Multiple Axes Synchronizing. System under Adaptive Feedforward Control

This thesis investigates how insects control movement using adaptive feedforward control and efference copies, and how these principles can be applied to robotic systems. A phenomenological control architecture—the fully-separable-degrees-of-freedom (FSDoF) controller—is developed to formalise efference copies in a control-theoretic context. The FSDoF architecture is used in simulation to examine control advantages of efference copy-based systems. Specifically, it enables sensors to remain within their operating range, thereby optimising sensitivity to external disturbances, and is particularly effective in systems with time delays and significant sensor noise. When combined with high-pass-filter-like sensors, the FSDoF controller also exhibits a unique robustness to changes in sensor gain and bandwidth. Furthermore, a method is introduced to design FSDoF controllers for multi-input multi-output (MIMO) systems with multiple, distinct time delays. This work formalises the efference copy hypothesis in insects from an engineering perspective, offering a functional explanation for their evolutionary advantages, and enabling the generation of experimentally testable hypotheses. One such hypothesis is that insect feedforward controllers adapt to changes in system dynamics. A series of free-flight experiments in Drosophila melanogaster supports this idea. Following unilateral wing damage, flies exhibited increased flight speeds in darkness compared to intact controls. When flying in light, with visual feedback, their speed was reduced to near-control levels. Altogether, the results indicate that the adaptation was not solely due to changes in mechanosensory feedback, but suggest adaptive feedforward control. Building on these insights, an implicit adaptive feedforward (IAFF) control strategy is proposed and implemented on a quadrotor platform. This controller uses adaptive sensitivity derivatives, so that it could, in theory, cope with an inversion of the system dynamics. IAFF control improved the reference tracking in a Parrot Mambo mini-drone within minutes of operation, starting from a randomised feedforward controller.

A multi-input multi-output adaptive feed-forward controller for vibration alleviation on a large blended wing body airliner

The proposed investigation will lead to the development of adaptive truss technology for use in future aircraft and satellite design. The adaptive scheme is of feedforward type and essentially generates a secondary behavior using actuators to control host structure to meet the desired requirements. Three points are focused in this paper First, an adaptive controller is designed based on the filtered-X Least Mean Square (X-LMS) algorithm. Secondly, the control algorithm is developed under Matlab and Labview for simulation and real-time adaptive control. Forthermore, in the work, an existing truss structure with three sensors and three motor actuators has been used to examine the control system. The research results show us that the controller is feasible and suitable to die on-line control of adaptive taiss described as a slowing varying system. Introduction A structure in space may require changes to initially establish the desired configuration and characteristics by self-turning to adapt the variable environment. Adaptive trusses are those that incorporate actuators and sensors that are highly integrated into the host structures. These structures are capable of altering both their mechanical states (position or velocity) and their mechanical characteristics (stiffness or damping), according to time-varying environment. Once the precision structure is in the operational position, its static position must be precisely located. The desired static position can be attained by adjusting active members. The shape control of truss structures has been studied # Graduate Student (zlizhuo@hippo.nwpu.edu.cn) # Professor of Aero Engine Department ## Engineer, Department of Aerospace Department Copyright @ 2001 by the American Institute of the Aeronautics and Astronautics, Inc. All rights reserved. by several investigators in the decades. For example, optimization of the support truss was formulated so as to minimize die required effort to correct the shape, while improving the performance of the antenna. Minimization of the effort required for the shape correction was implemented in the form of the minimizing the maximum actuator input. Bruno and Salama ' have achieved significant improvements in static accuracy of the Precision Segmented Reflector (PSR) using only limited number of actuators. All of these investigations showed the feasibility of using active members to adaptively alter the shape of the structure and/or its behavior to other states. In this paper, a truss controller is developed for shape control by using X-LMS and Fuzzy Logic Control. Under Matlab and Labview, the computer simulation and physical experiments are used to examine the characteristics of the controller according to a truss structure model with 3 sensors, 3 actuators and 3 exciters. The primary results are supplied and will be discussed in the following sections. Adaptive Feedforward Controller The increase in computational capabilities of modern digital signal processing system has permitted the real-time implementation (at high bandwidths) of adaptive filter algorithms developed in the 1960s and 1970s. These algorithms provide a control system that can adapt to changes in the plant H (z) and provide outstanding disturbance rejection for certain classes of inputs. In the control scheme, an adaptive feedforward controller has been designed based on X-LMS algorithm. ' In the development of the LMS algorithm the output of the LMS filter is assumed to be the convolution of the input data sequence to the filter with the LMS filter coefficients. So, the control filter is an FIR filter. A block diagram of the controller is shown in Fig. 1. In the schematic diagram, the error output sensors consist of M outputs arranged in a vector, E(k), of length M as follows: (1) 1 American Institute of Aeronautics and Astronautics (c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. me uuimui miei^ consist or an array ot P actuators Y(k] = H(k} * U(k] (9) (or control inputs), each of order N. These aggregate control weights are arranged in one vector: ^^ is ^ M x P matrix of me control to output impulse response functiopns. The matrix H(k) is n of the form:

An Adaptive Feedforward Control Algorithm for Computer Control of Wastewater Neutralization

Adaptive traffic control systems continuously monitor traffic conditions on an arterial and adjust traffic signal timings to accommodate variation in traffic volume and minimize stops and delays. This paper describes currently practical adaptive control systems which have been used in the United States and worldwide, which include Adaptive Control System-Lite (ACS-Lite), Optimization Policies for Adaptive Control (OPAC), Real-Time Hierarchical Optimized Distributed and Effective System (RHODES), Sydney Coordinated Adaptive Traffic System (SCATS) and Split Cycle Offset Optimization (SCOOT). The comparisons of the benefits from past experiences, cost and hardware requirements will help the traffic engineers and decision makers a guideline to select the optimized adaptive control system based on research objectives.

A unique approach to adaptive feedforward control, namely the higher-harmonic time-averaged gradient descent algorithm (H-TAG), is presented. As opposed to the more common adaptive feedforward control method based on the filtered-x LMS algorithm, H-TAG does not require a measure of the control to error sensor transfer function for the system. Thus if the system dynamics change during the control process, H-TAG will simply adapt to the new optimal control configuration, rendering the approach model insensitive. Until now, work presented on the H-TAG algorithm has been limited to a single-input, single-output theoretical simulation. This paper describes the additional considerations involved in implementing the algorithm on an actual complex laboratory model (a de Havilland Dash 7 turbo-prop fuselage) that has multiple inputs and multiple outputs. Results from this preliminary experimental study suggest that the H-TAG algorithm is a viable alternative for adaptive feedforward control of systems with slowly time-varying parameters.

Inertial stabilization systems are designed to maintain the angular orientation of an optical system or other device in inertial space. Applications include surveillance, target tracking, flight control, weapon pointing, navigation, communication and others. Typically, some variation of a closed loop control system is employed which uses feedback from a gyro. The object of this paper is to consider the potential benefits and some approaches to adaptive control algorithms. Ideally, an adaptive control system would adjust itself to achieve "optimal performance" with respect to some criteria in the presence of changing plant characteristics, changing noise characteristics and a changing disturbance environment. Potentially, such a design could provide robust control for a variety of system configurations, mission conditions and applications resulting in significant reductions in both development and production costs as well as improved performance. First, the general stabilization problem and some conventional control system designs are briefly reviewed. Then a variety of adaptive techniques are surveyed. Finally, the current status and results of a joint research project between Texas Instruments and the University of Texas at Arlington whose goal is to develop an adaptive stabilization control system is discussed.

The structure and performance requirements for active vibration isolation control systems motivate the use of adaptive control. This is especially true for spacecraft platforms subject to uncertainties inherent in on-orbit operation. This paper is an initial investigation into adaptive control strategies and algorithms that may have application to isolation on spacecraft platforms. Analysis of the algorithms, numerical simulation, and laboratory test data are used to evaluate adaptive feedforward control. Of particular interest are the performance characteristics and limitations of the filtered-x LMS (FXLMS) algorithm. The augmented error algorithm and combination feedback/feedforward control are two means investigated to extend the capabilities of FXLMS by desensitizing the algorithm to the specific dynamics of the plant. Several experiments were conducted on a laboratory testbed which serves as the prototype for a planned active vibration isolation flight demonstration.

Parametric study of nonlinear adaptive control algorithm with magneto-rheological suspension systems

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

Usage of frequency based error correction methods for repetitive disturbances becomes popular in data storage devices that use a rotating mechanical power plant. Two distinct disturbance cancellation methods, disturbance observer (DOB) and adaptive feed forward control (AFC) are the most popular approaches used for current drive servo integration. DOB reduces error using a predetermined function that is calculated thorough a complicated process. Generally the response to disturbance and initiation of control action of DOB is relatively fast even if the amount of error reduction is limited. Meanwhile the error elimination capability of AFC for the repetitive disturbance is normally better than DOB as it utilizes a Fourier coefficients updating feature that supports an enhanced fine adaptation of a certain frequency disturbance. Of course, AFC suffers for disturbances that have neighboring close frequencies due to the inherent fundamental frequency dependencies of AFC formulation. In the present work, an attempt for combining benefits of AFC and DOB is made. This might enables control action not only to generate fast response to disturbance but also perform an accurate error rejection. This feature also provides to the closed loop system a frequency selection for enabling a strategic selective error rejection. The proposed method named Hybrid Disturbance Observer (HDOB) is compared to the traditional AFC and DOB.

We consider the problem of rejection of a sinusoidal disturbance of unknown frequency, phase and magnitude acting on an uncertain internally stable SISO linear system. We present a solution that extends our previous work on adaptive feedforward control of uncertain systems by disposing of the need to know the frequency of the harmonic disturbance. The proposed methodology reposes upon a switching-based combination of an adaptive feedforward control algorithm and a deadbeat frequency estimator. The method accounts for the presence of bounded sensor noise as well as imprecise frequency estimation; it is shown that the regulation error is bounded by a function of the norm of the noise that depends on the choice of the controller and the estimator gains.

SummaryIn this study, we present a reinforcement learning (RL)‐based flight control system design method to improve the transient response performance of a closed‐loop reference model (CRM) adaptive control system. The methodology, known as RL‐CRM, relies on the generation of a dynamic adaption strategy by implementing RL on the variable factor in the feedback path gain matrix of the reference model. An actor‐critic RL agent is designed using the performance‐driven reward functions and tracking error observations from the environment. In the training phase, a deep deterministic policy gradient algorithm is utilized to learn the time‐varying adaptation strategy of the design parameter in the reference model feedback gain matrix. The proposed control structure provides the possibility to learn numerous adaptation strategies across a wide range of flight and vehicle conditions instead of being driven by high‐fidelity simulators or flight testing and real flight operations. The performance of the proposed system was evaluated on an identified and verified mathematical model of an agile quadrotor platform. Monte‐Carlo simulations and worst case analysis were also performed over a benchmark helicopter example model. In comparison to the classical model reference adaptive control and CRM‐adaptive control system designs, the proposed RL‐CRM adaptive flight control system design improves the transient response performance on all associated metrics and provides the capability to operate over a wide range of parametric uncertainties.

In this paper properties of adaptive algorithms utilising ideas of stochastic gradient search optimisation applied to noise reduction are discussed. A focus on their average rate of convergence as well as on the obtained noise reduction is given. The presented discussion is based on a simulation case study devoted to noise reduction using a feedforward adaptive active noise control system.

Presents an approach to the design and real-time implementation of an adaptive controller for a robotic manipulator based on digital signal processors. The Texas Instruments DSP (TMS320C31) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for robotic manipulators. In the proposed scheme, adaptation laws are derived from the direct model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feedforward and feedback controller and PI-type time-varying auxiliary control elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for an industrial robot with four joints in the joint space and Cartesian space.