Resource-aware motion control:feedforward, learning, and feedback

Controllers with new sampling schemes improve motion systems’ performanc

Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation

Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation

Performance-driven control of nano-motion systems

The performance of high-precision mechatronic systems is subject to ever increasing demands regarding speed and accuracy. To meet these demands, new actuator drivers, sensor signal processing and control algorithms have to be derived. The state-of-the-art scientific developments in these research directions can significantly improve the performance of high-precision systems. However, translation of the scientific developments to usable technology is often non-trivial. To improve the performance of high-precision systems and to bridge the gap between science and technology, a performance-driven control approach has been developed. First, the main performance limiting factor (PLF) is identified. Then, a model-based compensation method is developed for the identified PLF. Experimental validation shows the performance improvement and reveals the next PLF to which the same procedure is applied. The compensation method can relate to the actuator driver, the sensor system or the control algorithm. In this thesis, the focus is on nano-motion systems that are driven by piezo actuators and/or use encoder sensors. Nano-motion systems are defined as the class of systems that require velocities ranging from nanometers per second to millimeters per second with a (sub)nanometer resolution. The main PLFs of such systems are the actuator driver, hysteresis, stick-slip effects, repetitive disturbances, coupling between degrees-of-freedom (DOFs), geometric nonlinearities and quantization errors. The developed approach is applied to three illustrative experimental cases that exhibit the above mentioned PLFs. The cases include a nano-motion stage driven by a walking piezo actuator, a metrological AFM and an encoder system. The contributions of this thesis relate to modeling, actuation driver development, control synthesis and encoder sensor signal processing. In particular, dynamic models are derived of the bimorph piezo legs of the walking piezo actuator and of the nano-motion stage with the walking piezo actuator containing the switching actuation principle, stick-slip effects and contact dynamics. Subsequently, a model-based optimization is performed to obtain optimal drive waveforms for a constant stage velocity. Both the walking piezo actuator and the AFM case exhibit repetitive disturbances with a non-constant period-time, for which dedicated repetitive control methods are developed. Furthermore, control algorithms have been developed to cope with the present coupling between and hysteresis in the different axes of the AFM. Finally, sensor signal processing algorithms have been developed to cope with the quantization effects and encoder imperfections in optical incremental encoders. The application of the performance-driven control approach to the different cases shows that the different identified PLFs can be successfully modeled and compensated for. The experiments show that the performance-driven control approach can largely improve the performance of nano-motion systems with piezo actuators and/or encoder sensors

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Preview Scheduled Model Predictive Control For Horizontal Axis Wind Turbines

This research investigates the use of model predictive control (MPC) in application to wind turbine operation from start-up to cut-out. The studies conducted are focused on the design of an MPC controller for a 650 KW, three-bladed horizontal axis turbine that is in operation at the National Renewable Energy Laboratory\u27s National Wind Technology Center outside of Golden, Colorado. This turbine is at the small end of utility scale turbines, but it provides advanced instrumentation and control capabilities, and there is a good probability that the approach developed in simulation for this thesis, will be field tested on the actual turbine. MPC is an active area for turbine control research, because wind turbine operation is complicated by multiple factors that are intrinsic to harvesting power from the wind resource: Since the goal of the turbine is to produce as much energy as possible from the available power in the air ow passing through the turbine\u27s rotor plane, either the turbine\u27s blade pitch (used to regulate aerodynamic torque), or the generator load torque (used to regulate rotor speed at the optimal tip-speed-ratio) are routinely set at the limits of their operating range. There is a significant variation in the gain from perturbations in blade pitch to perturbations in bending moments and torque. This variation is dependent on the relative speed between the blade and wind, and the nominal blade pitch. As a result, gain scheduling techniques are found to be necessary in order to obtain adequate speed regulation, and optimal load mitigation. The three individual pitch (IP) commands and the generator load command, along with structural loads that can be in conflict with speed regulation objectives, make the turbine control problem inherently multi-input-multi-output (MIMO) in nature. Advanced measurement technologies like LIDAR (light detection and ranging) make the use of preview control plausible in the near future. Standard formulations of MPC accommodate each of these issues. Also, a common MPC technique provides integral-like control to achieve offset-free operation [9]. At the same time in wind turbine applications, multiple studies [38, 5, 73] have developed \feed-forward\u22 controls based on applying a gain to an estimate of the wind speed changes obtained from an observer incorporating a disturbance model. These approaches are based on a technique that can be referred to as disturbance accommodating control (DAC) [32]. In this thesis, it is shown that offset-free tracking MPC [52] is equivalent to a DAC approach when the disturbance gain is computed to satisfy a regulator equation. Although the MPC literature has recognized that this approach provides \structurally stable\u22 [20] disturbance rejection and tracking, this step is not typically divorced from the MPC computations repeated each sample hit. The DAC formulation is conceptually simpler, and essentially uncouples regulation considerations from MPC related issues. This thesis provides a self contained proof that the DAC formulation (an observer-controller and appropriate disturbance gain) provides structurally stable regulation

Quantification of human operator skill in a driving simulator for applications in human adaptive mechatronics

Nowadays, the Human Machine System (HMS) is considered to be a proven technology, and now plays an important role in various human activities. However, this system requires that only a human has an in-depth understanding of the machine operation, and is thus a one-way relationship. Therefore, researchers have recently developed Human Adaptive Mechatronics (HAM) to overcome this problem and balance the roles of the human and machine in any HMS. HAM is different compared to ordinary HMS in terms of its ability to adapt to changes in its surroundings and the changing skill level of humans. Nonetheless, the main problem with HAM is in quantifying the human skill level in machine manipulation as part of human recognition. Therefore, this thesis deals with a proposed formula to quantify and classify the skill of the human operator in driving a car as an example application between humans and machines. The formula is evaluated using the logical conditions and the definition of skill in HAM in terms of time and error. The skill indices are classified into five levels: Very Highly Skilled, Highly Skilled, Medium Skilled, Low Skilled and Very Low Skilled. Driving was selected because it is considered to be a complex mechanical task that involves skill, a human and a machine. However, as the safety of the human subjects when performing the required tasks in various situations must be considered, a driving simulator was used. The simulator was designed using Microsoft Visual Studio, controlled using a USB steering wheel and pedals, as was able to record the human ii path and include the desired effects on the road. Thus, two experiments involving the driving simulator were performed; 20 human subjects with a varying numbers of years experience in driving and gaming were used in the experiments. In the first experiment, the subjects were asked to drive in Expected and Guided Conditions (EGC). Five guided tracks were used to show the variety of driving skill: straight, circular, elliptical, square and triangular. The results of this experiment indicate that the tracking error is inversely proportional to the elapsed time. In second experiment, the subjects experienced Sudden Transitory Conditions (STC). Two types of unexpected situations in driving were used: tyre puncture and slippery surface. This experiment demonstrated that the tracking error is not directly proportional to the elapsed time. Both experiments also included the correlation between experience and skill. For the first time, a new skill index formula is proposed based on the logical conditions and the definition of skill in HAM