Joint University Program for Air Transportation Research, 1989-1990

Research conducted during the academic year 1989-90 under the NASA/FAA sponsored Joint University Program for Air Transportation research is discussed. Completed works, status reports and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, aircraft performance, human factors, and expert systems concepts applied to airport operations. An overview of the year's activities for each university is also presented

Error minimising gradients for improving cerebellar model articulation controller performance

In motion control applications where the desired trajectory velocity exceeds an actuator’s maximum velocity limitations, large position errors will occur between the desired and actual trajectory responses. In these situations standard control approaches cannot predict the output saturation of the actuator and thus the associated error summation cannot be minimised.An adaptive feedforward control solution such as the Cerebellar Model Articulation Controller (CMAC) is able to provide an inherent level of prediction for these situations, moving the system output in the direction of the excessive desired velocity before actuator saturation occurs. However the pre-empting level of a CMAC is not adaptive, and thus the optimal point in time to start moving the system output in the direction of the excessive desired velocity remains unsolved. While the CMAC can adaptively minimise an actuator’s position error, the minimisation of the summation of error over time created by the divergence of the desired and actual trajectory responses requires an additional adaptive level of control.This thesis presents an improved method of training CMACs to minimise the summation of error over time created when the desired trajectory velocity exceeds the actuator’s maximum velocity limitations. This improved method called the Error Minimising Gradient Controller (EMGC) is able to adaptively modify a CMAC’s training signal so that the CMAC will start to move the output of the system in the direction of the excessive desired velocity with an optimised pre-empting level.The EMGC was originally created to minimise the loss of linguistic information conveyed through an actuated series of concatenated hand sign gestures reproducing deafblind sign language. The EMGC concept however is able to be implemented on any system where the error summation associated with excessive desired velocities needs to be minimised, with the EMGC producing an improved output approximation over using a CMAC alone.In this thesis, the EMGC was tested and benchmarked against a feedforward / feedback combined controller using a CMAC and PID controller. The EMGC was tested on an air-muscle actuator for a variety of situations comprising of a position discontinuity in a continuous desired trajectory. Tested situations included various discontinuity magnitudes together with varying approach and departure gradient profiles.Testing demonstrated that the addition of an EMGC can reduce a situation’s error summation magnitude if the base CMAC controller has not already provided a prior enough pre-empting output in the direction of the situation. The addition of an EMGC to a CMAC produces an improved approximation of reproduced motion trajectories, not only minimising position error for a single sampling instance, but also over time for periodic signals

Mechanisms of motor learning: by humans, for robots

Whenever we perform a movement and interact with objects in our environment, our central nervous system (CNS) adapts and controls the redundant system of muscles actuating our limbs to produce suitable forces and impedance for the interaction. As modern robots are increasingly used to interact with objects, humans and other robots, they too require to continuously adapt the interaction forces and impedance to the situation. This thesis investigated the motor mechanisms in humans through a series of technical developments and experiments, and utilized the result to implement biomimetic motor behaviours on a robot. Original tools were first developed, which enabled two novel motor imaging experiments using functional magnetic resonance imaging (fMRI). The first experiment investigated the neural correlates of force and impedance control to understand the control structure employed by the human brain. The second experiment developed a regressor free technique to detect dynamic changes in brain activations during learning, and applied this technique to investigate changes in neural activity during adaptation to force fields and visuomotor rotations. In parallel, a psychophysical experiment investigated motor optimization in humans in a task characterized by multiple error-effort optima. Finally a computational model derived from some of these results was implemented to exhibit human like control and adaptation of force, impedance and movement trajectory in a robot

A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces

Computational modelling of the human motor control system: Nonlinear enhancement of the adaptive model theory through simulation and experiment

Adaptive Model Theory (AMT) proposes that the brain forms and adaptively maintains inverse models of the world around it for adaptive feedforward control. This leading motor control theory unites principles of neurobiology, psychology and engineering. A modified version of AMT was developed with the capacity to control nonlinear systems, to predict signals with nonlinear statistical characteristics, and to perform simultaneous feedback and feedforward adaptive control. The modified version is called nonlinear Adaptive Model Theory or nAMT. An experimental study was also performed investigating inverse model formation in the human motor control system, the results of which were then compared with the nAMT model. A nonlinear dynamic system identification method was developed for nAMT to replace the linear structures employed by AMT. This method employs a neurobiologically-inspired locally-recurrent neural-network structure. A multi-layer adaptation algorithm was also developed specifically for this structure. Nonlinear AutoRegressive Moving-Average (NARMA) adaptive predictor structures replace the linear Moving Average (MA) predictor circuits used in AMT. Adaptive feedback control is augmented using a nonlinear dynamic forward model observer to improve the quality of the estimated response signal. Nonlinear dynamic inverse models are formed by placing the forward model in an internal feedback loop in which the gain function is adjusted to maintain stability. The internal inverse model motor-control hypothesis was tested experimentally in a study looking at human open-loop performance in a tracking task. The study was aimed at directly demonstrating the formation of an internal inverse model of a novel visuomotor relationship for feedforward control in the brain. The study involved 20 normal adult subjects who performed a pursuit random tracking task with a steering wheel for input. During learning the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Results showed a transfer of learning from the unblanked runs to the blanked runs for a static nonlinear system (14% median improvement between first 4 and last 4 runs, p = .001) thereby demonstrating adaptive feedforward control in the nervous system. No such transfer was observed for a dynamic linear system, indicating a dominant adaptive feedback control component. The observed open-loop responses showed a high-pass frequency response which could not be explained using traditional control-systems motor control models. Experimental results were compared with simulated results from the nAMT model. Results from the experimental study were used to verify and tune the computational model. The resulting simulations produced effects that mirrored the closed- and openloop characteristics of the experimental response trajectories. This supports the claim that an internal feedback loop is used for the inversion of external systems in the human brain. Other control-systems models (both AMT and feedback-error learning) would require substantial ad hoc modification to reproduce the observed disparity between closed- and open-loop results. In contrast, nAMT naturally reproduced the effect as a consequence of its novel nonlinear inversion method. In nAMT an inverse model is formed by embedding a forward model in an internal feedback loop incorporating a low derivative gain. The derivative loop-gain caused the inverse model to be relatively inaccurate at low frequencies, for which the feedback control loop was adequate, but to be increasingly accurate at higher frequencies. Maintenance of the loop-gain at the lowest possible levels maximizes the internal stability of the inverse. The simulation work confirmed that the nAMT model is capable of reproducing human behaviour under a wide range of conditions

Development of Self-Learning Type-2 Fuzzy Systems for System Identification and Control of Autonomous Systems

Modelling and control of dynamic systems are faced by multiple technical challenges, mainly due to the nature of uncertain complex, nonlinear, and time-varying systems. Traditional modelling techniques require a complete understanding of system dynamics and obtaining comprehensive mathematical models is not always achievable due to limited knowledge of the systems as well as the presence of multiple uncertainties in the environment. As universal approximators, fuzzy logic systems (FLSs), neural networks (NNs) and neuro-fuzzy systems have proved to be successful computational tools for representing the behaviour of complex dynamical systems. Moreover, FLSs, NNs and learning-based techniques have been gaining popularity for controlling complex, ill-defined, nonlinear, and time-varying systems in the face of uncertainties. However, fuzzy rules derived by experts can be too ad-hoc, and the performance is less than optimum. In other words, generating fuzzy rules and membership functions in fuzzy systems is a potential challenge especially for systems with many variables. Moreover, under the umbrella of FLSs, although type-1 fuzzy logic control systems (T1-FLCs) have been applied to control various complex nonlinear systems, they have limited capability to handle uncertainties. Aiming to accommodate uncertainties, type-2 fuzzy logic control systems (T2-FLCs) were established. This thesis aims to address the shortcomings of existing fuzzy techniques by utilisation of type-2 FLCs with novel adaptive capabilities. The first contribution of this thesis is a novel online system identification technique by means of a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) to accommodate the footprint-of-uncertainties (FoUs). This development is meant to specifically address the shortcomings of type-1 fuzzy systems in capturing the footprint-of-uncertainties such as mechanical wear, rotor damage, battery drain and sensor and actuator faults. Unlike previous type-2 TS fuzzy models, the proposed method constructs two fuzzifiers (upper and lower) and two regression coefficients in the consequent part to handle uncertainties. The weighted least square method is employed to compute the regression coefficients. The proposed method is validated using two benchmarks, namely, real flight test data of a quadcopter drone and Mackey-Glass time series data. The algorithm has the capability to model uncertainties (e.g., noisy dataset). The second contribution of this thesis is the development of a novel self-adaptive interval type-2 fuzzy controller named the SAF2C for controlling multi-input multi-output (MIMO) nonlinear systems. The adaptation law is derived using sliding mode control (SMC) theory to reduce the computation time so that the learning process can be expedited by 80% compared to separate single-input single-output (SISO) controllers. The system employs the `Enhanced Iterative Algorithm with Stop Condition' (EIASC) type-reduction method, which is more computationally efficient than the `Karnik-Mendel' type-reduction algorithm. The stability of the SAF2C is proven using the Lyapunov technique. To ensure the applicability of the proposed control scheme, SAF2C is implemented to control several dynamical systems, including a simulated MIMO hexacopter unmanned aerial vehicle (UAV) in the face of external disturbance and parameter variations. The ability of SAF2C to filter the measurement noise is demonstrated, where significant improvement is obtained using the proposed controller in the face of measurement noise. Also, the proposed closed-loop control system is applied to control other benchmark dynamic systems (e.g., a simulated autonomous underwater vehicle and inverted pendulum on a cart system) demonstrating high accuracy and robustness to variations in system parameters and external disturbance. Another contribution of this thesis is a novel stand-alone enhanced self-adaptive interval type-2 fuzzy controller named the ESAF2C algorithm, whose type-2 fuzzy parameters are tuned online using the SMC theory. This way, we expect to design a computationally efficient adaptive Type-2 fuzzy system, suitable for real-time applications by introducing the EIASC type-reducer. The proposed technique is applied on a quadcopter UAV (QUAV), where extensive simulations and real-time flight tests for a hovering QUAV under wind disturbances are also conducted to validate the efficacy of the ESAF2C. Specifically, the control performance is investigated in the face of external wind gust disturbances, generated using an industrial fan. Stability analysis of the ESAF2C control system is investigated using the Lyapunov theory. Yet another contribution of this thesis is the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). T2-EFCS has two phases, namely, the structure learning and the parameters learning. The structure of T2-EFCS does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one rule. The rules are then added and pruned in an online fashion to achieve the desired set-point. The proposed technique is applied to control an unmanned ground vehicle (UGV) in the presence of multiple external disturbances demonstrating the robustness of the proposed control systems. The proposed approach turns out to be computationally efficient as the system employs fewer fuzzy parameters while maintaining superior control performance

Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks

The values of a given manipulator's dynamics coefficients need to be accurately identified in order to employ model-based algorithms in the control of its motion. This thesis details the development of a novel form of adaptive network which is capable of accurately learning the coefficients of systems, such as manipulator inverse dynamics, where the algebraic form is known but the coefficients' values are not. Empirical motion data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear Combiner (CSLC) network developed, and the coefficients of their inverse dynamics identified. The resultant precision of control is shown to be superior to that achieved from employing dynamics coefficients derived from direct measurement. As part of the development of the CSLC network, the process of network learning is examined. This analysis reveals that current network architectures for processing analogue output systems with high input order are highly unlikely to produce solutions that are good estimates throughout the entire problem space. In contrast, the CSLC network is shown to generalise intrinsically as a result of its structure, whilst its training is greatly simplified by the presence of only one minima in the network's error hypersurface. Furthermore, a fine-tuning algorithm for network training is presented which takes advantage of the CSLC network's single adaptive layer structure and does not rely upon gradient descent of the network error hypersurface, which commonly slows the later stages of network training