Conventional fault-tolerant control (FTC) schemes typically assume the exponent of the faulty input to be 1, overlooking its impact on actuator power. In this article, we propose a novel FTC strategy that extends the exponent to any positive odd integer, thus capturing higher-order fault effects. In addition, by integrating a Gaussian function to modify the constraint boundaries, a novel suction-cup-type prescribed performance function is proposed. Unlike existing prescribed performance functions, this design uses a suction cup module to regulate output overshoot without requiring asymmetric design. This design is globally effective, eliminating the initial feasibility conditions. Simulation results validate the effectiveness of the proposed scheme.

Ergonomics or human factors engineering (HFE) mainly exploits human experiments to discover one's cognitive and behavioral mechanisms. Such a paradigm, however, suffers from the scale of subject group and the extent to which they can stand for the whole studied population. Additionally, for real-time human-machine tasks, the experiment-modeling-validation-application path may not be applicable since the experiment cannot be flexibly conducted to update cognitive models, leading to a failure of the online system control and management. To solve the dilemma, this article proposes the generative artificial intelligence (GAI)-driven ergonomics to augment the HFE research. By introducing GAI techniques, virtual-real hybrid experiments are combined and supplement more heterogeneous samples, enhancing the input diversity for cognitive modeling and behavioral learning. The case studies of human-machine cooperative driving and aerospace robotic arm operation indicate that the innovative paradigm can effectively and efficiently augment the human experiment data. It can elevate the generality and robustness of human models.

This article investigates the issue of composite anti-disturbance and attack control for networked systems under unknown actuator attacks and external disturbances. First, a double-ended event-triggering mechanism is designed to reduce unnecessary data transmission in the feedforward and feedback channels. Second, an augmented observer is designed to estimate the system states and disturbances. The intermittent estimation signal is then used to compensate for external disturbances through a novel trigger sampling mechanism. Third, the fuzzy logic system is used to approximate the unknown attack signal, and an adaptive output feedback controller is employed to mitigate its impact on the system. Based on these three key techniques, a novel functional dependent on the sampling instants is constructed to analyze semi-global uniform ultimate boundedness of the closed-loop system and obtain the criterion condition of low conservatism. Additionally, the event-triggering matrix and gains are explicitly solved by matrix transformation. Finally, the feasibility and effectiveness of the proposed method are validated via a visual servo control system and a third-order system.

This paper presents an optimal control scheme for a class of nonlinear systems subject to input saturation and external disturbances, achieved through the integration of reinforcement learning (RL) and a self-regulated prescribed performance control (SRPPC) algorithm. The proposed architecture employs an Identifier-Critic-Actor RL structure, implemented via interval type-2 fuzzy logic systems (IT2FLSs), to accurately approximate unknown nonlinear dynamics while optimizing control performance. To circumvent the singularity issues inherent in conventional PPC, the SRPPC strategy is developed to dynamically initialize the prescribed performance bounds (PPBs) and autonomously relax them during severe disturbances or actuator saturation, thereby maintaining the tracking error within a feasible envelope. By synergizing RL-based optimization with the SRPPC safety mechanism, the resulting RL-SRPPC controller not only minimizes operational costs but also guarantees transient safety and strict adherence to performance specifications. Numerical simulations on a one-link manipulator demonstrate the robustness and superiority of the proposed scheme compared to existing methodologies.

Embodiment is a key aspect of human intelligence, related to our ability of to identify the context of the individual experiences at a given time, corresponding to the natural constraints represented by our body. Learning from higher cognitive functions and social coordination between humans can support building intelligent robot systems and facilitates harmonious human-machine interactions. This position paper provides an overview of neural structures and neural dynamics contributing to human cognitive functions, including multisensory integration, Gestalt formation, perception, and building sensory associations. Embodied cognitive principles are illustrated through the intentional action-perception cycle. The results are applied to the design novel algorithms for brain-inspired cognitive robotics. Example scenarios include imitation learning, and the emergence of dialogue patterns in social robotics settings.

This paper proposes a fuzzy control scheme that enforces a unified prescribed performance for full-state errors in input-saturated systems with external disturbances. The proposed scheme is characterized by three key innovations: First, a series of functional transformations are designed to guarantee multiple performance behaviors within a unified control framework, enabling desired behaviors through parameter selection without controller redesign. Second, a novel performance function for virtual errors completely eliminates strict initial value constraints, thereby removing offline verification computations and streamlining the design/implementation process. Third, an error-driven mechanism is developed to prevent singularities induced by input saturation and disturbances. Unlike existing flexible prescribed performance control methods that rely on a control input-driven mechanism, this mechanism offers two distinct advantages: it directly adjusts only a single boundary for a more straightforward adjustment, and operates without dependency on auxiliary system integration. This design eliminates adjustment delays while minimizing performance degradation caused by boundary relaxation. Simulations confirm the scheme’s efficacy and superiority.

Lately, voltage regulations for distributed generation (DG) in direct current (DC) microgrid (MG) have been extensively investigated based on various communication methods. However, the communication is not always reliable or even nonexistent in actual remote area. In this paper, a distributed coordinated voltage control strategy based on power/information hybrid modulation without additional communication lines is proposed. First, the whole state-space model in DC MG under double carriers differential phase shift keying (DC-DPSK) modulation method is constructed. Then, distributed dynamic output feedback control strategy is designed according to established model. The designed distributed control can address leader-following consensus issue in linear heterogeneous time delay multi-agent system which is equal to solve output voltage consensus issue in DC MG. Finally, the effectiveness and potential of the proposed design strategy is verified by simulation results.Note to Practitioners—Output voltage is the vital index for evaluating the quality of power system. Although various accomplishments have been achieved in the area of voltage regulation, the study of voltage regulation control without extra communication lines is rarely considered in DC MG. To fill in the gap, the paper concentrates on designing output feedback consensus control method under DC-DPSK modulation communication to ensure the stability and output voltage consensus in power system. It is worth noting that proposed control strategy based on multi-agent algorithm can be implemented distributed performance without additional communication lines, which is practical in real application. The results aim to provide a helpful reference for secondary control design in the remote area power system, such that the corresponding application research can be promoted.

This paper is interested in the event-triggered leader-following consensus problem for discrete-time multi-agent systems subject to nonlinear dynamic topology. Considering that the on-board resources of each agent are generally limited, a novel adaptive event-triggered strategy is presented in this paper through establishing a nonlinear transformation law of consensus errors, which provides an effective way to weaken the impact of small fluctuations on triggering behaviors after the error systems converge. Then, an interval type-2 fuzzy model is introduced to describe the nonlinear dynamic topology where the dynamic characteristics contain both nonlinear time-varying law and uncertain parameter. In view of the discrepancies in adjacency relationships between different agents, the heterogeneous fuzzy-dependent controllers are employed to further decrease the consensus error. Meanwhile, some sufficient conditions are deduced to solve the designed controllers while ensuring that the consensus of multi-agent systems can be achieved with desired$H_{\infty }$performance. Ultimately, advantages of the presented leader-following control strategy are illustrated by two examples.Note to Practitioners—Many practical control scenarios, such as formation of unmanned air vehicles, can be described by a leader-following consensus problem of multi-agent systems with limited onboard resources. To save the resource consumption of agent, a novel event-triggered scheme is provided, which has more potential to reduce the computational tasks to a greater extent. In addition, aiming to promote the convergence of tracking error, the agent will tend to strengthen connections with the one that have not yet reached consensus, and weaken connections with the others. Therefore, a nonlinear dynamic topology with time-varying weight is introduced during the design of heterogeneous controller, such that the adjacency relationship between agents can be adjusted online. The effectiveness of the obtained results is demonstrated by an actual experiment. It is expected that the presented strategy can be further extended to multi-agent systems where network structures are more complex, such as the one with multiple groups of agents.

In this paper, the reliable tracking control and collision avoidance problems for cyber-physical systems (CPSs) with false data injection (FDI) attacks are investigated. FDI attacks can significantly compromise the safety and performance of CPSs by corrupting control and navigation data. Safety is an important aspect of CPSs. Unmanned ground vehicles and aerial vehicles are important applications of CPSs. The dual challenge of maintaining system safety and stability under deliberate cyberattacks, while ensuring reliable obstacle avoidance in dynamic environments, remains unresolved in many current methodologies. These challenges are amplified in CPSs owing to their reliance on real-time data and their susceptibility to adversarial manipulation. The main objective of this study is to develop a resilient tracking control strategy that can effectively mitigate the impact of FDI attacks and achieve obstacle avoidance. We propose a novel framework based on the exponential control barrier function (ECBF) and a novel observer-based auxiliary signal approach that can ensure the resilience of CPSs against FDI attacks and obstacle avoidance. The effectiveness of our methods is shown through extensive simulations and physical experiments, which depict improved tracking accuracy and system stability in the presence of FDI attacks as compared with those of traditional control methods.Note to Practitioners—This research tackles the problem of maintaining dependable tracking control and avoiding collisions in CPSs vulnerable to FDI attacks, with applications in fields such as smart transportation and smart city. To mitigate the impact of FDI attacks, we propose a resilient control strategy that uses exponential ECBF and an observer-based auxiliary signal approach. This framework mitigates the impact of corrupted data, maintains system stability, and avoids collisions, even in the presence of cyberattacks. Practitioners in CPS design and deployment can benefit from this approach by integrating it into existing systems to improve safety and security. The proposed method is validated through both simulations and physical experiments, demonstrating its practicality for real-world applications.

Recent advances in video tracking with discriminative correlation filters leverage diverse observation models. However, fusing hand-crafted and deep convolutional neural network representations equivalently would overly constrain resolution conditions for template matching, leading to peak response slippage and jittery neighboring search processes, especially problematic in autonomous driving scenarios. This article addresses the inference conservatism issue in multitype feature tracking. We propose a target-observation constraint framework to formalize discrimination conservatism across feature map channels. A learning constraint transformation methodology is introduced to cluster similar representations while pushing dissimilar ones apart. These discriminant constraints are further fine-tuned through joint learning with correlation filters, improving the positional precision of detection responses. Additionally, we propose an updating strategy that suppresses low scores of symmetric dispersion ratio, enhancing tracking robustness. Extensive evaluations on five tracking datasets demonstrate the superior performance of our approach: UAV20L, UAVDT, OTB-100, VOT-2019, and LaSOT.

This research proposes a neural network-based super-twisting controller for robot joints. A modified fast nonsingular terminal sliding surface is introduced, which not only avoids singularity but also increases the convergence rate of the sliding mode control. To address the challenge of system uncertainty modeling, a type-2 fuzzy single hidden layer recurrent neural network (T2FSHLRNN) is proposed. The T2FSHLRNN, configured as a weighted combination of a type-2 fuzzy neural network and a single hidden layer network, demonstrates strong global learning ability. Leveraging its internal and external double-layer feedback mechanism, the network can incorporate both current and previous error information during the approximation process, effectively improving the approximation accuracy and reducing system chattering. Furthermore, an adaptive gain function is proposed and an adaptive terminal super-twisting controller based on T2FSHLRNN (ATSC-T2FSHLRNN) is developed. The system’s stability under unknown disturbance is ensured using Lyapunov synthesis. Based on this, the online parameter learning algorithm for T2FSHLRNN and the variable gains of ATSC are derived. Simulation confirms the effectiveness of the proposed ATSC-T2FSHLRNN.

This paper presents event-triggered integral re-inforcement learning methods to solve nonlinear nonzero-sum differential game problems. Firstly, for nonlinear systems, by constructing coupled Hamilton-Jacobi equations, the theoretical basis for solving multi-player nonzero-sum game problems is established. With the help of integral reinforcement learning, the approximate optimal control strategy corresponding to each player can be obtained without knowing the drift dynamics of the system. Then, the event-triggered mechanism with preliminary operation is constructed by designing appropriate triggering condition. The dynamic triggering mechanism is further integrated into the algorithm architecture of online learning method to realize aperiodic adaptive learning and sampling control, and effectively save system computing and communication resources. Finally, the effectiveness of the proposed reinforcement learning method is verified by theory analyses and simulation experiments.

Recent problems in robotics can sometimes only be tackled using machine learning technologies, particularly those that utilize deep learning (DL) with transfer learning. Transfer learning takes advantage of pretrained models, which are later fine-tuned using smaller task-specific datasets. The fine-tuned models must be robust against changes in environmental factors such as illumination since, often, there is no guarantee for them to be constant. Although synthetic data for pretraining has been shown to enhance DL model generalization, there is limited research on its application for fine-tuning. One limiting factor is that the generation and annotation of synthetic datasets can be cumbersome and impractical for the purpose of fine-tuning. To address this issue, we propose two methods for automatically generating annotated image datasets for object segmentation, one for real-world and another for synthetic images. We also introduce a novel domain adaptation approach called filling the reality gap (FTRG), which can blend elements from real-world and synthetic scenes in a single image to achieve domain adaptation. We demonstrate through experimentation on a representative robot application that FTRG outperforms other domain adaptation techniques, such as domain randomization or photorealistic synthetic images, in creating robust models. Furthermore, we evaluate the benefits of using synthetic data for fine-tuning in transfer learning and continual learning with experience replay using our proposed methods and FTRG. Our findings indicate that fine-tuning with synthetic data can produce superior results compared to solely using real-world data.

Chemotherapy optimization based on mathematical models is a promising direction of personalized medicine. Personalizing, thus optimizing treatments, may have multiple advantages, from fewer side effects to lower costs. However, personalization is a complicated process in practice. We discuss a mathematical model of tumor growth and therapy optimization algorithms that can be used to personalize therapies. The therapy generation is based on the concept of keeping the drug level over a specified value. A mixed-effect model is used for parametric identification, and the doses are calculated using a two-compartment model for drug pharmacokinetics, and a nonlinear pharmacodynamics and tumor dynamics model. We propose personalized therapy generation algorithms for having a maximal effect and minimal effective doses. We handle inter-and intra-patient variability for the minimal effective dose therapy. Results from mouse experiments for the personalized therapy are discussed and the algorithms are compared to a generic protocol based on overall survival. The experimental results show that the introduced algorithms significantly increased the overall survival of the mice, demonstrating that by control engineering methods an efficient modality of cancer therapy may be possible.

In this article, a velocity-free adaptive controller is proposed for the tracking control of servo mechanisms with friction compensation. A continuously differentiable friction model is employed to compensate for the dominant friction nonlinearity of servo mechanisms. Besides, a projection-type adaptive law is applied to handle parameter uncertainties in the system model. Since only the output position signal is directly measurable, an adaptive extended state observer (AESO) is constructed to estimate the indeterminate velocity state, which can also provide an estimation of unmodeled dynamics. Moreover, the dynamic gain switching of AESO can effectively suppress the peaking phenomenon at the motion beginning. Specifically, the parameter adaptation law utilizes the desired velocity state instead of the estimated value, avoiding the coupling problem between parameter and state estimation. The proposed control strategy theoretically demonstrates the transient performance and boundedness of the error in output tracking. Asymptotic stabilization of the system can also be implemented when only parameter uncertainty exists. Comparative experiments are conducted on a linear motor platform to demonstrate the effectiveness of the proposed control scheme.

Like many physiological systems, the various mathematical models describing the glucose-insulin system can only have non-negative inputs. Thus, the control method — often model predictive control in artificial pancreas systems—must provide a non-negative control signal. Existing solutions include saturation and constrained optimization. In this paper, we propose a dynamic extension of the patient model as a way of ensuring the positivity of the control signal, a method previously applied in tumor growth control. We evaluate the controller in a closed-loop simulation and compare the results with a controller using saturation. Our simulations show that control performance with the extended model can reach or exceed the performance achieved with saturation.

Advanced robotics and autonomous vehicles rely on filtering and sensor fusion techniques to a large extent. These mobile applications need to handle the computations onboard at high rates while the computing capacities are limited. Therefore, any improvement that lowers the CPU time of the filtering leads to more accurate control or longer battery operation. This article introduces a generic computational relaxation for the unscented transformation (UT) that is the key operation of the Unscented Kalman filter-based applications. The central idea behind the relaxation is to pull out the linear part of the filtering model and avoid the calculations for the kernel of the nonlinear part. The practical merit of the proposed relaxation is demonstrated through a simultaneous localization and mapping (SLAM) implementation that underpins the superior performance of the algorithm in the practically relevant cases, where the nonlinear dependencies influence only an affine subspace of the image space. The numerical examples show that the computational demand can be mitigated below 50% without decreasing the accuracy of the approximation. The method described in this article is implemented and published as an open-source C++ library RelaxedUnscentedTransformation on GitHub.

Dual-drive gantry stage has been widely applied to various industrial manufacturing fields with its unique structural advantages, and the synchronous control accuracy of the platform is crucial to the performance of the whole motion system. Therefore, an adaptive robust synchronous control scheme based on an improved master-slave structure is proposed, which is not only simple in structure but also easy to implement in engineering. The error dynamics model established in this article makes up for the lag of response of traditional master-slave control and improves the stability of closed-loop system. Online parameter adaptive algorithms deal with parameter uncertainties in the system, while robust control deals with unmodeled dynamics and external disturbances. In addition, nonlinear cogging force compensation is applied to the gantry biaxial system to further improve the control accuracy of tracking and synchronization. Finally, a dual-drive gantry stage system with good tracking and synchronization performance is obtained. The effectiveness and superiority of the proposed control strategy are verified by the comparison of several groups of experiments.

The effects of individual physiological phenomena play an important role considering the accuracy of artificial pancreas systems. An example of these phenomena is the heart rate which is easy to measure. There is a connection between heart rate and endogenous glucose production that significantly influences the blood glucose level. The proper implementation of heart rate could lead to defining physical activity in TIDM models. The aim of the current study is to examine how the change in endogenous glucose production influences the fitting accuracies in model versions with different complexity. Our extensions include a heart rate dependent endogenous glucose production equation, modeling the effect of physical activity, and a part defining the effect of insulin on endogenous glucose production. The joint effect of the mentioned extensions was also considered.

This article studies the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the finite-time prescribed performance is considered, which can guarantee the tracking error enters into the prescribed bounded set in a known time. A disturbance observer is proposed to estimate the external disturbance. It is proved that the closed-loop system is semi-globally practically finite-time stable. Finally, simulation studies for a one-link manipulator are shown to verify the effectiveness of the proposed approach.

This article studies the asynchronous sampled-data filtering design problem for Itô stochastic nonlinear systems via Takagi-Sugeno fuzzy-affine models. The sample-and-hold behavior of the measurement output is described by an input delay method. Based on a novel piecewise quadratic Lyapunov-Krasovskii functional, some new results on the asynchronous sampled-data filtering design are proposed through a linearization procedure by using some convexification techniques. Simulation studies are given to illustrate the effectiveness of the proposed method.

The ever-increasing complexity of robot applications induces the need for methods to approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of tools to address this kind of problems. This survey presents a categorization of the major challenges in robotics that leverage DL technologies and introduces representative examples of successful solutions for the described problems. We also consider the question when and whether to use modular, monolithic models or end-to-end DL, in order to provide a guideline for the selection of the correct model structure and training strategy. By doing so, the current role and adaptability of different techniques at different hierarchical levels of a robot-application can be highlighted, thus providing a well-structured basis to assist future approaches.

A 50th birthday is an important milestone in the life of any individual and certainly in the development of collectives and organizations. January 2021 is such a milestone in the life of the IEEE Transactions ofSystems, Man,andCybernetics, which had its very first issue published in January 1971. Our 50th Anniversary Issue celebrates this remarkable achievement by introducing 20 survey articles from early pioneers of our field, as well as works by leading scientists conducting research at the cutting edge of systems science, with a focus on human aspects, cybernetics, with crucial societal impact. Our goal is to provide a vista of the remarkable developments in the past, giving a snapshot of the present state of our field, and indicating possible avenues for future progress in our rapidly changing research discipline.

To commemorate the 50th anniversary of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, this article examines and reports on its past to current topical coverage of systems science and engineering toward exploring the evolving focus of the research community. Results of a systematic bibliometric analysis are presented with associated conclusions, implications, and summary of topical areas. In addition, respective views regarding the current state of the field and where it is headed are offered by recent leaders of the IEEE Systems, Man, and Cybernetics Society, including its continued relevance and role in the advancement of systems technology.

It is recognized that system trustworthiness is a hyperstructure embodied by the structural, behavioral, and system dimensions with a set of coherent attributes. We explore a theoretical framework of tripartite trustworthiness that can be applied to real-world autonomous systems. We present a formal study of the essences and mathematical models of system trustworthiness and their quantitative measurements in the contexts of autonomous and mission-critical intelligent systems where humans and machines interact in a hybrid environment.

This article studies the issue of adaptive neural network (NN) control for strict-feedback multi-input and multioutput (MIMO) nonlinear systems with full-state constraints and actuator hysteresis. Radial basis function NNs (RBFNNs) are introduced to approximate unknown nonlinear functions. The command filter is adopted to solve the issue of "explosion of complexity." By applying a one-to-one nonlinear mapping, the strict-feedback system with full-state constraints is converted into a new pure-feedback system without state constraints, and a novel NN control method is proposed. The stability of the closed-loop system is proved via the Lyapunov stability theory, and the tracking errors converge to small residual sets. The simulation results are given to confirm the validity of the proposed method.

In case of physiological systems state and parameter estimation is a crucial question. It is key to describe given patient population with appropriate accuracy. Furthermore, state feedback kind of applications also require some sort of estimation procedure in order to get internal information about the controlled system. Linear parameter varying (LPV) framework is beneficial for controller design as well. However, to realize the necessary scheduling parameters, estimation of both state variables and model parameters is needed. A possible solution is the application of Dual Extended Kalman Filter (DEKF) which is able to estimate these signals. The developed framework can be used to design LPV based controller in our further work. In this study we introduce our developed DEKF solution by using the widely applied Cambridge Type 1 Diabetes Mellitus (TIDM) model for virtual patient generation. We have found that our solution is able to estimate the state variables with good accuracy. The variation of parameters can also be tracked by using the proposed solution.

The utilization of modern and advanced control engineering related methods for the control, estimation and assessment of physiological applications is widespread. It is also well-known that this engineering apparatus is executed on digital computers. The current insufficiency of available and accurate discretized models, especially in case of Diabetes Mellitus (DM), provides incentive for this research. The researchers typically approximate the continuous solutions which may not be the best alternative in many cases, in particular considering numerical stability and cost-effectiveness. In this paper we performed an analysis of the available discretization options in order to develop discrete models with a special focus on the Linear Parameter Varying (LPV) systems. LPV techniques are very useful frameworks which allow the application of linear controller, observer and estimator design. In this study, three LPV discretization and two Jacobian based discretization methods are introduced and analyzed to provide a basis for our further investigations in the topic.

The application of the Soft Computing based methods, especially, the Tensor Product (TP) transformation has several beneficial properties from the biological modeling and control point of view, because complex, nonlinear processes can be handled by them effectively. Another advantage of these tools consist on the Linear Parameter Varying (LPV) and Linear Matrix Inequality (LMI) based techniques can be easily connected to them. The aim of this study is to develop TP models, which can describe the tumor growth beside anti-angiogenic treatment. The role of the anti-angiogenic therapies is to decrease the size of the tumor to operable or maintainable level. From control engineering point of view, the treatment process can be formulated as a control task. In this work, we realized two TP models, which approximates the initial transformed model with high accuracy, regardless the kind of input load and without stability problems. The TP models will be used for TP-based controller design on LMI basis.

In case of physiological related researches the appropriate adjustment of the parameters of the mathematical models describing biological phenomenons is a crucial issue. These models are essential in many research field such as the personalized health care or the control of physiological processes. Despite the available identification techniques there is no general solution in those cases where the mathematical model is given, but highly nonlinear in order to capture the main dynamical attitude of the physiological processes to be described. One of our aims was to develop such a general nonlinear identification framework which is flexible, can be easily used and supports the identification of these kind of models. We defined different metrics to measure the performance of the developed system. From the other hand, our goal was to successfully realize the identification framework in case of tumor growth beside anti-angiogenic treatment which is essential in our future work in order to validate the performance of advanced control algorithms. The results show that the nonlinear identification framework performed well in this case, since the predefined requirements from the applied metrics points of view were satisfied in all cases.

Medical imaging introduced the greatest paradigm change in the history of modern medicine, and particularly ultrasound (US) is becoming the most widespread imaging modality. The integration of digital imaging into the surgical domain opens new frontiers in diagnostics and intervention, and the combination of robotics leads to improved accuracy and targeting capabilities. This paper reviews the state-of-the-art in US-based robotic platforms, identifying the main research and clinical trends, reviewing current capabilities and limitations. The focus of the study includes non-autonomous US-based systems, US-based automated robotic navigation systems and US-guided autonomous tools. These areas outline future development, projecting a swarm of new applications in the computer-assisted surgical domain.

Concept model is needed to represent multidisciplinary product system in recent advanced industrial product engineering. This paper is novel methodological contribution to intelligent driving of functional and logical components in system concept model of industrial product. Enhanced representation and handling of objects in requirements, functional, logical, and physical (RFLP) structured engineering model is introduced. The IEEE standardized RFLP structure integrates systems engineering (SE) methodology with physical level product modeling. The proposed method relies upon driving entities in RFLP enabled engineering model using intelligent content representation. It supposes application of configuration and extension purposed user software development capabilities within the functionality of host engineering system at implementation. Research for the proposed driving method utilized former own research results in product model related issues such as integration, high level abstraction, intelligent content definition, and human intent representation. Method is suitable for industrial, concept, or experimental product. In this paper, main elements of the contribution are structure of driving content in the background of RFLP structure represented product information, communication of content at model object driving, and means for connection between parameters of elements in the proposed model extension and in the functional and logical components of RFLP structured product model. More issues in this paper are about connection between virtual and cyber-physical product environments and application of knowledge from organized intellectual property (IP) sources at content background.

In-silico modeling is an important part of biomedical engineering. Advanced controllers providing high quality control can be validated through it checking if the available mathematical model of the given biomedical process produces the desired output. However, due to high patient variability the advanced linear control methods applied on linearized models could produce several distortions compared to the original nonlinear models; hence, these errors should be reduced. Hierarchical control strategies could be a possibility or from modeling point of view using different control-oriented modeling methodologies. Linear Parameter Varying (LPV) approaches with Linear Matrix Inequality (LMI) based modeling and controller design represent one choice. In this paper, we investigate their generalized extension, the Tensor Product (TP) model transformation demonstrated on diabetes modeling. In concrete, the type 1 diabetes modeling on Intensive Care Units (ICU) is envisaged. The achieved results will be used for TP transformation based controller design in our later work.

It is a great pleasure and honor to greet you to the annual conference, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016) at Budapest, Hungary. SMC 2016 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and participants to report up-to-the-minute innovation and development, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human-machine systems, and cybernetics. SMC2016 is dedicated to the Hungarian born John von Neumann “a Pioneer of Modern Computer Science”. In honor of him, the theme of the conference is “A theory that transformed the world to a Cyberspace”. We hope that this conference provides a good platform for valuable meeting and you will enjoy Budapest as well, which is frequently called the “Little Paris of Middle Europe” since it has rich historical and cultural heritage.

Advanced surgical robotic systems aim to offer improved capabilities through automated low-level functions. In the applications, soft tissue mechanics and tool-tissue interaction modeling play an important role in achieving optimal control, relying on model-based control methods. This approach allows for addressing crucial issues during teleoperation, such as time-delay, state observation or stability. This paper presents a novel approach for modeling the behavior of soft tissue during surgical interventions, relying on the widely-employed concept of rheological models. The nonlinear Wiecher model is used for reaction force estimation during tissue indentation, tested on beef liver samples for acquiring mechanical parameters from experimental data. Curve fitting methods were used in both stress relaxation and constant indentation speed compression phases. Reaction forces are estimated using the proposed model, followed by verification tests on ex-vivo beef liver samples. The results of this research showed that the proposed novel rheological soft tissue model is capable of estimating the reaction forces acting on the tool, if the shape of the deformed tissue is known in time. This model can be successfully integrated into closed-loop surgical robot controllers.

Reliable force control is one of the key components of modern robotic teleoperation. The performance of these systems, in terms of safety and stability, largely depends on the controller design, as it is desired to deal with various disturbing conditions, such as uncertainties of the model parameters or latency-induced problems. This work presents a polytopic quasi-linear parameter-varying (qLPV) model derived from a previously verified nonlinear soft tissue model, along with a model-based force control scheme that involves a tensor product polytopic state feedback controller. The derivation is based on the Tensor Product (TP) Model Transformation. The proposed force control scheme is verified and evaluated through numerical simulations.

Complexity is the most common feature of the practical tasks to be solved in control technology. Grasping only a little particular segment of reality, often referred to as “model building”, provides us with imprecise and incomplete models of certain subsystems that operate in dynamic interaction with their neglected environment. The classical method of Model Predictive Control (MPC) assumes that our models, though they are not perfect, at least well describe the most important features of the system under control, therefore these models can be used for controller design and the remaining errors can be treated as uncertainties and/or unknown external perturbations.

Latest development in model based engineering methods and modeling environments placed one of the main emphases on representation of multidisciplinary product system. For that reason, discipline specific modeling procedures and model representations are being integrated. One of the largest software applications was emerged concentrating product engineering related problem solving and applying most advanced software engineering methodology. As contribution to research in this area, this paper introduces new approach to intellectual content driven context structure for driving generation process for elements of requirements, functional, logical, and physical (RFLP) structured generic engineering model. The formerly developed initiative, behavior, con-text, and action (IBCA) driving content structure and its processing procedures were revised and extended for this purpose. The new approach supposes R, F, L, and P levels in engineering model, intellectual property (IP) type knowledge accumulation and reuse, and virtually executable structure of global product level contextual simulations. Industrial engineering system is required as implementation environment which is able to organize and manage engineering models for lifecycle of product. This paper starts with discussion of relevant structural elements and features of engineering model which can accommodate the proposed approach based engineering model components. Following this, need for the proposed extended and restructured IBCA structure and the new approach to content based driving of contextual engineering model elements (components) are explained. Rest of paper introduces issues about the new IBCA content structure and concept considering its implementation in industrial product engineering environment.

Adaptive, model-free control of Type 1 Diabetes Mellitus (T1DM) is a lack in the field of diabetes control, since, most of the applied control strategies are model-based ones. The main problem is that difficult to formulate exact mathematical models to replicate the physiological processes, not just because of their behavior, rather then these processes are changing patient-by-patient. Furthermore, the developed models so far, are highly non-linear and difficult to manage. A possible adaptive control solution can be the recently developed Robust Fixed Point Transformation (RFPT)-based control design method, which can provide control action, based on the observations about the actual output of a controlled system. In this paper we show a survey, how can be used this novel technique related with a known, highorder glucose-insulin model, to investigate the usability according to diabetes control.

Under the conceptual framework of Cognitive Info communications, a recently published definition of Cognitive Control draws twofold sense characterizing the goals of this branch of systems and control science. On the one hand, Cognitive Control aims to apply the results of control theory to regulate natural or artificial cognitive systems. While on the other hand it points toward the modeling of cognitive processes of different levels utilizing the apparatus of the modern systems theory. Focusing on that view, this paper discusses a systematic way of deriving the convex polytopic qLPV model of nonlinear dynamic processes of the human organism based on the Tensor Product (TP) Model Transformation. To support the idea, a concrete modeling example of Insulin-Glucose Dynamics in intensive insulin therapy is presented.

Organization of product information in requirements, functional, logical, and physical (RFLP) model structure is recent result on the long way from conventional documents to comprehensive and consistent virtual environments in engineering. Recent virtual engineering environments utilize generic product model which is self adaptive in order to assure its automatic instantiation in case of changed circumstances and events. The RFLP structure applies known ideas from requirements engineering (RE) and systems (SE) engineering and provides high level abstraction for multidisciplinary definition of product concepts and features. At the same time, advanced features of RFLP structured product model allows for wide applications including fundamental and product related research and development from small experiments on few objects to definition of complete products. Actual problem is to replace the current human dialogue based RFLP structure element definition by multiple human requests driven RFLP structure element generation. As a contribution to solve this problem, paper introduces the request, behavior, and actions (RBA) knowledge content structure. RBA structure was tailored in accordance with knowledge demand at RFLP structure element generation and assured consideration of human intent through contextual chain of content elements from content dialogue or import to RFLP element generation. RBA structure concentrates on modeling of request content driven product behavior. Functional, logical and physical product structures are driven by behavior definitions. RBA structure is recent result at the Laboratory of Intelligent Engineering Systems (LIES, Óbuda University).

With blood purification the life of a person suffering by kidney malfunction can be saved or the quality of her/his life can be increased. This is the purpose of hemodialysis machines, where the blood of the patient is filtered (cleared) in an extracorporeal tube system. Here peristaltic pumps are responsible for the fluid transport, where the strict control is a serious need, in order to maintain patient fluid balance and meet exact dosage. The current paper focuses on designing a robust controller with TP transformation. This controller is presented from the application point of view. The experiences with the designed controller are tested on a real system discussing their applicability.

Robotic telesurgery application could greatly benefit from force control, providing safe and stable application even under time delays and other disturbing conditions. This paper describes a soft tissue model which can be integrated into the control loop of a teleoperational master -- slave robot. Three alternations of the Wiechert model have been implemented and matched against experimental data. A nonlinear model has been proposed to realistically model the properties of liver-type tissues. Based on the model, adequate force control can be designed in the future.

Recently emerging paradigms of the so called Future Internet induce significant changes in consumer and industrial ICT applications. Remote collaboration in mixed physical and virtual realities made possible thanks to the increasing network bandwidth further empowered by the achievements of Internet of Things, Cloud Computing and Internet of Services. This enticing vision brings benefits for several application fields ranging from STEM education to smart factories. The paper discusses some new possibilities through the VirCA (Virtual Collaboration Arena) framework as a pilot realization. A brief introduction is given to VirCA focusing on the basic concepts and features that make it well suited for collaborative work in mixed virtual and physical reality. Through a concrete life-like example, the paper illustrates the way of involving real industrial devices into remote collaboration scenarios and reviews the typical uses of such a shared infrastructure considering the relationship of the virtual and real entities.

Benchmark problems continue to represent an actively studied domain, focusing on application-based situations, where controllers have to deal with typical real environments. In this paper, a Robust Fixed Point Transformations (RFPT)-based Model Reference Adaptive Controller (MRAC) is designed for a modified Translational Oscillations by a Rotational Actuator (TORA) system, which is an indirectly driven, underactuated classical mechanical system with peculiar properties. The RFPT-based design has the advantage of working only with three free parameters, and does not need complex a priori calculations. It is founded on the idea that at the cost of replacing the requirement for global stability with local stability, a mathematically very simple and geometrically lucid, well interpreted methodology can be developed. The resulting structure directly concentrates on the primary design intent, i.e., on the realization of a purely kinematically prescribed trajectory tracking. Examples and simulation results are presented in this paper, demonstrating that the RFPT-based design can provide an efficient MRAC controller for a very special physical system.

Strong demand for definition of multidisciplinary products in virtual engineering environments enforced development of product models towards higher level of abstraction in product lifecycle management (PLM) systems. In order to achieve this, the requirement, functional, logical, and physical (RFLP) structure was included in leading industrial product models. Modeling of product in RFLP structure extended product related problem solving by systems engineering (SE) methodology. This change was grounded by the application of active knowledge (AK) features in feature driven product models. Modeling by AK features established knowledge control at the generation of product component and structure (PCS) features. In this changed scenario, new challenge is development of knowledge driven procedures for the generation of RFLP structure elements. Current leading PLM systems provide dialogues for RFLP element definition and means for including procedures and knowledge for the generation of these elements. Knowledge is product, company, project, and engineer specific and constitutes intellectual property (IP) at companies. Consequently, it should be defined at application environment. The Laboratory of Intelligent Engineering Systems (LIES) joined to the research in abstraction concepts and RFLP element generation methods and widely published the results. This paper introduces a new method for the knowledge communication between human definition and generation of R, F, L, and P elements. This method is based on an abstraction concept which was developed and published at the LIES. It applies the new initiative, behavior, context, and action (IBCA) structure in order to realize a new abstraction which allows for development of knowledge representation of procedures for RFLP element generation. IBCA structure elements and their connections fill the gap between engineer requirement and associated knowledge definition and elements of RFLP structure.

In physiological control there is a need to estimate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma-point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model.

Model Reference Adaptive Controllers (MRAC) have dual functionality: besides guaranteeing precise trajectory tracking of the controlled system, they have to provide an “external control loop” with the illusion that it controls a physical system of prescribed dynamic properties, i.e., the “reference system”. The MRACs are designed traditionally by Lyapunov's 2ndmethod that is mathematically complicated, requiring strong skills from the designer. Adaptive controllers alternatively designed by the use of Robust Fixed Point Transformations (RFPT) operate according to Banach's Fixed Point Theorem, and are normally simple iterative constructions that also have a standard variant for MRAC design. This controller assumes a single actuator that is driven adaptively. Master-Slave Systems form a distinct class of practical applications, in which two arms-the master and the slave-operate simultaneously. The movement of the master must be tracked precisely by the slave in spite of the quite different forces exerted by them. In the present paper, a soft tissue-cutting operation by a master-slave structure is simulated. The master arm has a simple torque-reference friction model, and is driven by the surgeon. The obtained master arm trajectory has to be precisely tracked by the electric DC motor driven slave system, which is in dynamic interaction with the actual tissue under operation. It is shown via simulations that the RFPT-based design can efficiently solve such tasks without considerable mathematical complexity.

Engineering modeling for lifecycle management of product information is challenged with the new demand for representation of multidisciplinary product in a single complex model system. Leading developers of product lifecycle management (PLM) systems recognized the need for conceptual modeling of industrial products in higher abstraction level than the classical physical level representation of product and related objects. They turned to PLM models which use representation of product in the requirement, functional, logical, and physical (RFLP) structure. This means introduction of systems engineering (SE) methodology in PLM modeling providing user surface for the definition of R, L, F, and P elements and their connections. It was recognized that definition of elements by engineers is very complex work and needs automation by user defined and integrated procedures. Development of these procedures for the new abstraction levels needs new research results in the PLM modeling methodology. After early recognition of the need for higher level abstraction in product modeling, the Laboratory of Intelligent Engineering Systems (LIES) at the Óbuda University, Budapest, developed five level abstraction model which allowed high level representation of knowledge for decisions at conceptual product development. This knowledge content based abstraction model was utilized at development of the initiative, behavior, context, and action (IBCA) structure. The IBCA structure includes knowledge represented entities for the generation of elements in RFLP structure. This paper introduces the RFLP structure based self adaptive PLM model, its relations to the conventional product modeling, the IBCA structure, and application of the IBCA structure at RFLP element generation. Finally, importance of the proposed method and feasibility of its implementation are discussed.

One of the most dynamically developed areas of the research in engineering is definition and application of knowledge in product model. Product model is in the centre of product lifecycle management systems to support engineering activities with comprehensive representation of product information. Recently, knowledge is defined in the form of active features in the product model. When an active knowledge feature or value of its parameter changes, other feature parameters those are in contextual connection with it change according to its content. In this paper, a new modeling method is introduced to elevate active knowledge from the product feature level to the product behavior level. This allows for coordination of product definition on the behavior level. Result of a recent study on the currently applied product definition is utilized at the establishment of a connection between the proposed higher level knowledge features and the currently used active knowledge features. In the proposed modeling method, result of human intent analysis is applied at situation based generation of features in the product structure. The new method of product feature definition and the demanded new features are explained in this paper. In order to easy implementation of the proposed modeling in large modeling systems, the method of the feature driven product definition is the same as in the current leading industrial product lifecycle management systems.

In engineering, self-adaptive product model provides a medium which is in the possession of the capability to accommodate any knowledge for autonomous actions to modify product representations in case of any changed circumstance. In current advanced product models, knowledge defines relationships among feature parameters in order to automatic modification of the model for new situations and events. In this way rules, formulas, algorithms, and other knowledge features give self-adaptive characteristic to product model for design parameter driven self modification and reconfiguration. Generally, new instances are created from a generic model. Changes are propagated across the product model by contextual connections of feature parameters. Development of self adaptive product model needs new methods for decision assistance during lifecycle of the product. This paper introduces a research which is aimed to contribute to this effort by extension of the direct feature parameter control knowledge towards higher level representations. The proposed modeling applies higher level knowledge for the definition of relationship between product objectives and feature parameters. Objective covers product function which is supported by specification and knowledge driven method in order to control product feature generation. Any objective change initiates appropriate changes of the self adaptive product model. In order to achieve this, contextual chain is established between objective and the related affected feature parameters in the product model. The proposed method is a contribution to efforts to achieve aimed physical product behaviors using more sophisticated virtual prototype of product.

Product lifecycle management (PLM) systems have been gained application at leading areas of industry. By now, they integrate all engineering activities. Product definition in these systems relies upon advanced object modeling and virtual prototyping. However, large models with thousands of strongly related objects brought new problem of thinking and seeing in extensive information structures. New methods are demanded mainly to establish better organized knowledge based parallel interactions by high number of engineers. Fortunately, recent developments in product modeling introduced rules, reactions, function structures, and other active knowledge carriers in product models in order to establish situation and event based product definition. These techniques serve storage and repeated application of engineering knowledge and offer good connection for enhanced knowledge based communication between model generation processes and group work environment in the future. The authors of this paper established a new methodology for enhanced knowledge based connection of human interactions with current product modeling. They utilized their former results and realized coordinated interacting human intent based and product behavior driven adaptive product object definition. The proposed new control of product definition is supported by extended context definition. In this paper, the authors introduce the new product definition methodology and discuss key issues including principle of their Coordinated Request based Product Modeling (CRPM), connection of CRPM to the currently prevailing Classical Product Modeling (CPM), new representations in product behavior related structures, and a new method for the definition of coordinated contextual structure of objectives (SOB) and behaviors (SBE). The proposed modeling is considered as API communicated extension to the CPM modeling in current industrial PLM systems.

This paper introduces new objective related knowledge driven product feature definition method in order to achieve better human influence on feature definition in model based product engineering. The proposed method utilizes earlier relevant results by the authors in knowledge communication intensive product modeling and it is devoted as a contribution to solution for some actual problems in current product lifecycle management (PLM) systems. The authors analyzed definition of objects, knowledge representations, contextual connections, and product behavior descriptions in industrial PLM systems. They recognized that current PLM systems suffer from the lack of higher level representations in product model and decided development of new representations mainly in product function and quality driven feature definition. Main purposes of the reported work were establishing organized situation and event based active knowledge representation and replacing direct definition of product model features by a new indirect communication between human and product feature generation processes. The proposed method is an organic extension to modeling in current PLM systems. Its main characteristics are improved human interactions, situation and event based knowledge, and multi intent based decisions in contextual feature based product definition.

Traditional differential evolution (DE) mutation operators explore the search space with no considering the information about the search directions, which results in a purely stochastic behavior. This paper presents a DE variant with self-navigation ability for multi-objective optimization (MODE/SN). It maintains a pool of well designed DE mutation operators with distinct search behaviors and applies them in an adaptive way according to the feedback information from the optimization process. Moreover, we deploy the neural network, which is trained by the extreme learning machine, for mapping an artificially generated solution in the objective space back into the decision space. Empirical results demonstrate that MODE/SN outperforms several state-of-the-art algorithms on a set of benchmark problems with variable linkages.

In currently applied product definition, product lifecycle management (PLM) systems apply integrated object modeling for contextual development of products in model space. In order to elevate this object level of integration to the level of knowledge, a new product modeling method is proposed. On the knowledge level of modeling, human makes attempt for model change by launching product objects accompanied with knowledge objects or by launching knowledge objects for generation of new product objects. Requested model changes are coordinated on the basis of human and product aspects. When an attempt gains permission to execute, an adaptive action entity is generated to replace direct human control of creation or modification of product objects. In this paper, current knowledge based features of product modeling systems are characterized. A new knowledge based product definition method by the authors is introduced and its integration with the currently applied object modeling is explained. Following this, the currently applied direct and expert knowledge based product definition methods are compared with the proposed indirect product definition. Finally, consistency and context as two main advantages of the new modeling are introduced and discussed.

In this study, we continue our analysis on the use of RLS in neural fuzzy systems. The recursive least square (RLS) algorithms can have great learning performance for neural fuzzy networks. From our previous work, it can be observed that the advantages of using RLS instead of using BP are not so obvious. For the use of forgetting factor in RLS, the idea is to account for the effects of the change in the premise part. In this study, we have observed that the use of a forgetting factor can still have some advantages when the premise part is fixed. The idea is similar to the used of Widrow-Hoff learning concept in the backpropagation learning algorithm. From our experiments, a strong forgetting factor (smaller value) can let the consequent part trace the error in the learning phase. But the testing error becomes very large. When the system capacity is sufficient, a forgetting factor will improve both in the learning phase and in the testing phase. Finally, the initial value of the covariance matrix is considered. The learning capacity will rise when the initial value increases. But it will increase the error tracing phenomenon in the consequent part too. But it is opposite in a system with less learning capacity.

A method of FPGA implementation of the class of parametric digital conjunctions defined by (p)-monotone sum of basic t-norms is proposed. The paper presents the logical diagrams of parametric digital conjunctions developed by means of VHDL language in Quartus II with ModelSim software of Altera. Parametric digital conjunctions can be used in reconfigurable digital fuzzy systems where the parameter p and a sequence of basic t-norms used in definition of parametric conjunction can be changed.

The present paper generalizes the problems of nonnegative matrix factorization and decomposition of fuzzy relation into a common non-linear non-negative matrix factorization problem. Algorithms for solving such a general nonlinear problem are discussed, based on general algebraic structures of ordered semirings with generated pseudo-operations. Some decompositions in max-product, max-plus algebras are also shown.

Recently, product models serve lifecycle product information for wide area of engineering activities. This extended purpose results very large and complex product models with high number of relations. Restricted capability of product models for representation of some important elements of the human thinking process during definition of engineering objects in current industrial modeling systems makes it impossible to evaluate, revise, and change hundreds of already decided engineering object parameters. In this paper, a new methodology and several related processes are introduced in order to better communication between human and model generation processes. The main essence of the proposed product modeling is that human intent controlled development of engineering objects is realized by an extension to current industrial product model called as model of information content. Engineering objectives and contextual connections are defined as human intent driven features and applied at decision on engineering object parameters and at control of processes for engineering object parameter optimization and relating. The proposed modeling is connected to knowledge handling and knowledge based advisory functionalities of industrial product modeling systems. As a conclusion, a new modeling is placed between human and model information generation procedures in order to represent human originated background of model information for engineering objects.

Recently it has been shown that in image processing, the usual sum and product of the reals are not the only operations that can be used. Several other operations provided by fuzzy logic perform well in this application. We continue this line of research and we propose the use of a pair consisting of a uninorm and an absorbing norm determined by a continuous, strictly increasing generator instead of the classical sum and multiplication. In the present paper the discrete cosine transform (DCT)-based image compression method is generalized by using a pair consisting of a uninorm and an absorbing norm. We show that the results of the proposed method outperform in several cases the classical DCT image compression algorithm.

This paper gives an overview on some classes of binary connectives that play a key role in fuzzy logic. We start with left-continuous triangular norms, by recalling some facts about the nilpotent minimum and its extensions. Some standard construction methods are also described. We also deal with two possible extensions of t-norms: the classes of uninorms and nullnorms.

Effectiveness of an engineering process in product modeling is determined by modeling system capability in representation of elements from the human thinking process during decision making on strongly interrelated engineering objects. Engineering objects are not only objects in the product structure but also include objects for product related activities such as analysis, manufacturing and production. Current product modeling systems apply the well-proven information based modeling and model in order to assist product related engineering activities in the industry. The authors analyzed this classical modeling and developed a modeling methodology to improve communication between engineer and information based modeling procedures. The proposed methodology is also aimed to enhance indirect communication between engineers by using of the product model as medium. In this paper, thinking process of human for product definition, information content based modeling, changed application of knowledge, and restricted automation of engineering decision processes are discussed. A comparison of information and information content based product modeling methods is given. In addition to enhanced human-computer and human-human communication, higher level of automation in product related engineering mainly in decision making is also discussed.

The problem addressed in this paper is that of including knowledge in product model for the application in industrial product development. A recent paradigm in engineering is product lifecycle management (PLM) in virtual space. Increasing demand for communication, representation, assignment, acceptance, and processing of high number of knowledge entities in PLM systems is a new challenge. Decision making on modeled product objects in virtual spaces requires better communication between interacting human and computer procedure than it is possible with data representations in current product models. In order to achieve this, the authors analyzed knowledge specific characteristics of engineering activities for entire product lifecycle. They concluded that current product models should be completed by description of information content for enhanced explanation and evaluation at decision-making. In this paper, knowledge advisors as recent achievements in industrial product modeling systems are evaluated and problems emerged at knowledge assistance of engineering decisions are identified. Rest of the paper is organized as follows. Flow of knowledge related information and processing of multiple intents are evaluated as communication issues. Next, content orientated product model is proposed and its integration with data oriented descriptions in present industrial models is explained. Following this, a new content oriented sector of the product model is conceptualized. Finally, implementation of the proposed modeling method in industrial PLM system is discussed and items are issues are assigned for the research work in the next future.

Recently it has been shown that sum and product are not the only operations that can be used in order to define concrete approximation operators but several operations provided by fuzzy logic can be used. In this sense, in the present paper, pseudo-linear approximation operators of Shepard-type are studied from the practical point of view, in function approximation. It is shown that in several cases these outperform classical approximation operators based on sum and product operations.

Integrated engineering systems are composed by modeling (CAD/CAM) and product data management (PDM) software. They offer communicative framework for engineering activities. However, integrated engineering systems have inherently restricted capabilities in organizing different engineering processes in projects at development and application of products. The authors are active in development of ideas, concepts, and methods to solve this problem. They proposed integrated model object (IMO) to enhance engineering processes in case of high number of interrelated activities and objects. IMO can be implemented in industrial engineering software systems as application development component. In this paper, the authors first give an outline of their approach to product lifetime management regarding integration-related purposes, development, content, and structure of an IMO. Following this, development of description of modeled objects is characterized by connections of IMO with CAD/CAM systems, essential IMO functions, and the IMO ware. Next, elements of IMO for analysis and multilevel information structure are detailed. Finally, essential IMO processes are detailed for decision on changes and management of change chains.

The authors propose a unified modeling approach to product-robot information systems as a contribution to robot related applications of product models. Despite product modeling by CAD/CAM system is prevailing in the industry, robot processes are typically not product model data driven. The authors consider geometric aspect of robot programming as the main drawback of development. The main purpose of the reported work is to establish an application centered and multi-aspect-based communication between product modeling and industrial robot related engineering. Although the primary application area of the proposed approach is robot-assisted manufacturing of products, it also can be utilized at other applications where engineering object descriptions in product models are available for robot environment. In the proposed model, engineering objects are described as application oriented associative aspects. High level integration of engineering information including intelligent content is intended for environment adaptive model objects. In this manner, models with the ability to react changes in their affect zone can be established. The paper also explains key elements of the related modeling methodology. Implementation of the approach and methodology is considered as an extension to modeling procedures and models in advanced industrial CAD/CAM systems with open architecture.

In this paper, the authors propose a method for intelligent computing extension of industrially applied shape centered modeling. The objective is development of intelligent model objects that include modeled object data and behavior description and human intent filtered knowledge. The proposed model objects are intelligent organizers of information, knowledge, and procedures for decision assistance. Results facilitate the completion of present simple data, rule, and formula based on relating parameters of modeled objects by an intelligent solution. The paper details application of integrated model object for description of an interrelated group of modeled objects. Following this, coordinated behavior and adaptive actions are proposed. Furthermore, associativity-based definition of affect zones for object changes is explained and intent filtered application of knowledge at engineering decisions is introduced. Finally, implementation of the proposed methodology as an extension of shape centered modeling of products and concept of the extended modeling systems are discussed as implementation issues.

This paper deals with the application of adaptive model objects for integration of modeling products and robot systems. Product modeling is an advanced method for integration of product related computer descriptions. Product model describes engineering related information about parts and assemblies in a mechanical system. Robots use this information for flexible automated manufacturing of the modeled product. Consequence of definition or change of model objects may be unacceptable change in production. To prevent similar problems by improper design at production, two-way communication is needed between product design and production planning. The authors proposed highly integrated model objects for this purpose. Model objects evaluate and react inside and outside changes by analysis of the related behaviors. Adaptivity features are created and then applied for behavior driven generation of modification of model entities. This paper analyzes process-oriented product modeling of robot application related mechanical systems. The proposed objects are composed by elementary, structural, relationship, behavior, knowledge and adaptivity features. This paper also discusses integration of multi layered robot process model with shape models, procedure of modification handling by behavior features in a product-robot model system, general architecture of the proposed model objects and behavior feature driven activities of active model objects on four levels.

The application of a novel branch of computational cybernetics is extended to the adaptive control of very inaccurately and partly modeled electromechanical systems also having unmodeled non-linear dynamic coupling with their environment. The method uses "uniform structures" for modeling, and "uniform procedures" for "repetitive" or cumulative learning like in the case of the "traditional" soft computing approaches. But it considerably reduces the number of free parameters tuning with simple, lucid, and explicit algebraic operations of limited number of steps. It is demonstrated via simulation that the control can operate even if strongly nonlinear terms (Coulomb friction and sticking combined with the elastic and viscous terms of the unmodeled external interaction in the Stribeck model), and elastically deformable joints are present. As a paradigm a mechanically 3 DOF SCARA arm actuated by voltage-controlled DC motors is considered. The system has 9 degrees of freedom (6 mechanical and 3 electrical). For the controller only three mechanical degrees can efficiently compensate the effect of the unmodeled degrees of freedom, the external interaction, and the inaccuracy of the modeled part of the robot.

In this paper, the authors discuss their recent contribution to the methodology of active product modeling and propose integrated model objects for engineering activities in mechanical systems. The purpose of the proposed model objects is to react to changes in the inside and the related modeled world by analysis of behaviors and behavior driven generation of adaptivity features for modification of model entities inside and outside the object. They are composed of elementary, structural, relationship, behavior, knowledge and adaptivity features. The proposed model objects are inherently highly integrated. An overview of product modeling introduces the authors' approach to modeling by using the proposed model objects. Following this, the architecture of integrated, environment adaptive model objects is detailed. Then activities of integrated objects are placed on four levels of the model. Finally, integration and implementation issues are discussed.

One of the usual important criteria to be considered in real time control applications is the computational complexity of the controllers, observers and models applied. In the paper a higher order singular value decomposition (HOSVD) based complexity reduction technique is proposed for Takagi Sugeno (TS) fuzzy models, which is capable of defining the contribution of each local linear model included in the TS fuzzy model. This helps us with discarding the weakly contributing ones according to a given threshold. Reducing the number of models leads directly to the computational complexity reduction.

In this paper an integration of product modeling with robot control is discussed. The authors propose a method to enhance applications of geometric and other product and production equipment related information in robot control. Research of the related methodology was motivated by a growing significance of robots in highly flexible manufacturing systems. Difficulties of reproduction of modeled product geometry at its applications resulted a growing importance of online real time communications between a centralized product modeling and several model applications. The reported research concentrates on the creation and application of model data information understandable by robot controls, on communication of model data with robot environments through the Internet, and finally on some related telerobotics issues. It covers one of the actual problem complexes in the integration of engineering modeling with its shop floor level applications. Systems integration, data model, data communication and model processing have been investigated. In the paper an introduction of the robot related activities of engineers and the modeling environment are first given, taking into account the robot control aspect. Following this, key issues regarding robot tasks and their shape model based control are explained. Then an approach to interactive application of Internet-based telerobotics for product model driven control of robots is outlined. Finally, possibilities for an integration of remote robot control applications with product modeling environments are discussed.

This paper introduces a modeling method for intelligent model features for engineering modeling in industrial CAD/CAM systems. The related research started from earlier results in development of integrated feature based product modeling. An active model is proposed that integrates knowledge from modeling procedures, generic part models and engineers. Paper discusses the scenario of intelligent modeling based engineering. Then key methods for application of computational Intelligence in computer model based engineering systems are detailed including knowledge driven models as well as areas of their application. Next, behavior based models with intelligent content involving specifications and knowledge for the design processes are emphasized. Finally, an active modeling is proposed and possibilities for its application are outlined.

An approach to fuzzy modeling based on the tuning of parametric conjunction operations is proposed. First, some methods for the construction of parametric generalized conjunction operations simpler than the known parametric classes of conjunctions are considered and discussed. Second, several examples of function approximation by fuzzy models, based on the tuning of the parameters of the new conjunction operations, are given and their approximation performances are compared with the approaches based on a tuning of membership functions and other approaches proposed in the literature. It is seen that the tuning of the conjunction operations can be used for obtaining fuzzy models with a sufficiently good performance when the tuning of membership functions is not possible or not desirable.

The present state of development of a new branch of soft computing (SC) developed for the adaptive control of a special class of nonlinear coupled MIMO systems is reported. Its uniform structures are obtained from certain Lie groups, unlike that of the traditional SC approaches. The advantages include: a priori known, very much reduced in size, and increased in lucidity; and the parameter tuning or learning is replaced by a simple and short explicit algebraic procedure. The only disadvantage is a limited circle of applicability. Convergence considerations are also discussed for MIMO and SISO systems. Simulation examples are presented for the control of an inverted pendulum by using the generalized Lorentzian matrices. It is concluded that the method is promising and probably imposes acceptable convergence requirements in many practical cases.

An efficient approach for the adaptive control of approximately and partially known mechanical systems, like traditional soft computing, uses "uniform structures" for modeling, but these structures are obtained from certain Lie groups as the symplectic group or the generalized Lorentz group. This approach considerably reduces the number of free parameters in the model. Until now its efficiency was investigated for mechanical uncertainties and external dynamic interactions. In this paper the behavior of the electric drives are also included in the investigations. A 1-DOF mechanical system, a pendulum driven by a DC motor in a computed torque control, is investigated via simulation. The necessary torque is calculated from a formal primitive mechanical model and an adaptivity rule. For adaptivity real number scaling and the generalized Lorentz group elements are used. It is concluded that a single adaptive loop can compensate for the mechanical and the electrical uncertainties simultaneously.

Traditional fuzzy controllers or artificial neural networks are constrained by standardized formal procedures and structural restrictions. While in general it is guaranteed that these approaches or their integration can solve a quite wide class of problems, their "orthodox" application may result in too complicated control not exploiting the peculiarities of the given task under consideration. The aim of the paper is to demonstrate that unconventional integration of simple elements such as classic PID/ST, regression analysis, saturated sigmoid transition functions, fuzzy sets and uniform structures obtained from the Lagrangian classical mechanics instead of the connection structure of a feedforward artificial neural networks can result in a simple and efficient adaptive control for robots involved in unknown environmental dynamic interaction. Due to their simplicity and reduced number of parameters real time tuning can be carried out in these structures. Most of the parameters are independent of the particular problem to be solved and neither "scaling problems" nor "network paralysis" occur during the learning phase. It is concluded that the different components of the control can successfully co-operate in finding the "proper" system model even in the case of very rough initial model estimation and external interaction.

Integration of engineering activities in advanced, intelligent computing assisted environments constitutes one of the most active research interests in engineering modeling. This is because high level modeling techniques are required and these techniques must be suitable for integration in comprehensive product models. Existing application oriented master model concepts and standards such as STEP can serve this purpose. However, some important models and their relationships with other models have not been made available. One of these is a model of the machining process of mechanical parts that is associative with all of the related models in a comprehensive product model. The authors propose a model that offers full associativity with form feature based part models so that creation of part models can be done simultaneously with the analysis of machinability. The authors refused earlier solutions as the application of separate design and machining features and proposed the application of a second associative reordered feature sequence for machining purposes. Then the fusion or split of the listed shape modifications are applied to fulfil the demands of the machining process. An introduction of the feature based concepts is followed by an outline of the applied modeling method. Following this the creation and handling of Petri net model representations are detailed. After a discussion of the application of the results, the main characteristics of the modeling are concluded.

In this study, a novel method for identification and control of nonlinear systems is developed. The method proposed realizes the dynamics of a system by employing the Runge-Kutta method at the upper level. The intermediate level of the strategy constructs the architecture utilizing an adaptive neuro fuzzy inference system. The overall system is able to imitate the behavior of a complex dynamic system with a few rules or to control the system with high accuracy. The proposed method has been applied to a two degrees of freedom direct drive SCARA robot.

On the basis of Lagrangian mechanics uniform structures made of simple and standardized procedures and closed form analytical formulas were previously used to develop an adaptive control for SCARA robots in dynamic interaction with an unmodeled environment. This standardized form contains well defined free parameters by tuning of which the complex effect of the behavior of the controlled system as well as that of the external interactions can be 'imperfectly' modeled and learned. These structures, free parameters and procedures play exactly the same role as that of the traditional artificial neural networks (ANNs) or fuzzy controllers: the structures and the procedures are fit for a wide class of problems more or less similar to each other, while parameter tuning corresponds to learning the concrete properties of a particular element of this wider set. While in the case of the standard soft computing methods there is no reliable a priori information on the number of the concrete elements in the uniform structures, the proposed method has definite indication for this by using the Lie parameters of the orthogonal group. In the initial stage of learning the proposed methods also use a standardized ancillary procedure, a very simple form of regression analysis based controller compensating the remnant errors whenever appropriate. In this paper certain details of the uniform control and parameter-tuning are discussed on the basis of simulation results. It can be concluded that the approach is promising therefore experimental investigations are under preparation.

This paper describes an on going research by the authors who are seeking methods to integrate an earlier developed manufacturing process modeling method in the virtual manufacturing (VM) technology. The primary aim is to attach special manufacturability evaluation to modeling of mechanical products. A more or less generic process model is generated for a cluster of manufacturing tasks then manufacturing process entities are created by evaluation of the manufacturing process model using part, form feature, production environment, production planning and cost information. Petri net manufacturing process model representation is used. It has proved to be a versatile virtual manufacturing tool in manufacturing process planning, evaluation of manufacturability and in manufacturing environment design. A process model evaluation procedure is emphasized in the course of which feasibility and resource requirements of the manufacturing are revealed. Firstly, techniques of evaluation of manufacturability, the applied approach to integration of shape and manufacturing process modeling and the related virtual manufacturing concepts are introduced. The process model is outlined and earlier relevant publications of the authors are cited. Then problems and solutions at evaluating of process model in close connection with design and environmental changes are detailed. Following this, integration of the method into a comprehensive virtual manufacturing concept is explained. Finally, main issues and future research plans are concluded.

While well-proved shape description methods are available in present day CAD/CAM systems modeling of design intent is a rather new area. The authors have investigated modeling of complex considerations that are behind the decisions of engineers on model data in the course of product modeling. A product modeling system is considered that involves humans, model creating procedures and model data processing procedures. The proposed intent description can be utilized in present and future applications and later modification or re-engineering of models. Modeling of design intent is considered as part of the virtual manufacturing where integration of conceptualization activities is emphasized. In this paper, the problem and the analyzed human-machine-human system are outlined. Then characteristics and content of the design intent are described. Following this, representation of design intent and attaching intent data to product models are detailed. Finally, possibilities for application of the results of the research in CAD/CAM modeling environments are concluded.

By using a fuzzy entropy approach, three sets of new generalized operators are presented. After a general discussion on fuzzy entropy, the concept of an elementary entropy function of a fuzzy set is introduced. Using this mapping, the generalized intersections and unions are defined as mappings that assign the least and the most fuzzy membership grade to each of the elements of the domain of the operators, respectively. It is shown that these operators can be constructed from the conventional min and max operations. Next, two modified sets of operations are introduced. The second part of the paper investigates the applicability of the new operators in fuzzy logic controllers. Simulations have been carried out so as to determine the effects of the operators on the performance of the fuzzy controllers. It is concluded that the first set of operators does not provide stable control, but the performance of the fuzzy controller can be improved by using the modified operations for a class of plants.

A number of approaches based on sliding mode control methodology are proposed in the literature for the position control of robotic manipulators. An important problem in this context is chattering, that is high frequency oscillations in the velocity. In this paper, a novel approach is considered which eliminates chattering if the controller parameters are set suitably. A fuzzy adaptation scheme is devised for the online adaptation of the parameters of this method. Simulation results show that the fuzzy rule based adaptation scheme ensures good controller performance without chattering.

In this paper a theoretical approach to performance improvement of fuzzy logic robot controllers is presented. The approach is based on two new sets of T-operations introduced by the authors. The T-norms and T-conorms are defined as minimum and maximum entropy operations. Simulation has been carried out so as to compare the effects of the new and the conventional T-operators in case of a 4 DOF rigid-link flexible-joint SCARA type robot. It is concluded that by fixing the other parameters of the controller certain sets of T-operations can improve the performance of the controller.

The problem of designing a unified control and fault detection system is addressed. The proposed control scheme combines a modified inverse problem technique and the idea of repetitive control. The joint torques are computed in each time instant from the unbiased, minimum variance estimate of the joint coordinates and velocities based on the exact robot dynamic model and the previous observations. The estimations are generated by a discrete Kalman filter. The idea of the control system's refinement is to use the optimally estimated trajectory obtained at the previous trial as a better reference trajectory for linearization in each working cycle. The problem of fault detection and isolation is formulated as a problem in hypothesis testing by regarding the normal operation of the robot manipulator as the null hypothesis. The error signal is defined as the difference between the actual robot manipulator output and the expected output based on a stochastic system model and the previous output data and is generated by the discrete Kalman filter.

The novel control method presented addresses the problem to design a nonlinear feedback controller in case of uncertain dynamical parameters and other disturbances. The input torques necessary to drive the robot manipulator are on-line computed as a function of the unbiased, minimum variance estimates of the joint coordinates and velocities, and the corrected accelerations. The state estimator is given by the discrete Schmidt-Kalman filter. The development of the system model, the control law and the implementation of the technique are outlined.>