Model based prediction, preview and robust controls in motion control systems

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.

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.

In the path following motion control system, the reference tool path of the machine tool is geometric curves predetermined by applications. The reference motion command for servo control system is generated based on the geometric tool path and the feedrate. The command generation module is called path interpolation. It is a classical problem in the motion control system. However, a clear formula is still missing in the literature. In this paper, path interpolation problem for the generic parametric tool path is formulated clearly in a mathematical way. By examining the parametrization of the tool path, the problem turns out to be an initial value problem for the ordinary differential equation system (ODE). From this formula, traditional interpolation algorithms, like digital differential analyzer (DDA) and digital increment algorithm, are easily derived. Numerical computation algorithms, such as the truncated Taylor expansion algorithm, the Runge-Kutta algorithm and the predictor-corrector algorithm, can be utilized. Considering reliability, accuracy and complexity, all those algorithms are analyzed and compared from three perspectives - theoretical comparison based on numerical analysis, simulation and experiments. Although the Taylor expansion algorithms and the predictor-corrector algorithms are widely used in the literature, the Runge-Kutta algorithms are highly recommended from our analysis and comparison. The Runge-Kutta algorithm of order 4 is recommended, if the computation resource is available.

We propose a robust control system which has robustness for disturbance and can deal with a nonlinear system with partially unknown structure by fusing reinforcement learning and robust control theory. First, we solved an optimal control problem without using unknown part of functions of the system, using neural network and the repetition learning of reinforcement learning algorithm. Second, we built the robust reinforcement learning control system which permits uncertainty and has robustness for disturbance by fusing the idea of H infinity control theory with above system.

For field tasks, it is quite challenged to operate in a complex environment for the underwater robots, especially for those with multiple control surfaces due to different response and gain characteristics. To this end, this paper develops a highly integrated robotic dolphin followed by a robust motion control system. For better maneuverability and fault-tolerant capabilities, a newly-designed robotic dolphin is presented, owning a wide array of sensors and multiple control surfaces, in which passive flukes are particularly applied. On this basis, a robust motion control system is proposed, including a depth controller based on velocity-related allocation strategies and a heading controller based on clearance compensation. In detail, considering the degradation of motion performance caused by passive flukes, a sliding mode controller for gain uncertainty and an allocation-related parameter tuning strategy for inputs response characteristics are designed. Extensive simulations and aquatic experiments are conducted, and the obtained results demonstrate the satisfied maneuverability of the designed prototype and the effectiveness of the proposed methods. This study can lay a foundation for further development of robotic dolphins with a robust motion system to execute complex tasks in the field. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is inspired by the issue of robust motion control system for a newly-designed practical robotic dolphin that possesses a passive tail and redundant control surfaces. The traditional methods are usually susceptible to uncertainties in the passive tail gain, exhibiting degraded control performance. Moreover, control oscillations and slow convergence speed often occur caused by neglecting the characteristics of different control surfaces, including response patterns and clearance. This paper suggests a robust depth controller based on velocity-related allocation strategies and a robust heading controller based on clearance compensation. Specifically, an allocation-related parameter tuning strategy is given by considering inputs response characteristics, including response speed, saturations, and hydrodynamic force variation patterns. To guarantee fine regulations of heading control, a nonlinear disturbance observer (NDOB)-based clearance compensation is proposed. Extensive aquatic experiments on the newly-designed robotic dolphin verified the effectiveness of the proposed methods. It is envisioned that all these presented results can provide valuable engineering practice insights for industry practitioners.

This paper presents a comparison of the motion produced by a flight simulator using conventional washout filters and a novel robust controller. Both approaches are implemented in an off-line Matlab/Simulink simulation which includes a model of an electrically-driven synergistic motion base flight simulator. In order to evaluate the performance of these motion base control approaches, the Boeing 747 airliner has been simulated using a nonlinear model of its dynamics. The motion experienced by the aircraft pilot is compared to that experienced by the simulator pilot, using both control approaches. Additionally, the model used for the simulator motion base is adjusted to represent two different scenarios: a highand a low-fidelity motion system. It is shown that the two control approaches reasonably track the motion of the aircraft, within the limitations of the motion base, but that the robust controller is able to compensate for variations in the fidelity of the motion system.

Since the computed torque or inverse dynamics control techniques are based on a good understanding of the controlled system dynamics and even its environment; the objection for the real-time application is unrealizable for the unknown systems. To tackle this problem, this study developed a robust adaptive fuzzy sliding-mode control system based on the H/sup /spl infin// tracking design technique. The developed control system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is utilized to approximate an ideal controller by the tuning algorithms based on the Lyapunov sense, and the robust controller is designed to achieve H/sup /spl infin// robust tracking performance. To investigate the effectiveness of the developed control system, it is applied to a wing rock motion control system. Simulation results demonstrate that the proposed control system can achieve favorable tracking performances with unknown system dynamics.

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this study, a robust hybrid control system is developed for the antilock braking systems. In this design method, the robust hybrid control system is comprised of a hybrid controller and a robust controller to achieve a desired H/sup /spl infin// control performance. The hybrid controller containing a fuzzy uncertainty observer is the principal controller, and the robust controller is designed to achieve the desired H/sup /spl infin// tracking performance. Finally, simulations are performed to demonstrate the effectiveness of the proposed robust hybrid control system for antilock braking control under various road conditions.

The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.

A systematic and complete method to design robust predictive controllers for unconstrained linear systems is proposed. The synthesis procedure is based on rigorous theoretical foundations, without resorting to approximations or ad hoc design guidelines, yet it remains a viable tool for practical application. A significant feature is that the robust predictive controller retains the servo performance of a nominal predictive controller designed using conventional methods. In addition, the robust predictive controller can be designed to guarantee perfect steady‐state rejection of asymptotically constant disturbances. The robust design method is developed for systems affected by unmodeled dynamics and is based on solving a discrete‐time model‐matching problem. It is shown that the robust controller can be classified legitimately as a predictive controller because it minimizes the same performance functional as the nominal predictive controller. An illustrative design example is given.

The increasing gap between design productivity and chip complexity, and emerging systems-on-a-chip (SoC) have led to the wide utilization of reusable intellectual property (IP) cores. Educators' responsibility is to provide future generations of SoC architects with knowledge necessary for successful design and use of IP cores, and to offer them a system perspective including both hardware and software. One such proposal is to design an accelerometer IP core for advanced control applications such as robust motion control, servomotor or robotic applications. There are a lot of strong demands on motion control systems, with reasonable resolution and accuracy. The performance of such motion control systems can be greatly enhanced by including some form of velocity or acceleration feedback. An accurate and rapid velocity measurement is vital for high-performance acceleration control. The functionality of the system core is coded in Verilog HDL, mapped onto architecture, simulated, synthesized and implemented on FPGA and tested. The performance evaluation and validation of the developed core can also be done by comparison with a software model realized using Matlab Simulink. The velocity estimates of RTL model can be further improved by considering some factors affecting accuracy, resolution and range of velocity measurements. This validated velocity signals are used to compute the acceleration of the moving object using simple differentiation technique without accumulated quantization errors.

Advances in high performance motion control systems by Dongjun Zhang thesis 2009 xiii, 135 p. : ill. ; 30 cm Equipment industry is the most fundamental industry of any modern society. Modern equipment is characterized by its high automation, efficiency,…Read more ›

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.

In this article, robust control schemes are presented for time-varying trajectory tracking control of Wheeled Mobile Robots (WMRs) in the presence of disturbance. First, the robust model predictive control (RMPC) is implemented for the kinematic subsystem by solving the modified optimization problem with the fixed starting point. Second, the robust control is extended for the dynamic subsystem by the Backstepping technique and additional observer. This paper shows how to ensure the stability of the closed-loop tracking error system. The performance of the proposed robust control is verified through simulation studies using Casadi tool.KeywordsModel predictive control (MPC)Wheeled mobile robotic systems (WMRs)Output feedback controlTerminal controllerFeasibility