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The application of robots in urban search and rescue (USAR) is a domain receiving increasing attention from the scientific community, although it does not yet grant the requirements for field implementation, such as robustness, reliability, and safety. In this work, we propose a multi-robots coordination system based on the supervisory control theory (SCMCS) divided into two layers: reactive and deliberative. While the reactive layer assures that each robot attains to the critical USAR requirements in face of unexpected events by means of a maximally permissive and nonblocking supervisory control, the deliberative layer coordinates the multi-robots system exploiting heuristics to perform the missions with efficiency. The formal modeling and synthesis of supervisors are applied to a case study based on the international USAR agency procedures. The SCMCS architecture is implemented in a simulated scenario composed of two ground robots and two aerial robots executing USAR missions in a post-disaster scene subject to unexpected events such as robot failures, victims and dangers detection and battery losses. The simulation results show that SCMCS is capable of efficiently coordinating the tasks assigned to multiple robots while guaranteeing critical requirements even in face of the uncertainties of robots and environments in USAR disasters. [ABSTRACT FROM AUTHOR]

In the last decade Internet of Things (IoT) emerged as a concept for intelligent, flexible, autonomous, and distributed system while IoT-based devices have highlighted for agent communication, data analysis, and decision-making tasks. Integrating high-level behavioral and low-level control has capital significance for their implementation and is still a challenge. In the field of high-level behavior, Petri nets are widely applied for modeling, simulating, and analyzing concurrent, discrete-event and knowledge-based systems, furthermore even models are used as basis for control algorithm, direct controlling devices' behavioral with high-level Petri nets is little diffused. This paper presents a control architecture used to integrate models in high-level Petri nets with IoT-based devices. Operational tests were provided using CPN Tools and micro-controlled physical devices. A study with a mobile robot is shown and results indicate the reasonability and applicability of this proposed approach in IoT-based devices. [ABSTRACT FROM AUTHOR]

In discrete event systems, it is often convenient and possible to observe directly whether or not the system is in a subset of the state space, typically after some delay, even if some event occurrences leading to the current state were not observed. We model supervisory control with the delayed observations of events and states and investigate the existence of a supervisor to obtain a given desired language accordingly. An existence verifier with polynomial run time is presented. [ABSTRACT FROM AUTHOR]

Product rate and grade transitions are two significant operations undertaken by process plants to operate in uncertain environments. Rate transitions, typically dictated by economic considerations, are often implemented in open loop via a throughput manipulator (TPM). The absence of feedback of product rate to manipulate the TPM results in an offset during rate/grade transitions as well as in presence of feed-side disturbances. In this work, we explore use of a simple PI controller based supervisory control layer that guides the plantwide regulatory layer to achieve product rate targets during grade transition or in the presence of feed grade disturbances. While such a layer cannot incorporate optimal operation and constraint handling explicitly as in model predictive control, it obviates the need for advanced process control elements that medium-to-small scale industries can ill-afford. The PI based supervisory-regulatory control structure is illustrated using simulated case studies on a prototypical process for diacetone alcohol production comprising of a reactor followed by two distillation columns and a recycle. [Display omitted] • Managing rate and grade transitions using a PI controller based supervisory layer. • PI supervisory layer augmented with feedforward for upstream feed related disturbances. • PI supervisory-regulatory control strategy demonstrated for diacetone alcohol production. • Location of the throughput manipulator plays important role in closed-loop performance. [ABSTRACT FROM AUTHOR]

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants' thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner's evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340–497 or 22%–31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint. • Field implementation of model predictive control for residential air conditioning. • Development of scalable methods for data-driven modeling of humidity effects. • Comparison of practical impacts of humidity model fidelity. • Model fidelity matters less for energy efficiency than for limiting peak demand. • Estimated year-round savings of $340-497 or 22%–31% of heating and cooling energy cost. [ABSTRACT FROM AUTHOR]

This paper proposes an iterative control policy to design a less restrictive and admissible Petri net (PN) controller for a Discrete Event System (DES) with uncontrollable transitions, when a maximally permissive controller is not obtains by. This paper exploits a structural supervisory control method, avoiding reachability graph. This exciting method addresses the controllability condition of the desired functioning PN to define it as General Mutual Exclusion constraints (GMEC), which leads to design place invariant-based controller. The controller is non-admissible if one control place is connected to uncontrollable transition. To obtain an admissible PN controller, if such a controller exists, authors propose constraints transformation, which is computationally complex, while the controller arcs displacement approach is unsystematic. Based on it, we develop the idea to iterate the structural supervisory control method, to ensure that control place is connected to controllable transition. Through this, it was found that the displacement of arcs is systematic and the controller is less restrictive and admissible, namely the supreme controller. The results indicate that the linear constraints are never violated through the firing of uncontrollable transitions. The finding of this work may serve to evaluate the structural optimality of the controller, in order to perform it practically. [ABSTRACT FROM AUTHOR]

This paper develops multiple fixed model blending based adaptive parameter identification schemes for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification schemes are proven to be asymptotically stable for uncertain linear time-invariant (LTI) MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification schemes, a linear-quadratic (LQ) optimal multiple-model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is proven to be asymptotically stable for LTI MIMO systems and applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability, effectiveness, and comparison of the proposed MMAC scheme. [ABSTRACT FROM AUTHOR]

This article presents a supervisory model predictive control system to track the desired speed profile and simultaneously prevent the wheels from slipping in acceleration mode of electrical trains. The proposed control strategy employs field-oriented control (FOC) to control the angular speed of the wheel. Model predictive control (MPC) is used to control the longitudinal velocity of the train to track the desired speed profile and prevent the wheels from slipping by generating the desired angular velocity for the FOC. Since, it is not possible to control the longitudinal velocity and slip ratio independently, a fuzzy supervisor system is proposed to control the train dynamics at the appropriate operating point. A method is presented to estimate train longitudinal velocity and the adhesion coefficient between the wheels and rail surface. These components are vital to implement the proposed method in a real train control system. The closed loop stability of the control system has been studied. Simulations were run under different friction coefficients corresponding to real train parameters to verify the effectiveness of the proposed re-adhesion control system. The simulation results have been compared with the results of other researches to show the feasibility and validity of the presented approach. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Sleep restriction may lead to decreased performance and increased accidents and errors. SPHERES, a small satellite testbed, was used to examine the effects of sleep restriction and situation awareness (SA) aids on a simulation of satellite operations. METHODS: Subjects {N = 8) were trained on SPHERES, then, in a randomized order cross-over design, had 3 d of sufficient sleep (SS) or 3 d of sleep restriction (SR) before a testing session. Subjects controlled two SPHERES satellites in a space debris avoidance scenario. Dependent measures included survival time, area covered by the satellites, and satellite motion perception. RESULS: There were significant interaction effects of sleep protocol Order (SS or SR first) and sleep Condition (SS or SR) on survival time and area covered. Post hoc tests showed longer survival time for the second testing session if the Order was SS first (Mean = 56.1 s, Median = 44.0 s) as compared to SR first (Mean = 42.7 s, Median = 33.5 s). SS-first subjects received benefit from added SA cues of the augmented display in perceiving the satellite motion. DISCUSSION: These data support that learning in a well-rested state may support development of appropriate strategies for better performance. Subjects that were SS during the first session were better able to use added SA cues provided by the augmentation and may have then developed a better mental model of the task and the system. This pilot study suggests that training guidelines for operating multiple robotic assets should permit appropriate rest before and after training to assist in mental model development and task performance. [ABSTRACT FROM AUTHOR]

Highlights • Step length adaption while using a powered ankle prosthesis. • ANFIS with multisensory approach for prediction of step length. • Merging of prediction algorithm with inverse pendulum model of biped walking. • Simplicity, good accuracy and robustness. Abstract In daily-life activities, it becomes essential to modulate the walking speed, when an amputee wishes to walk outdoor wearing his/her powered prosthesis. Different walking speeds can be achieved by varying the step length or stride length, and ankle torque requirement during push-off varies significantly with the desired step length. Thus, in order to mimic the natural gait for varying step lengths, it is essential to develop a control algorithm that can predict the amputee’s desired step length and modulate the ankle torque of a powered ankle prosthesis, accordingly. Therefore, in this study, a control scheme has been proposed to solve the said purpose. The prediction of step length is achieved with the help of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and multisensory data fusion. Experiments are carried out on walking of four healthy adults and a large amount of data are collected to train the ANFIS. After the training is over, the trained fuzzy inference system is used to evaluate the efficiency of the algorithm for a set of test data. Finally, the predicted step length value is used to calculate the required force during push-off (where the prosthesis has to be put) with the help of biped walking dynamics. Thus, the novelty of this study lies with the proposal of a new strategy for deciding adaptive step length and control algorithm. The key features of this algorithm include its simplicity, good prediction accuracy, ability to tackle uncertainty or imprecision and fast response. [ABSTRACT FROM AUTHOR]

Utilizing of supervisory control theory on the real systems in many modeling tools such as Petri Net (PN) becomes challenging in recent years due to the significant states in the automata models or uncontrollable events. The uncontrollable events initiate the forbidden states which might be removed by employing some linear constraints. Although there are many methods which have been proposed to reduce these constraints, enforcing them to a large-scale system is very difficult and complicated. This paper proposes a new method for controller synthesis based on PN modeling. In this approach, the original PN model is broken down into some smaller models in which the computational cost reduces significantly. Using this method, it is easy to reduce and enforce the constraints to a Petri net model. The appropriate results of our proposed method on the PN models denote worthy controller synthesis for the large scale systems. [ABSTRACT FROM AUTHOR]

Considered usage of Honeywell C300 and Safety Manager industrial controllers along with HPM controllers designed for process control and production safety (Fire&Gas Systems, Emergency Shutdown Systems (ESD), Safety Interlocks, etc.) without applying significant changes in existing plant units’ hardware (input/output cards, network structures, etc.) and maintaining the completeness of data transmission between new and old local equipment room and central control room as well. On the example of complex of Zakum fields it is described the application of the idea of openness of the interfaces for both newly constructed and already operating enterprises, and enterprise integration with other industrial clusters with opportunity to output information into the field of cloud intelligence, and also is highlighted the aspect of further modernization of the complex of fields in the intention for realization of the idea of “digital field”. [ABSTRACT FROM AUTHOR]

This paper proposes an indirect adaptive fuzzy neural network (FNN) controller with state observer and supervisory controller for a class of uncertain nonlinear dynamic time delay systems. First, the approximate function of unknown time delay system is inferred by the adaptive time delay FNN system. Next, a state observer is designed to estimate the unknown system states and the indirect adaptive fuzzy controller is constructed. Finally, the closed loop controller is obtained by incorporating the supervisory controller with the indirect adaptive fuzzy controller. Therefore, if the system tends to unstable, i.e., error dynamics is larger than a prescribed constraint which is determined by designer, the supervisory controller will activate to force the state to be stable. The free parameters of the indirect adaptive FNN controller can be tuned online by observer-based output feedback control law and adaptive laws by means of Lyapunov stability criterion. The resulting simulation example shows that the performance of nonlinear time delay chaotic system is fully tracking the reference trajectory. Meanwhile, simulation results show that the adaptive control effort of the proposed control scheme is much less due to the assist of the supervisory controller. [ABSTRACT FROM AUTHOR]

• Construct the observable state transition structure matrix of a given system. • Propose a lower-space-complexity algorithm to state estimation. • Present the sufficient and necessary condition for an opaque (controlled) system. In this study, the current-state opacity verification and enforcement problems in the context of partially-observed discrete event systems are investigated based on an algebraic state space approach. Current-state opacity is defined as a measure of whether a system can protect current vital information from exposure. If given systems are not current-state opaque, it is necessary to synthesize a supervisor to restrict systems' behavior and enforce the property. In this paper, the algebraic-state-space-based approach to these problems provides a novel perspective to the literature. Firstly, an equivalent algebraic structure of a partially-observed finite automaton is established with the help of the semi-tensor product, where the complexity of an observable structure matrix is O (| X | 2 | E o |) (X is the set of states and E o is the set of observable events). And then, using the breadth-first search method, an algorithm to ascertain the state estimate is proposed based on the observable structure matrix, the complexity of which is O (| X | 3 | E o |). For the verification of current-state opacity, it is carried out by determining whether or not there exists a leaking state estimate in the set of secret-related state estimates. In the worst case, this process would be done in O (2 | X |). For the enforcement of opacity, the algebraic expression of a controlled system is also constructed, under which, the breadth-first search approach can still be applied to ascertain state estimates under each control profile. How to obtain all permissive control profiles is summarized as Algorithm 2, the complexity of which is O (2 | E | × | X | 3 × | E o | + 2 | X |). [ABSTRACT FROM AUTHOR]

The deepening impact of global warming has stimulated numerous researchers to come up with alternative energy sources and transportation facilities that create fewer emissions. DC distribution systems are attractive candidates for adapting high penetration of distributed generation and electric vehicles. Accordingly, this work proposes a distributed power management scheme to enhance the reliability and economic performance of isolated dc microgrids. In the presented approach, the dc microgrid is assumed to contain both renewable and nonrenewable generating units. At lower loading conditions, the proposed scheme stimulates a power-sharing strategy among renewable sources to increase system reliability, whereas nonrenewable sources are kept operating at their lower power limits. Alternatively, renewable sources are allowed to operate at their maximum power limits while maintaining optimum economic operation among nonrenewable sources to meet higher loading conditions. To achieve these operational criteria, both renewable and nonrenewable DG units apply a consensus-based algorithm in a supervisory control level. Each DG unit exchanges information with its neighbors, thereby locally updating the no-load voltage setting of its primary control according to system voltage sensitivity analysis. The incorporation of DG droop-based primary control renders the proposed algorithms fully distributed with a reduced number of agents. The proposed algorithm has the additional advantage of restoring system voltage to its nominal value. The stability and convergence of the algorithm are analytically addressed, and real-time OPAL-RT simulations are performed in a hardware-in-the-loop (HIL) application to verify its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we improve the previous work by considering that a control place can have multiple self-loops. Then, two integer linear programming problems (ILPPs) are formulated. Based on the first ILPP, an iterative deadlock control policy is developed, where a control place is computed at each iteration to implement as many marking/transition separation instances (MTSIs) as possible. The second ILPP can find a set of control places to implement all MTSIs and the objective function is used to minimize the number of control places. It is a non-iterative deadlock control strategy since we need to solve the ILPP only once. Both ILPPs can make all legal markings reachable in the controlled system, i.e., the obtained supervisor is behaviorally optimal. Finally, we provide examples to illustrate the proposed approaches. [ABSTRACT FROM AUTHOR]

Although UAV systems are currently controlled by a group of people, in the future, increased automation could allow a single operator to supervise multiple UAVs. Operators will be involved in the mission planning, imagery analysis, weapon control, and contingency interventions. This study examines the architecture and prototype of multiple UAVs supervisory control system. Firstly, the architecture for testing and evaluating human supervisory system controlling multiple UAVs is devised and each sub-system is described in detail. Then a prototype test bed of multiple UAVs supervisory control for demonstrating architecture and adaptive levels of autonomy is built. Finally, with the test bed, the impact of dynamic role allocation on system performance is studied based on quantitative criteria of wait times and operator utilisation. It is shown by simulation that dynamic role allocation can effectively shorten wait times, and eventually improve the system performance. [ABSTRACT FROM AUTHOR]

This paper presents a new control design approach for discrete-event systems described by Input/Output automata. A formal design method guarantees the fulfillment of the specifications for the closed-loop system including the system safety. Necessary and sufficient conditions for the well-posedness of the control loop and the controllability of the plant with respect to the specification are proved. The control of a batch process is used to illustrate the results. [ABSTRACT FROM AUTHOR]

Multi-zone structure is commonly used in small commercial office buildings, retail stores and supermarket. While there is no adjacent partition wall between the zones, the impact of a neighbor zone on the current zone can be approximated and analyzed through the application of virtual walls. It is critical to accurately estimate the virtual wall surface temperature in order to evaluate the model uncertainty and apply improved supervisory control on multiple rooftop air-conditioning units (RTUs). We propose an innovative virtual surface temperature sensor based on system-identification to solve this challenge. The validation of the virtual temperature model is processed by the three validation criteria: goodness of fit (G), mean squared error (MSE) and coefficient of determination (R 2 ) through off-control conditions with data obtained from a building simulation platform. Further, the sensitivity analysis using the on-control three conditions (under-sizing, properly sizing and oversizing condition) is conducted for analyzing and evaluating the performance of this system-identification based virtual sensor. [ABSTRACT FROM AUTHOR]

Abstract: Based on analysis of data flows and functional structure of information – measuring and control systems of mobile robots with supervisory control, proposed a combined approach to designing this systems and multi-camera computer vision. This approach is based on network technologies and our analysis of data flows and suggested functional structure of main types of mobile robots with supervisory control. [Copyright &y& Elsevier]

Abstract: This paper deals with the problem of forbidden states in discrete event systems based on Petri net models. So, a method is presented to prevent the system from entering these states by constructing a small number of generalized mutual exclusion constraints. This goal is achieved by solving three types of Integer Linear Programming problems. The problems are designed to verify the constraints that some of them are related to verifying authorized states and the others are related to avoiding forbidden states. The obtained constraints can be enforced on the system using a small number of control places. Moreover, the number of arcs related to these places is small, and the controller after connecting them is maximally permissive. [Copyright &y& Elsevier]

Nowadays, the lead user method is a state of the art method used to generate breakthrough innovations for the new product development. Since the lead user method was successfully applied in generating simple products for business-to-business environments, the contribution of lead users in a complex product environment is highly controversial. This research adopts this to generate future complex IT-security solutions for business-to-business environments in the energy sector. The combined approach of lead user intelligence and voice of the customer techniques is expected to lower the risk of an unreliable product development and to fasten the lead user method. The empirical findings show an appropriate lead user method fitting small- and medium-sized enterprises (SMEs) restricted resources and requirements of effective research and development processes in a high-technology environment. It further enables SMEs to specify and parameterise future products according to reliable and user verified data within development processes. [ABSTRACT FROM AUTHOR]

We present a software platform developed by Genentech and MathWorks Consulting Group that allows arbitrary MATLAB (MATLAB is a registered trademark of The MathWorks, Inc.) functions to perform supervisory control of process equipment (in this case, fermentors) via the OLE for process control (OPC) communication protocol, under the direction of an industrial automation layer. The software features automated synchronization and deployment of server control code and has been proven to be tolerant of OPC communication interruptions. Since deployment in the spring of 2010, this software has successfully performed supervisory control of more than 700 microbial fermentations in the Genentech pilot plant and has enabled significant reductions in the time required to develop and implement novel control strategies (months reduced to days). The software is available for download at the MathWorks File Exchange Web site at http://www.mathworks.com/matlabcentral/fileexchange/36866. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a novel scheme of incorporating a learning mechanism into previous step supervisory controllers for adaptive fuzzy control is proposed to relax bounds required in the control process. In traditional supervisory adaptive fuzzy control approaches, the use of fuzzy estimators for approximating system functions and a robust supervisory control law are necessary to deal with any possible uncertainties caused in the system. This kind of supervisory controller depends on the robust bounds of system functions so that it can ensure the Lyapunov stability of controlled systems. However, in those approaches, the output may not be able to follow the reference trajectory well if the robust bounds are predicted improperly. In our implementation, CMAC (Cerebellar Model Articulation Controllers) is used as the learning mechanism because of its quick learning capability. Under the Lyapunov stable criterion, the proposed CMAC learning mechanism can improve the output performance and can relax the robust bound limitation so that practical systems can easily be realized. In summary, the proposed approach not only can relax bounds for previous step supervisory controllers in adaptive fuzzy control, but also can significantly improve the control performance of the system. [Copyright &y& Elsevier]

This paper reports the implementation of a supervisory control framework and modular software architecture built around the lightweight communication and marshalling (LCM) publish/subscribe message passing system. In particular, we examine two diverse marine robotics applications using this modular system: (i) the development of an unmanned port security vehicle, a robotic surface platform to support first responders reacting to transportation security incidents in harbor environments, and (ii) the adaptation of a commercial off-the-shelf autonomous underwater vehicle (the Ocean-Server Iver2) for visual feature-based navigation. In both cases, the modular vehicle software infrastructures are based around the open-source LCM software library for low-latency, real-time message passing. To elucidate the real-world application of LCM in marine robotic systems, we present the software architecture of these two successful marine robotic applications and illustrate the capabilities and flexibilities of this approach to real-time marine robotics. We present benchmarking test results comparing the throughput of LCM with the Mission-Oriented Operating Suite, another robot software system popular in marine robotics. Experimental results demonstrate the capacity of the LCM framework to make large amounts of actionable information available to the operator and to allow for distributed supervisory control. We also provide a discussion of the qualitative tradeoffs involved in selecting software infrastructure for supervisory control. [ABSTRACT FROM AUTHOR]

Abstract: As the computer disappears in the environments surrounding our activities, the objects therein become augmented with sensors, actuators, processors, memories, wireless communication modules and they can receive, store, process and transmit information. In addition to objects, spaces also undergo a change towards becoming smart and eventually Ambient Intelligence (AmI) spaces. In order to model the way everyday activities are carried out within an AmI environment, we introduce the notion of “activity sphere”. In this paper, we are interested in the ontology-based representation of activity spheres from two different perspectives (as creators and as observers), as well as the modeling and control of the dynamic nature of activity spheres. [Copyright &y& Elsevier]

Integration of flight and propulsion control systems in advanced aircraft has attracted much attention because of ever increasing demand on enhancement of performance and reliability. As the underlying dynamic couplings and non-linear interactions of flight and propulsion systems are too complex for realtime execution in onboard computational platforms, hierarchical hybrid (i.e. combined continuously varying and discrete event) architecture is proposed for the development of future generation control systems that will take the advantage of these interactions for mission enhancement. Although the original structures of continuously varying control systems for propulsion and flight are retained, discrete event supervisory (DES) control would facilitate decision-making for aircraft operation. DES decisions regarding propulsion and flight control influence the performance and reliability of the entire vehicle control system due to interactions at the level of continuously varying dynamics. A two-level hierarchical DES control system is designed to supervise and coordinate the operation of twin-engine aircraft propulsion with flight dynamics. In essence, the propulsion system is integrated with the flight dynamical system such that the DES controller at the propulsion level of hierarchy provides load balancing of the engines as well as overall health and mission management of the aircraft propulsion system. The parameter-scheduling dynamic-inversion controller stabilizes and drives the flight system in the vehicle operation envelope and compensates for potential unbalance and any other undesirable action, resulting from discrete event supervision of the propulsion system. Results of real-time simulation on a test bed are presented to demonstrate the efficacy of the proposed control concept. [ABSTRACT FROM AUTHOR]

The article summarizes the results and recommendations from a human-performance-oriented assessment on the involvement of U.S. Army Patriot air defense missile units in two fraticide incidents during the combat operations phase of the Iraq War (March-April 2003). The recommended solutions to the fratricide problem involved both command and control and training modifications. The primary focus of the article is the 3-year follow-on effort to implement and evaluate selected aspects of the recommended fixes.

Because the dynamic characteristics of many physical systems are non-linear and time varying, it is difficult to evaluate appropriate control effort to track the desired trajectory. To deal with this problem, a novel supervisory state feedback controller for unknown non-linear dynamical systems is proposed in this paper to achieve high-precision tracking performance. The proposed scheme comprises a grey uncertainty predictor and a state feedback linearization controller, which evaluates the control effort according to the forecasted data on line. Moreover, to stabilize the system states around a predefined bound region, a supervisory controller is added to adjust the control effort. The design process, theoretical and stability analysis are discussed in detail. Finally, three applications are provided to demonstrate the effectiveness of the proposed strategy. Compared with other methods, the proposed scheme possesses the salient advantages of a simple framework, free from chattering, stable tracking control performance, and robust to uncertainty. [ABSTRACT FROM AUTHOR]

We investigated the visual strategy of a subject without eye movements (AI), comparing her with normal subjects on the ‘real-life’ task of making a cup of tea. Differences in overall performance were surprisingly few. She took no more time than the controls to complete the tea-making task and the division of the task into object-related actions was essentially similar. However, the way AI look in visual information was very different from the normal subjects who used a typical ‘saccade and fixate’ strategy when moving between and scrutinizing objects. AI made saccades with the head, which were on average 1.5 times larger than the eye-head saccades of the controls and lasted four times as long, meaning that AI would have had impaired vision for more of the time than the controls. She also made only approximately one-third as many saccades as normals during the same task. However, she had another strategy, ‘slow drift’, in which she allowed her eyes to move smoothly across the scene at speeds of up to 30°/s. Such movements were never seen in the controls, and we assume that AI used them to offset the cost in time of the slow head saccades, even though they had their own cost in terms of reduced resolution. We demonstrate that these differences have a minimal effect on the timings of events during an object-related action. We discuss supervisory checking operations within actions, and consider what information is needed for appropriate gaze control during object-related actions. [ABSTRACT FROM AUTHOR]