the CVDC who supported the work in this paper. At the time of writing, the Consortium consisted of the University of Cambridge with the following partners from the heavy vehicle industry: Anthony Best Dynamics, Camcon, Denby Transport, Firestone Air Springs, GOODYEAR DUNLOP, Haldex, Motor Industry Implementation of Reversing Control on a Doubly-Articulated Vehicle 15 Research Association, SDC Trailers, SIMPACK, Tinsley Bridge, Tridec, Volvo Trucks, and Wincanton.

Real-Time Estimation of Lithium-Ion Concentration in Both Electrodes of a Lithium-Ion Battery Cell Utilizing Electrochemical–Thermal Coupling

Abstract: This paper proposes and demonstrates an estimation scheme for Li-ion concentrations in both electrodes of a Li-ion battery cell. The well-known observability deficiencies in the two-electrode electrochemical models of Li-ion battery cells are first overcome by extending them with a thermal evolution model. Essentially, coupling of electrochemical-thermal dynamics emerging from the fact that the Lithium concentrations contribute to the entropic heat generation is utilized to overcome the observability issue. Then, an estimation scheme comprised of a cascade of a sliding mode observer and an Unscented Kalman filter (UKF) is constructed that exploits the resulting structure of the coupled model. The approach gives new real-time estimation capabilities for two often-sought pieces of information about a battery cell: 1) estimation of cell-capacity, and 2) tracking the capacity loss due to degradation mechanisms such as Lithium plating. These capabilities are possible since the two-electrode model need not be reduced further to a single electrode model by adding Li conservation assumptions, which do not hold with long-term operation. Simulation studies are included for the validation of the proposed scheme. Effect of 1 Corresponding author Journal of Dynamic Systems, Measurement, and Control 2 Dey, # DS-15-1432 measurement noise and parametric uncertainties are also included in the simulation results to evaluate the performance of the proposed scheme.

Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

Abstract: A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-meansquare (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF. [DOI: 10.1115/1.4036546]