Trajectory-based generic chassis control framework for the MMX-Rover

This paper describes the application examples of satellite interferometric synthetic aperture radar (InSAR) in exploring potential geo-hazards and mapping geo-related disasters, including earthquakes, landslides, and land subsidence, triggered by natural forces and/or human activities. Satellite images were acquired from the Japan Aerospace Exploration Agency's (JAXA's) ALOS-PALSAR satellite, the Euroeapn Space Agency's (ESA's) Envisat-ASAR satellite, and the German Aerospace Center's (DLR's) TerraSAR-X satellite, covering frequency bands of L, C, and X, respectively. The study areas include Taiwan and Vietnam because both regions are prone to greater risks of geo-hazards and natural disasters. A series of C-band and L-band both ascending and descending mode SAR imagery data were used to map the deformation and subsidence of the environmentally sensitive areas. With the historical information and fast updated data sets, long-term and short-term subsidence patterns can be effectively and efficiently obtained. Excellent agreements were obtained as compared with Global Positioning System (GPS) and leveling measurements. Stacking of dual-beam mode as ascending and descending mode could extract the vertical displacement more easily and reduce the influence from the horizontal displacement. In summary, the advance of satellite radar interferometry provides a vital mapping to detect and identify geo-hazard and potential geo-disaster in general.

In any mechatronic system, faults can occur. Likewise
\nalso in the MMX rover, which is a wheeled rover mutually
\ndeveloped by CNES (Centre national d'études spatiales) and
\nDLR (German Aerospace Center), intended to land on Phobos.
\nAn essential part of the MMX rover is the locomotion subsystem
\nwhich includes several sensors and eight motors actuating the
\nfour legs and the four wheels. In each of these components and
\ntheir interfaces, there is a possibility that faults arise and lead
\nto subsystem failures, which would mean that the rover cannot
\nmove anymore. To reduce this risk, the possible faults of the
\nMMX locomotion subsystem were identified in a FMECA study
\nand their criticality was classified, which is presented in here.
\nDuring this examination, the criticality was graded depending
\non different mission phases. With the help of this study, the
\nhardware, firmware and software design were enhanced. Fur-
\nther, certain fault detection, isolation and recovery strategies
\nwere implemented in the locomotion firmware and software as
\nwell as in the full rover software.

A generic control system framework for both real-time and batch six-degree-of-freedom (6-DOF) simulations is presented. This framework uses a simplified dynamic inversion technique to allow for stabilization and control of any type of aircraft at the pilot interface level. The simulation, designed primarily for the real-time simulation environment, also can be run in a batch mode through a simple guidance interface. Direct vehicle-state acceleration feedback is required with the simplified dynamic inversion technique. The estimation of surface effectiveness within real-time simulation timing constraints also is required. The generic framework provides easily modifiable control variables, allowing flexibility in the variables that the pilot commands. A direct control allocation scheme is used to command aircraft effectors. Primary uses for this system include conceptual and preliminary design of aircraft, when vehicle models are rapidly changing and knowledge of vehicle 6-DOF performance is required. A simulated airbreathing hypersonic vehicle and simulated high-performance fighter aircraft are used to demonstrate the flexibility and utility of the control system.

In this article, an enhanced model reference adaptive control (EMRAC) algorithm is used to design a generic lateral-tracking controller for a vehicle. This EMRAC is different from the EMRAC in the literature as it adopts a σ-modification approach to bind the adaptive gain of the switching action. Moreover, an extended Lyapunov theory for discontinuous systems is used to analytically prove the ultimate boundedness of the closed-loop control system when the adaptive gain of the switching action is bounded with a σ-modification strategy. The control algorithm is applied to a vehicle path-tracking problem and its tracking performance is investigated under conditions of: 1) external disturbances such as crosswind; 2) road surface changes; 3) modeling errors; and 4) parameter missmatches in a co-simulation environment based on IPG Carmaker/MATLAB. The simulation studies show that the controller is effective at tracking a given reference path for performing different autonomous highway driving maneuvers while ensuring the boundedness of all closed-loop signals even when the system is subjected to the conditions mentioned above.

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.

The use of autonomous vehicles is becoming ubiquitous due to the versatility and flexibility that they display in the execution of individual and cooperative tasks, coupled with the fact that their use avoids placing human lives at risk. Closely related to these autonomous systems and their control are the simulation tools. A poor control design could have dramatic consequences to the vehicle itself, the rest of vehicles and even the environment in a real scenario. Thus, these tools are essential to test the correct design and behaviour of the modelling and control algorithms theoretically. In this work a benchmark for unmanned vehicles is presented. The main objective is to have a generic framework in which to develop and test control algorithms for coordinated and cooperative tasks between different kinds of vehicles. The tool is constructed in a modular way, so as any kind of vehicle can be simulated (or tested) with a slight modification of the program. The benchmark can run continuous and discrete (DEVS and non-DEVS) simulations and it is constructed over LabVIEW as Hardware-In-The-Loop (HIL) simulation platform. The use of the same system for simulation and real experiments reduces the cost of hardware tests and facilitates enormously the portability of the theoretical design to the real world. This tool is oriented both for researchers and students, to test their own control algorithms both theoretically and experimentally.

The PRISMA project constitutes an in-orbit test bed for Guidance, Navigation and Control (GNC) algorithms and sensors for advanced formation flying and rendezvous operations. Several experiments regarding manoeuvring and sensor experiments will be performed, sorted in 4 main categories – Autonomous Formation Flying, Homing and Rendezvous, Proximity Operations and Final Approach and Recede. The project consists of two satellites, one highly manoeuvrable Main satellite and one Target satellite without orbit control. The spacecraft masses are in the order of 150 and 50 kg respectively. The project is carried out by Swedish Space Corporation (SSC) in close cooperation with the German Aerospace Centre (DLR), CNES and the Danish Technical University. The project will also demonstrate 2 new propulsion technologies, the High Performance Green Propellant motor system and a MEMS technology microthruster cold-gas system milli-Newton thrust range. The launch is planned for the spring of 2009. The mission will be controlled from SSC facilities in Stockholm and Kiruna.

PRISMA—A formation flying project in implementation phase

Editorial

The control performance and robust behaviour of model-based control algorithms have been proved better than those of the traditional control algorithms. However, when the processes have highly nonlinear behaviour, use of linear model-based control strategies alone does not quite provide good control performance. Therefore, in this study a nonlinear-state feedback control (NSFC) strategy is developed to overcome such nonlinear process control problems. The basic structure of the NSFC strategy is coupled with a steady-state model and a nonlinear-state feedback controller derived from the nonlinear process model. We then apply the NSFC concept to establish an nonlinear inferential control system to control the packed-bed reactor. Simulation results have shown that the control performances of the NSFC scheme are much better than those of the internal model control (IMC) scheme and the generic model control (GMC) scheme.

On-line optimization and control of fed-batch fermentation is difficult due to its non linearity and dynamic operation points with uncertainty. A novel cascade optimal control algorithm was proposed to treat these difficulties with the operating points, which maximize the objective function, was defined by Necessary Conditions of Optimization (NCO). Consequently, a nonlinear Generic Model Control (GMC) was applied to eliminate effect of uncertainty. Calculations of Hamilton gradient online and repeated optimization were avoided. The simulation on saccharomyces cerevisiae enzyme cultivation process showed the efficiency and advantage of the proposed method.

This paper presents a new method for multivariable dead-time compensation for a wide class of chemical engineering process control problems. The method utilizes the powerful performance-following property of Generic Model Control (GMC). The design procedure involves model selection including process deadtime estimates together with a trajectory specification. A subset of the method is derived for use of nonlinear steady-state models directly in the control algorithm. Simulation of both linear and nonlinear systems demonstrate the simplicity and efficiency of the technique.

Process model-based control algorithms that employ a process model directly in the controller, have been shown to produce good control performance and robust behaviour, despite process modelling errors. However, when the process/model mismatch is large, the closed-loop response, while still being better than responses obtained by conventional controllers, will be degraded. This paper presents a new approach to compensate for process/model mismatch errors, and is based upon the Generic Model Control (GMC) algorithm. This approach is applicable to both linear and nonlinear model-based algorithms. Simulation results are presented to illustrate the efficiency of the approach

In this thesis, we design and develop controllers for trajectory planning and trajectory tracking to tackle autonomous high-speed overtaking for the next generation of autonomous vehicles. Both the controllers are developed for a JLR Range Rover Sport that is capable of autonomous driving functionalities. To assist with controller development, a high-fidelity vehicle model previously developed in IPG Carmaker is utilised that contains all the multi-body interactions and non-linear tyre characteristics. Trajectory Planning Autonomous high-speed driving is a safety-critical task and it is imperative that the planned trajectory of the vehicle can ensure safety (collision-avoidance) while computing smooth and feasible trajectories. We propose a trajectory planning framework that utilises information of the traffic vehicles to identify safe driving zones on the road using potential field functions and a robust model predictive controller for generating feasible trajectories that ensure the vehicle remains within the safe zones while performing the overtaking manoeuvre. The closed-loop performance of this controller is validated in a high-fidelity co-simulation environment. Trajectory Tracking The trajectory tracking controller is designed to ensure that the vehicle tracks the trajectory as closely as possible and preserves the lateral-yaw stability at all times. In this thesis, an Enhanced Model Reference Adaptive Control algorithm is used to design a generic lateral tracking controller for an autonomous vehicle. The control algorithm is applied to a vehicle path tracking problem and its tracking performance is investigated when subjected to external disturbances such as crosswind, road surface changes, modelling errors, and parameter miss-matches in a high-fidelity co-simulation environment. Combined Planning & Control Finally, the design of a combined motion planning & control scheme is carried out. The lateral tracking controller is augmented to include the dynamics of the steering actuator system and the updated tracking controller is combined with the RMPC based sophisticated path-planning framework to present a hierarchical closed-loop control architecture for autonomous overtaking. This architecture is implemented on the IPG CarMaker/Simulink environment and validated with different overtaking manoeuvring scenarios.

Achieving good uniformity process control in chemical mechanical polishing (CMP) requires a representative uniformity metric and strong models relating this metric to process tunable inputs. Previous efforts in CMP uniformity control have yielded acceptable results utilizing a center-to-edge (CTE) first order nonuniformity metric. Closer analysis of post CMP process nonuniformity, however, reveals significant higher order nonuniformity components such as the center “dimple” and outer “doughnut” regions. These nonuniformity characteristics are due in part to upstream chemical vapor deposition (CVD) processing. Utilizing a multizone approach to uniformity modeling, a more accurate mathematical model of CMP uniformity has been identified. The model has been utilized to customize a thickness and uniformity multivariate run-to-run software control solution for the process. The controller is based on the generic cell controller structure, which is a proven enabler for run-to-run control for a number of processes including CMP, vapor phase epitaxy, and etch. The control algorithm is a zeroth order adjustable linear approximation two-stage algorithm with exponentially weighted moving average noise filtering. This algorithm, which supports first order linear and nonlinear models, has been demonstrated to be effective in CMP CTE and thickness multivariate control. The control solution has been enhanced to utilize both pre and post CMP process metrology along with process models to suggest process recipe modifications on a run-to-run basis. Results indicate improved control of CMP process nonuniformity qualities of interest. Further, the results quantify the significant benefit of utilizing premetrology (feedforward) information in addition to traditional postmetrology (feedback) in determining control recipe advice.