A comparison of classical and robust flight simulator motion control

This paper proposes a unified robust motion controller for the position and force control problems of compliant robot manipulators driven by Series Elastic Actuators (SEAs). It is shown that the dynamic model of the compliant robot includes not only matched but also mismatched disturbances that act on the system through a different channel from the control input. To tackle this complex robust control problem, the unified robust motion controller is synthesised by employing a second-order Disturbance Observer (DOb), which allows us to estimate not only disturbances but also their first and second order derivatives, and a novel controller design approach in state space. By using the Brunovsky canonical form transformation and the estimations of disturbances and their first and second order derivatives, the dynamic model of the robot is reconstructed so that a new system model that includes only matched disturbances is obtained for compliant robots driven by SEAs. The robust position and force controllers are simply designed by eliminating the matched disturbances of the reconstructed system model via the conventional DOb-based robust control method. The stability and performance of the proposed robust motion controllers are verified by simulations.

Using the SIMONA Research Simulator for Human-machine Interaction Research

This paper describes the role of mathematical models in various model-based control schemes such as predictive control and preview control. To prevent the deterioration of performance of these control approaches in the presence of model uncertainties and disturbances, model based methods for state estimation, disturbance estimation and parameter identification may be utilized to obtain robust high performance motion control system.

This paper describes a novel approach of robust motion control of a redundant manipulator by projection planes. In the proposed approach, a robust controller based on a workspace observer is employed in the selectable projection plane. Then, an adequate selection of the projection planes makes it possible to simplify the controller without deteriorating the tip motion response and to obtain the desired motion in the null space of the manipulator. This is one of the remarkable features of the proposed approach. The validity of the proposed controller is confirmed by several numerical simulations.

In this paper, a new robust controller is proposed to improve the motion control performance of an autonomous four‐wheel steering vehicle. The vehicle is subject to time‐varying uncertainties caused by parameter perturbation and unmodeled disturbance. First of all, the vehicle motion control problem is modeled as a constraint‐following problem with the goal to drive the vehicle system to follow the given constraints. For safety reasons, inequality constraints are imposed on the lateral displacement of the vehicle. Next, the diffeomorphism mapping method is used to deal with the inequality constraints in the vehicle motion control, and the constrained lateral displacement space is mapped to an unbounded space. On this basis, a new multi‐parameter robust constraint‐following control is designed. Based on Lyapunov stability theory, the approximate constraint tracking performance of the path tracking system is proved. In order to make a trade‐off between the system performance and control cost, a multi‐parameter optimization problem is established, and the optimal robust controller is obtained. Last, the main theoretical results are verified by the Carsim‐Simulink co‐simulations.

This paper studies the precision motion control of high-speed/acceleration linear motors in a commercial gantry which are subject to significant nonlinear cogging forces. A discontinuous projection based desired compensation adaptive robust controller (ARC) is constructed and implemented. Design models consisting of known basis functions with unknown weights are used to approximate various unknown nonlinear forces with model approximation errors being explicitly accounted for in the controller design process. On-line parameter adaptation is then utilized to reduce the effect of various parametric uncertainties while certain robust control laws are synthesized to effectively handle various modeling uncertainties for a guaranteed robust performance. Comparative experimental results obtained on an Anorad commercial gantry with a linear encoder resolution of 0.5 mum and a position measurement resolution of 20 nanometers by external laser interferometers are presented to illustrate the achievable control performance of the proposed control strategy in implementation. Experiments are also performed to explicitly identify the cogging forces via external force sensors. Various comparative experimental results with and without the proposed cogging force compensations are then obtained to validate the effectiveness of the approach in practical applications.

Parallel robots are nowadays used in many high-precision tasks. The dynamics of parallel robots is naturally more complex than the dynamics of serial robots, due to their kinematic structure composed by closed chains. In addition, their current high-precision applications demand the innovation of more effective and robust motion controllers. This has motivated researchers to propose novel and more robust controllers that can perform the motion control tasks of these manipulators. In this article, a two-loop proportional–proportional integral controller for trajectory tracking control of parallel robots is proposed. In the proposed scheme, the gains of the proportional integral control loop are constant, while the gains of the proportional control loop are online tuned by a novel self-organizing fuzzy algorithm. This algorithm generates a performance index of the overall controller based on the past and the current tracking error. Such a performance index is then used to modify some parameters of fuzzy membership functions, which are part of a fuzzy inference engine. This fuzzy engine receives, in turn, the tracking error as input and produces an increment (positive or negative) to the current gain. The stability analysis of the closed-loop system of the proposed controller applied to the model of a parallel manipulator is carried on, which results in the uniform ultimate boundedness of the solutions of the closed-loop system. Moreover, the stability analysis developed for proportional–proportional integral variable gains schemes is valid not only when using a self-organizing fuzzy algorithm for gain-tuning but also with other gain-tuning algorithms, only providing that the produced gains meet the criterion for boundedness of the solutions. Furthermore, the superior performance of the proposed controller is validated by numerical simulations of its application to the model of a planar three-degree-of-freedom parallel robot. The results of numerical simulations of a proportional integral derivative controller and a fuzzy-tuned proportional derivative controller applied to the model of the robot are also obtained for comparison purposes.

This book presents the steps necessary for the application of rotorcraft flight modelling and simulation and analysis in support of certification for compliance with EASA CS-27 and CS-29, SUBPART B—FLIGHT and other flight-related rules (e.g. CS-27/9, Appendix B, Airworthiness Criteria for Helicopter Instrument Flight). The material is presented in the form of a structured ‘Rotorcraft Certification by Simulation’ process, starting from the relevant paragraphs in the Certification Specifications, through a comprehensive description of the assembly of flight simulation requirements, informed by judgements on Influence, Predictability and Credibility, and on into the detailed building of the three major elements of the process; the Flight Simulation Model, the Flight Simulator, and the associated Flight Test Measurement System.

For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision‐making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective.

There are increasing needs of precision pointing and extreme stability for current and future spacecrafts. The James Webb space telescope, terrestrial planet finder, space based laser, space-based interferometer and deep-space laser communication are such examples where the micro-radian pointing and nanometer level of motion stability are required by (Ford, et al, 2005), (Chen, et al, 2004) and (Winthrop, et al 2003). On the other hand, the space systems may contains many vibration sources. A satellite may contain multiple instruments; some of them may use reaction wheels, cryogenic coolers, control moment gyroscopes, solar array drives, stepper motors, and other motion devices. These devices will transmit vibrations. Passive isolation presents a reliable, low cost solution that is effective for attenuating high frequency vibrations, but it is in general not suitable for low frequency vibration isolation, and especially, passive isolation can not provide good trade-off between resonant peak and high frequency attenuation and the trade-off between pointing command keeping and disturbance rejection. (Winthrop, et al 2003) indicates the active vibration control can overcome these limitations. In order to achieve multi-DOF vibration isolation in broadband and precision pointing, the Stewart platform (or hexapod), especially the cubic one, has become one of the most popular approaches as in (Anderson, et al, 2000) and (Thayer, et al, 2002), as shown in Fig.1. The cubic hexapod simplifies the control topologies to allow the decoupled controller designs to be identical for each strut. In order to eliminate the micro dynamics (friction and backlash), flexure joints are generally used as in (Hanieh, 2003). Jet Propulsion Laboratory, Air Force Research Laboratory, Naval Postgraduate School, University of Washington, Free University of Brussels, University of Wyoming, CSA Engineering Inc are very active in this research. Classic control, adaptive control, LQG control, neural control, simple robust control and other control approaches were studied by (Gawronski, et al, 2004), (Joshi, et al, 2005) and (Liu et al, 2008). In this chapter, H∞ and μ controllers are designed for the struts of Stewart platforms, suppressing the overshoot in the neighborhood of resonance frequencies. Then the dynamic model of Stewart isolator is derived, and D-K iteration is used to solve the robust controller, finally, the time domain responses to suppress disturbance are also presented.

In this brief, a novel robust nonlinear motion controller with disturbance observer (DOB) for positioning control of a nonlinear single-input-single-output (SISO) mechanical system is proposed. The controller is designed in a backstepping manner. First, a proportional-integral (PI) controller is designed to stabilize the position error. Consequently, a novel robust nonlinear velocity controller with DOB is designed to stabilize the velocity error. With the help of nonlinear damping terms, the input-to-state stability (ISS) property of the overall nonlinear control system is proven, which leads to a major contribution of construction of a theoretically guaranteed robust nonlinear controller with DOB. The performance of the proposed controller is verified through application to a magnetic levitation system. Comparative studies with an adaptive robust nonlinear controller are also carried out. It is shown that the proposed novel controller while being simple is superior over the adaptive robust nonlinear controller for the experimental setup under study.

Aiming at the characteristics of automobile braking and the performance demands of ABS, the mathematic model is founded. ABS control strategy based on robust and fuzzy logic hybrid control is advanced. The fuzzy and robust control simulation model is founded with Matlab/Simulink for automobile ABS. The model has better robustness, good steady-state characteristics, strong anti-interference ability and high control accuracy. The simulation results compare with a single robust control, fuzzy control and the ideal ABS control. The results show the fuzzy robust control strategy can effectively improve the response of automobile ABS, strengthen the vehicle's ability to adapt to various roads. It also can get the best braking slip ratio and the braking distance.

In this article, a novel robust observer-based nonlinear control approach is proposed for quadrotors with unmodeled dynamics and external disturbances, wherein the tracking errors are restricted effectively. Two nonlinear disturbance observers are proposed to deal with uncertainties in the inner and outer loops, respectively. On this basis, a robust observer-based nonlinear control approach is put forward for quadrotor systems. Specifically, in the outer loop subsystem, a robust observer-based control scheme is proposed, which can reduce unexpected tracking errors for quadrotors effectively. Subsequently, based upon the geometric methods, the coordinate-free attitude controller and the nonlinear disturbance observer are integrated as a robust controller in the inner loop subsystem. Based upon full nonlinear dynamics, the solutions of the closed-loop system are proven to be uniformly ultimately bounded by means of rigorous Lyapunov analysis. A series of comparative experiments consisting of the implementation of a nonlinear proportional-integral-derivative (PID)-type controller, an observer-based sliding mode controller, and the proposed controller are conducted to demonstrate the remarkable performance of the proposed method in terms of higher tracking accuracy and stronger robustness.

Next-generation motion systems are expected to exhibit dominant flexible dynamical behavior. As a result, a dynamic relation between the measured variables and the performance variables is inevitable. The aim of the present paper is i) to develop a control framework to deal with unmeasured performance variables and ii) to implement the proposed methodology on a prototype experimental setup. A system identification for robust inferential control approach is pursued. Indeed, in the case that the performance variables cannot be measured directly, then these can be inferred from the measurements by means of a model. Experimental results i) confirm that prior approaches that aim at an improved response in terms of the measured variables can result in a deteriorated performance and ii) reveal that the proposed framework enables high performance robust inferential control.

This paper presents a robust physics-based motion control system for realtime synthesis of human grasping. Given an object to be grasped, our system automatically computes physics-based motion control that advances the simulation to achieve realistic manipulation with the object. Our solution leverages prerecorded motion data and physics-based simulation for human grasping. We first introduce a data-driven synthesis algorithm that utilizes large sets of prerecorded motion data to generate realistic motions for human grasping. Next, we present an online physics-based motion control algorithm to transform the synthesized kinematic motion into a physically realistic one. In addition, we develop a performance interface for human grasping that allows the user to act out the desired grasping motion in front of a single Kinect camera. We demonstrate the power of our approach by generating physics-based motion control for grasping objects with different properties such as shapes, weights, spatial orientations, and frictions. We show our physics-based motion control for human grasping is robust to external perturbations and changes in physical quantities.