Impedance Reduction Controller Design for Mechanical Systems

Safety is one of important factors in control of mechatronic systems interacting with humans. In order to evaluate the safety of such systems, mechanical impedance is often utilized as it indicates the magnitude of reaction forces when the systems are subjected to motions. Namely, the mechatronic systems should have low mechanical impedance for improved safety. In this paper, a methodology to design controllers for reduction of mechanical impedance is proposed. For the proposed controller design, the mathematical definition of the mechanical impedance for open-loop and closed-loop systems is introduced. Then the controllers are designed for stable and unstable systems such that they effectively lower the magnitude of mechanical impedance with guaranteed stability. The proposed method is verified through case studies including simulations.

Vibration Measurement and Monitoring of a Rotating Disk Using Contactless Laser Excitation

This paper proposes a contactless vibration testing system for rotating disks based on an impulse response excited by a laser ablation. High power YAG pulse laser is used in this system for producing an ideal impulse force on structural surface without contact. The contactless vibration testing system is composed of a YAG laser, laser Doppler vibrometer and spectrum analyzer. This system makes it possible to measure vibration characteristics of structures under operation, such as vibration measurement of a rotating disk. The effectiveness of this system is confirmed by experimental and theoretical analyses. In this paper, a platter of hard disk drive is employed as an experimental object. Vibration characteristics of a rotating and non-rotating platter are measured and compared with the results of theoretical analysis.

Simple Clothoid Paths for Autonomous Vehicle Lane Changes at the Limits of Handling

This paper demonstrates that an autonomous vehicle can perform emergency lane changes up to the limits of handling through real-time generation and evaluation of bi-elementary paths. Path curvature and friction limits determine the maximum possible speed along the path and, consequently, the feasibility of the path. This approach incorporates both steering inputs and changes in speed during the maneuver. As a result, varying path parameters and observing the maximum possible entry speed of resulting paths gives insight about when and to what extent a vehicle should brake and turn during emergency lane change maneuvers. Tests on an autonomous vehicle validate this approach for lane changes at the limits of handling.

Dynamic Response of a Dual-Hoist Bridge Crane

Cranes are used in manufacturing facilities, throughout nuclear sites, and in many other applications for various heavy-lifting purposes. Unfortunately, the flexible nature of cranes makes fast and precise motion of the payload challenging and dangerous. Certain applications require the coordinated movement of multiple cranes. Such tasks dramatically increase the complexity of the crane operation. This paper studies the dynamic behavior of a dual-hoist bridge crane. Simulations and experiments are used to develop an understanding of the dynamic response of the system. Various inputs and system configurations are analyzed and important response characteristics are highlighted.

Network Control of Vehicle Lateral Dynamics With Control Allocation and Dynamic Message Priority Assignment

In this paper we study the lateral motion control and torque allocation for four-wheel-independent-drive electric vehicles (4WID-EVs) with combined active front steering (AFS) and direct yaw moment control (DYC) through in-vehicle networks. It is well known that the in-vehicle networks and x-by-wire technologies have considerable advantages over the traditional point-to-point communications, and bring great strengths to 4WID-EVs. However, there are also bandwidth limitations which would lead to message time delays in network communication. We propose a method on effectively utilizing the limited bandwidth resources and attenuating the adverse influence of in-vehicle network-induced time delays, based on the idea of dynamic message priority assignment according to the vehicle states and control signals. Simulation results from a high-fidelity vehicle model in CarSim® show that the proposed vehicle lateral control and torque allocation algorithm can improve the 4WID-EV lateral motion control performance, and the proposed message priority dynamic assignment algorithm can significantly reduce the adverse influence of the in-vehicle network-induced time delays.

Vehicle Health Inferencing Using Feature-Based Neural-Symbolic Networks

In order to optimize the use of fault tolerant controllers for unmanned or autonomous aerial vehicles, a health diagnostics system is being developed. To autonomously determine the effect of damage on global vehicle health, a feature-based neural-symbolic network is utilized to infer vehicle health using historical data. Our current system is able to accurately characterize the extent of vehicle damage with 99.2% accuracy when tested on prior incident data. Based on the results of this work, neural-symbolic networks appear to be a useful tool for diagnosis of global vehicle health based on features of subsystem diagnostic information.

Nonlinear Compensation for High Performance Feedback Systems With Actuator Imperfections

Measures to reduce control performance for greater robustness (e.g. reduced bandwidth, shallow loop roll-off) must be enhanced if the plant or actuators are known to have nonlinear characteristics that cause variations in loop transmission. Common causes of these nonlinear behaviors are actuator saturation and friction/stiction in the moving parts of mechanical systems. Systems with these characteristics that also have stringent closed loop performance requirements present the control designer with an extremely challenging problem. A design method for these systems is presented that combines very aggressive Nyquist-stable linear control to provide large negative feedback with nonlinear feedback to compensate for the effects of multiple nonlinearities in the loop that threaten stability and performance. The efficacy of this approach is experimentally verified on a parallel kinematic mechanism with multiple uncertain nonlinearities used for vibration suppression.

An Adaptive Control Method With Low-Resolution Encoder

An adaptive control methodology with a low-resolution encoder feedback is presented for a biomedical application, the Ros-Drill (Rotationally Oscillating Drill). It is developed primarily for ICSI (Intra-Cytoplasmic Sperm Injection) operations, with the objective of tracking a desired oscillatory motion at the tip of a microscopic glass pipette. It is an inexpensive set-up, which creates high-frequency (higher than 500 Hz) and small-amplitude (around 0.2 deg) rotational oscillations at the tip of an injection pipette. These rotational oscillations enable the pipette to drill into cell membranes with minimum biological damage. Such a motion control procedure presents no particular difficulty when it uses sufficiently precise motion sensors. However, size, costs and accessibility of technology on the hardware components severely constrain the sensory capabilities. Consequently the control mission and the trajectory tracking are adversely affected. This paper presents a dedicated novel adaptive feedback control method to achieve a satisfactory trajectory tracking capability. We demonstrate via experiments that the tracking of the harmonic rotational motion is achieved with desirable fidelity.

Nonparametric Identification of Hammerstein Systems Using Orthogonal Basis Functions as Ersatz Nonlinearities

In this paper, we present a technique for estimating the input nonlinearity of a Hammerstein system by using multiple orthogonal ersatz nonlinearities. Theoretical analysis shows that by replacing the unknown input nonlinearity by an ersatz nonlinearity, the estimates of the Markov parameters of the plant are correct up to a scalar factor, which is related to the inner product of the true input nonlinearity and the ersatz nonlinearity. These coefficients are used to construct and estimate the true nonlinearity represented as an orthogonal basis expansion. We demonstrate this technique by using a Fourier series expansion as well as orthogonal polynomials. We show that the kernel of the inner product associated with the orthogonal basis functions must be chosen to be the density function of the input signal.

The Role of Model Fidelity in Model Predictive Control Based Hazard Avoidance in Unmanned Ground Vehicles Using LIDAR Sensors

Unmanned ground vehicles (UGVs) are gaining importance and finding increased utility in both military and commercial applications. Although earlier UGV platforms were typically exclusively small ground robots, recent efforts started targeting passenger vehicle and larger size platforms. Due to their size and speed, these platforms have significantly different dynamics than small robots, and therefore the existing hazard avoidance algorithms, which were developed for small robots, may not deliver the desired performance. The goal of this paper is to present the first steps towards a model predictive control (MPC) based hazard avoidance algorithm for large UGVs that accounts for the vehicle dynamics through high fidelity models and uses only local information about the environment as provided by the onboard sensors. Specifically, the paper presents the MPC formulation for hazard avoidance using a light detection and ranging (LIDAR) sensor and applies it to a case study to investigate the impact of model fidelity on the performance of the algorithm, where performance is measured mainly by the time to reach the target point. Towards this end, the case study compares a 2 degrees-of-freedom (DoF) vehicle dynamics representation to a 14 DoF representation as the model used in MPC. The results show that the 2 DoF model can perform comparable to the 14 DoF model if the safe steering range is established using the 14 DoF model rather than the 2 DoF model itself. The conclusion is that high fidelity models are needed to push autonomous vehicles to their limits to increase their performance, but simulating the high fidelity models online within the MPC may not be as critical as using them to establish the safe control input limits.