Bilateral Control with Task Learning and Adaptation to Environment

The Motion Copying System permits to save an operator task in terms of position and force references for the action reproduction whenever the operator isn't available or to train new users. This thesis analyzes the MCS design and limitations, the Bilateral Control System on which the MCS is based, and proposes a model to adapt the saved task to new environmental conditions

Precision Control of High Speed Ball Screw Drives

Industrial demands for higher productivity rates and more stringent part tolerances require faster production machines that can produce, assemble, or manipulate parts at higher speeds and with better accuracy than ever before. In a majority of production machines, such as machine tools, ball screw drives are used as the primary motion delivery mechanism due to their reasonably high accuracy, high mechanical stiffness, and low cost. This brings the motivation for the research in this thesis, which has been to develop new control techniques that can achieve high bandwidths near the structural frequencies of ball screw drives, and also compensate for various imperfections in their motion delivery, so that better tool positioning accuracy can be achieved at high speeds. A precision ball screw drive has been designed and built for this study. Detailed dynamic modeling and identification has been performed, considering rigid body dynamics, nonlinear friction, torque ripples, axial and torsional vibrations, lead errors, and elastic deformations. Adaptive Sliding Mode Controller (ASMC) is designed based on the rigid body dynamics and notch filters are used to attenuate the effect of structural resonances. Feedforward friction compensation is also added to improve the tracking accuracy at velocity reversals. A bandwidth of 223 Hz was achieved while controlling the rotational motion of the ball screw, leading to a servo error equivalent to 1.6 um of translational motion. The motor and mechanical torque ripples were also modeled and compensated in the control law. This improved the motion smoothness and accuracy, especially at low speeds and low control bandwidths. The performance improvement was also noticeable when higher speeds and control bandwidths were used. By adding on the torque ripple compensation, the rotational tracking accuracy was improved to 0.95 um while executing feed motions with 1 m/sec velocity and 1 g acceleration. As one of the main contributions in this thesis, the dynamics of the 1st axial mode (at 132 Hz) were actively compensated using ASMC, which resulted in a command tracking bandwidth of 208 Hz. The mode compensating ASMC (MC-ASMC) was also shown to improve the dynamic stiffness of the drive system, around the axial resonance, by injecting additional damping at this mode. After compensating for the lead errors as well, a translational tracking accuracy of 2.6 um was realized while executing 1 m/sec feed motions with 0.5 g acceleration transients. In terms of bandwidth, speed, and accuracy, these results surpass the performance of most ball screw driven machine tools by 4-5 times. As the second main contribution in this thesis, the elastic deformations (ED) of the ball screw drive were modeled and compensated using a robust strategy. The robustness originates from using the real-time feedback control signal to monitor the effect of any potential perturbations on the load side, such as mass variations or cutting forces, which can lead to additional elastic deformations. In experimental results, it is shown that this compensation scheme can accurately estimate and correct for the elastic deformation, even when there is 130% variation in the translating table mass. The ED compensation strategy has resulted in 4.1 um of translational accuracy while executing at 1 m/sec feed motion with 0.5 g acceleration transients, without using a linear encoder. This result is especially significant for low-cost CNC (Computer Numerically Controlled) machine tools that have only rotary encoders on their motors. Such machines can benefit from the significant accuracy improvement provided by this compensation scheme, without the need for an additional linear encoder

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications

Virtual Model Building for Multi-Axis Machine Tools Using Field Data

Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency. Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below. As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well. As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup. The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification. Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted. Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process

High Precision Positioning and Very Low Velocity Control of a Permanent Magnet Synchronous Motor

The purpose of this report is to evaluate a direct driven permanent magnet motor in high accuracy position and low speed operation. Actuation in this case is usually accomplished by stepping motors combined with belts and pulleys. High accuracy positioning is considered to be within 0.1 degrees and low speed 0.05 degrees per second, while at the same time have a 180 degree step response within 0.5 second. A model is derived of the motor along with methods for model parameter identification. This model is the basis for simulation of the motor in closed loop control. A prototype is developed in order to prove the validity of the results made by simulations. Experiments on the prototype resulted in two control methods, namely field oriented control and synchronous control. Conclusions drawn from the projects are as follows. The simulations do mirror the inherent problems with the permanent magnet motor. The prototype developed for the project is functioning and highly capable. Field oriented control was unable to meet the specified requirements. However, combined with iterative learning control the performance was improved significantly. Synchronous control satisfied most of the requirements, although its responsiveness and low efficiency are possible areas of improvement in future research

Feedback control of cycling in spinal cord injury using functional electrical stimulation

This thesis is concerned with the realisation of leg cycling by means of FES in SCI individuals with complete paraplegia. FES lower-limb cycling can be safely performed by paraplegics on static ergometers or recumbent tricycles. In this work, different FES cycling systems were developed for clinical and home use. Two design approaches have been followed. The first is based on the adaptation of commercially available recumbent tricycles. This results in devices which can be used as static trainers or for mobile cycling. The second design approach utilises a commercially available motorised ergometer which can be operated while sitting in a wheelchair. The developed FES cycling systems can be operated in isotonic (constant cycling resistance) or isokinetic mode (constant cadence) when used as static trainers. This represents a novelty compared to existing FES cycling systems. In order to realise isokinetic cycling, an electric motor is needed to assist or resist the cycling movement to maintain a constant cadence. Repetitive control technology is applied to the motor in this context to virtually eliminate disturbance caused by the FES activated musculature which are periodic with respect to the cadence. Furthermore, new methods for feedback control of the patient’s work rate have been introduced. A one year pilot study on FES cycling with paraplegic subjects has been carried out. Effective indoor cycling on a trainer setup could be achieved for long periods up to an hour, and mobile outdoor cycling was performed over useful distances. Power output of FES cycling was in the range of 15 to 20 W for two of the three subjects at the end of the pilot study. A muscle strengthening programme was carried out prior and concurrent to the FES cycling. Feedback control of FES assisted weight lifting exercises by quadriceps stimulation has been studied in this context