Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems

[EN] This paper presents a novel control strategy that provides active disturbance rejection predictive control on constrained systems with no nominal identified model. The proposed loop relaxes the modelling requirement to a fixed discrete-time state¿space realisation of a first-order plus integrator plant despite the nature of the controlled process. A third-order discrete Extended State Observer (ESO) estimates the model mismatch and assumed plant states. Moreover, the constraints handling is tackled by incorporating the compensation term related to the total perturbation in the definition of the optimisation problem constraints. The proposal merges in a new way state¿space Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) into an architecture suitable for the servo-regulatory operation of linear and non-linear systems, as shown through validation examples.This work has been supported by MCIN/AEI/10.13039/501100011033 [Project PID2020-120087GB-C21] , MCIN/AEI/10.13039/501100011033 [Project PID2020-119468O-I00] , the Generalitat Valenciana regional government, Spain [Project CIAICO/2021/064] , and the Ministry of Science, Technology and Innovation of Colombia [scholarship programme 885] .Martínez-Carvajal, BV.; Sanchís Saez, J.; Garcia-Nieto, S.; Martínez Iranzo, MA. (2023). Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems. ISA Transactions. 142:148-163. https://doi.org/10.1016/j.isatra.2023.08.01114816314

A laboratory breadboard system for dual-arm teleoperation

The computing architecture of a novel dual-arm teleoperation system is described. The novelty of this system is that: (1) the master arm is not a replica of the slave arm; it is unspecific to any manipulator and can be used for the control of various robot arms with software modifications; and (2) the force feedback to the general purpose master arm is derived from force-torque sensor data originating from the slave hand. The computing architecture of this breadboard system is a fully synchronized pipeline with unique methods for data handling, communication and mathematical transformations. The computing system is modular, thus inherently extendable. The local control loops at both sites operate at 100 Hz rate, and the end-to-end bilateral (force-reflecting) control loop operates at 200 Hz rate, each loop without interpolation. This provides high-fidelity control. This end-to-end system elevates teleoperation to a new level of capabilities via the use of sensors, microprocessors, novel electronics, and real-time graphics displays. A description is given of a graphic simulation system connected to the dual-arm teleoperation breadboard system. High-fidelity graphic simulation of a telerobot (called Phantom Robot) is used for preview and predictive displays for planning and for real-time control under several seconds communication time delay conditions. High fidelity graphic simulation is obtained by using appropriate calibration techniques

Reverse Engineering Biological Control Systems for Applications in Process Control.

The main emphasis of this dissertation is the development of nonlinear control strategies based on biological control systems. Commonly utilized biological control schemes have been studied in order to reverse engineer the important concepts for applications in process control. This approach has led to the development of a nonlinear habituating control strategy and nonlinear model reference adaptive control schemes. Habituating control is a controller design strategy for nonlinear systems with more manipulated inputs than controlled outputs. Nonlinear control laws that provide input-output linearization while simultaneously minimizing the cost of affecting control are derived. Local stability analysis shows the controller can provide a simple solution to singularity and non-minimum phase problems. A direct adaptive control strategy for a class of single-input, single-output non-linear systems is presented. The major advantage is that a detailed dynamic non-linear model is not required for controller design. Unknown controller functions in the associated input-output linearizing control law are approximated using locally supported radial basis functions. Lyapunov stability analysis is used to derive parameter update laws which ensure the state vector remains bounded and the plant output asymptotically tracks the output of a linear reference model. A nonlinear model reference adaptive control strategy in which a linear model (or multiple linear models) is embedded within the nonlinear controller is presented. The nonlinear control law is constructed by embedding linear controller gains derived from models obtained using standard linear system identification techniques within the associated input-output linearizing control law. Higher-order controller functions are approximated with radial basis functions. Lyapunov stability analysis is used to derive stable parameter update laws. The major disadvantage of the previous techniques is computational expense. Two modifications have been developed. First, the effective dimension is reduced by applying nonlinear principal component analysis to the state variable data obtained from open-loop tests. This allows basis functions to be placed in a lower dimensional space than the original state space. Second, the total number of basis functions is fixed a priori and an algorithm which adds/prunes basis function centers to surround the current operating point on-line is utilized

Modeling, Simulation and Control of Very Flexible Unmanned Aerial Vehicle

This dissertation presents research on modeling, simulation and control of very flexible aircraft. This work includes theoretical and numerical developments, as well as experimental validations. On the theoretical front, new kinematic equations for modeling sensors are derived. This formulation uses geometrically nonlinear strain-based finite elements developed as part of University of Michigan Nonlinear Aeroelastic Simulation Toolbox (UM/NAST). Numerical linearizations of both the flexible vehicle and the sensor measurements are developed, allowing a linear time invariant model to be extracted for control analysis and design. Two different algorithms to perform sensor fusion from different sensor sources to extract elastic deformation are investigated. Nonlinear least square method uses geometry and nonlinear beam strain-displacement kinematics to reconstruct the wing shape. Detailed information such as material properties or loading conditions are not required. The second method is the Kalman filter, implemented in a multi-rate form. This method requires a dynamical system representation to be available. However, it is more robust to noise corruption in sensor measurements. In order to control maneuver loads, Model Predictive Control is applied to maneuver load alleviation of a representative very flexible aircraft (X-HALE). Numerical studies are performed in UM/NAST for pitch up and roll maneuvers. Both control and state constraints are successfully enforced, while reference commands are still being tracked. MPC execution is also timed and current implementation is capable of almost real-time operation. On the experimental front, two aeroelastic testbed vehicles (ATV-6B and RRV-6B) are instrumented with sensors. On ATV-6B, an extensive set of sensors measuring structural, flight dynamic, and aerodynamic information are integrated on-board. A novel stereo-vision measurement system mounted on the body center looking towards the wing tip measures wing deformation. High brightness LEDs are used as target markers for easy detection and to allow each view to be captured with fast camera shutter speed. Experimental benchmarks are conducted to verify the accuracy of this methodology. RRV-6B flight test results are presented. System identification is applied to the experimental data to generate a SISO description of the flexible aircraft. System identification results indicate that the UM/NAST X-HALE model requires some tuning to match observed dynamics. However, the general trends predicted by the numerical model are in agreement with flight test results. Finally, using this identified plant, a stability augmentation autopilot is designed and flight tested. This augmentation autopilot utilizes a cascaded two-loop proportional integral control design, with the inner loop regulating angular rates and outer loop regulating attitude. Each of the three axes is assumed to be decoupled and designed using SISO methodology. This stabilization system demonstrates significant improvements in the RRV-6B handling qualities. This dissertation ends with a summary of the results and conclusions, and its main contribution to the field. Suggestions for future work are also presented.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144019/1/pziyang_1.pd

Seat belt control : from modeling to experiment

In the last decades, vehicle safety has improved considerably. For example, major improvements have been made in the area of the structural crashworthiness of the vehicle, various driver assistance systems have been developed, and enhancements can be found in the restraint systems, the final line of defense in occupant protection. Despite this increase of vehicle safety measures, many fatalities still occur in road transportation. Regarding the unavoidable crashes, a significant amount can be attributed to the fact that the seat belt system does not perform optimally. No crash event or occupant is identical, yet conventional seat belts are – in general – not able to adjust their characteristics accordingly. The system is therefore optimal for only a limited number of crash scenarios and occupant types. With the current sensor and processor technology, it may be possible to develop a seat belt that continuously adapts to the actual crash and occupant conditions. Such a device is referred to as a Continuous Restraint Control (CRC) system, and the work presented in this thesis contributes to the development of this type of systems. The main idea of seat belt control is to add sensors and actuators to the seat belt system. The force in the seat belt is prescribed by the actuator during the crash, such that the risk of injuries are minimized given the current impact severity and occupant size and position. This concept poses several technological challenges, which are in this thesis divided into four research topics. Although many sensor technologies exist nowadays, so far no methods have been proposed to measure the occupant injury responses in real-time. These responses are essential when deciding on the optimal belt force. In this thesis, a solution has been presented for the problem of real-time estimation of (thoracic) injuries and occupant position during a crash. An estimation is performed based on modelbased filtering of a small number of readily available and cheap sensors. Simulation results with a crash victim model indicate that the injury responses can be estimated with sufficient accuracy for control purposes, but that the estimation heavily depends on the accuracy of the model used in the filter. A numerical controller uses these estimated injury responses to compute the optimal seat belt force. In this computation, it has to be taken into account that the occupant position is constrained during the crash by the available space in the vehicle, since contact with the interior may result in serious injury. The controller therefore has to predict the future occupant motion, using a prediction of the future crash behavior, a choice for the future seat belt force, and a model of the vehicle-occupant-belt system. Given the type of control problem, a Model Predictive Control (MPC) approach is used to develop the controller. Simulation results with crash victim models indicate that using this controller lead to a significant injury risk reduction for the thorax, given that an ideal belt actuator is available. The injury estimator, the prediction and control algorithm proposed in the foregoing are designed with simple mathematical models of occupant, seat belt and vehicle interior. It is therefore recognized that such accurate, manageable models are essential in the development of CRC systems. In this thesis, models of various complexities have been constructed that represent three types of widely used crash test dummies. These models are validated against both numerical as experimental data. The conclusion of this validation is that in frontal crashes, the neck and thoracic injury criteria can well be described by linear (time-invariant) models. However, when the models are to be used in the design of a belt control system, more attention has to be given to the modeling of the chest and seat belt. The severity and duration of a typical impact require a seat belt actuator with challenging specifications. For example, it has to deliver very high forces over a large stroke, it must have a high bandwidth, and must be small enough to be fitted in a vehicle post. These devices do not yet exist. In this thesis, a semi-active belt actuator concept is presented. It is based on a pressure-controlled hydraulic valve, which regulates the belt force through an hydraulic cylinder. The actuator is designed and constructed at the TU/e, and evaluated experimentally. Moreover, a moving sled setup has been developed which allows testing the actuator under impact conditions. Experimental results show that the belt actuator meets the requirements, except for the maximum force. The actuator can therefore at this point be used to prescribe belt forces in a safety belt in low-speed impacts

Development of Biomimetic-Based Controller Design Methods for Advanced Energy Systems

A biologically inspired optimal control strategy, denoted as BIO-CS, is proposed for advanced energy systems applications. This strategy combines the ant\u27s rule of pursuit idea with multi-agent and optimal control concepts. The BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents\u27 local interactions. The developed BIO-CS is integrated with an Artificial Neural Network (ANN)-based adaptive component for further improvement of the overall framework. In particular, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO-CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems.;The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. Specifically, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIMRTM software platform is addressed. The proposed control laws are derived in MATLAB RTM environment, while the plant models are built in DYNSIM RTM, and a previously developed MATLABRTM-DYNSIM RTM link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO-CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking.;Other applications of BIO-CS addressed include: i) a nonlinear fermentation process to produce ethanol; and ii) a transfer function model derived from the cyber-physical fuel cell-gas turbine hybrid power system that is part of the Hybrid Performance (HYPER) project at the National Energy Technology Laboratory (NETL). Other theoretical developments in this work correspond to the integration of the BIO-CS approach with Multi-Agent Optimization (MAO) techniques and casting BIO-CS as a Model Predictive Controller (MPC). These developments are demonstrated by revisiting the fermentation process example. The proposed biologically-inspired approaches provide a promising alternative for advanced control of energy systems of the future

Enabling technologies for precise aerial manufacturing with unmanned aerial vehicles

The construction industry is currently experiencing a revolution with automation techniques such as additive manufacturing and robot-enabled construction. Additive Manufacturing (AM) is a key technology that can o er productivity improvement in the construction industry by means of o -site prefabrication and on-site construction with automated systems. The key bene t is that building elements can be fabricated with less materials and higher design freedom compared to traditional manual methods. O -site prefabrication with AM has been investigated for some time already, but it has limitations in terms of logistical issues of components transportation and due to its lack of design exibility on-site. On-site construction with automated systems, such as static gantry systems and mobile ground robots performing AM tasks, can o er additional bene ts over o -site prefabrication, but it needs further research before it will become practical and economical. Ground-based automated construction systems also have the limitation that they cannot extend the construction envelope beyond their physical size. The solution of using aerial robots to liberate the process from the constrained construction envelope has been suggested, albeit with technological challenges including precision of operation, uncertainty in environmental interaction and energy e ciency. This thesis investigates methods of precise manufacturing with aerial robots. In particular, this work focuses on stabilisation mechanisms and origami-based structural elements that allow aerial robots to operate in challenging environments. An integrated aerial self-aligning delta manipulator has been utilised to increase the positioning accuracy of the aerial robots, and a Material Extrusion (ME) process has been developed for Aerial Additive Manufacturing (AAM). A 28-layer tower has been additively manufactured by aerial robots to demonstrate the feasibility of AAM. Rotorigami and a bioinspired landing mechanism demonstrate their abilities to overcome uncertainty in environmental interaction with impact protection capabilities and improved robustness for UAV. Design principles using tensile anchoring methods have been explored, enabling low-power operation and explores possibility of low-power aerial stabilisation. The results demonstrate that precise aerial manufacturing needs to consider not only just the robotic aspects, such as ight control algorithms and mechatronics, but also material behaviour and environmental interaction as factors for its success.Open Acces