Extended crossover model for human-control of fractional order plants

A data-driven generalization of the crossover model is proposed, characterizing the human control of systems with both integer and fractional-order plant dynamics. The model is developed and validated using data obtained from human subjects operating in compensatory and pursuit tracking tasks. From the model, it is inferred that humans possess a limited but consistent capability to compensate for fractional-order plant dynamics. Further, a review of potential sources of fractionality within such man–machine systems suggests that visual perception, based on visual cues that contain memory, and muscular dynamics are likely sources of fractional-order dynamics within humans themselves. Accordingly, a possible mechanism for fractional-order compensation, operating between visual and muscular sub-systems, is proposed. Deeper analysis of the data shows that human response is more highly correlated to fractional-order representations of visual cues, rather than directly to objective engineering variables, as is commonly proposed in human control models in the literature. These results are expected to underpin future design developments in human-in-the-loop cyber-physical systems, for example, in semi-autonomous highway driving

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.This work has been supported by the Department of Science and Technology (DST), Government of India, under the PURSE programme

Memory pattern identification for feedback tracking control in human-machine systems

Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a onedimensional tracking task. Specifically, data recorded from human subjects controlling dynamical systems with different fractional order were investigated.Method: A Finite Impulse Response (FIR) controller was fitted to the data, and pattern analysis was performed to the fitted parameters. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closedloop was conducted.Results: It is shown that the FIR model can be employed to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less or equal to 1.Conclusion: For systems of different fractional order, the proposed control scheme – based on a FIR model – can effectively characterize the linear properties of manual control in humans.Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.</div

Increasing the damping of oscillatory systems with an arbitrary number of time varying frequencies using fractional-order collocated feedback

Acknowledgements: This research was sponsored in part by the Spanish Government Research Program with the project DPI2016-80547-R (Ministerio de Economía y Competitividad), in part by the University of Castilla-La Mancha under Project 2019-GRIN-26969 and in part by the European Social Fund (FEDER, EU).Peer reviewedPublisher PD

Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Fuzzy logic based PID controllers have been studied in this paper, considering several combinations of hybrid controllers by grouping the proportional, integral and derivative actions with fuzzy inferencing in different forms. Fractional order (FO) rate of error signal and FO integral of control signal have been used in the design of a family of decomposed hybrid FO fuzzy PID controllers. The input and output scaling factors (SF) along with the integro-differential operators are tuned with real coded genetic algorithm (GA) to produce optimum closed loop performance by simultaneous consideration of the control loop error index and the control signal. Three different classes of fractional order oscillatory processes with various levels of relative dominance between time constant and time delay have been used to test the comparative merits of the proposed family of hybrid fractional order fuzzy PID controllers. Performance comparison of the different FO fuzzy PID controller structures has been done in terms of optimal set-point tracking, load disturbance rejection and minimal variation of manipulated variable or smaller actuator requirement etc. In addition, multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) has been used to study the Pareto optimal trade-offs between the set point tracking and control signal, and the set point tracking and load disturbance performance for each of the controller structure to handle the three different types of processes

A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time Domain Tuning Based on Integral Performance Indices

A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input-output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI{\lambda}D{\mu} controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.Comment: 30 pages, 20 figure

Modelling Human-Driver Behaviour Using a Biofidelic Approach

This dissertation is concerned with the subject of modelling human steering control of ground vehicles. Special care has been taken with respect to designing a model that is biofidelic, i.e., a model that operates according to the principles of human control. With this aim, first classical human control theory has been revisited, both from a literature review and an experimental perspective; data have been recorded from test subjects in compensatory and pursuit tracking tasks. The tracking experiments are the first ever to be performed with fractional order plants, which are plants suitable to represent system memory. From the data, an extension of the Crossover model by McRuer’s is designed, to include the control of such category of plants. The proposed model is referred to as the Fractional Crossover Model. This is followed by a study on modelling memory in human-machine systems from a classical control theory viewpoint. These results broaden the existing array of manual control modelling techniques and can be employed in a modular manner, combined with current models. More significantly – and still with respect to the domain of generic human control and human-machine systems – a new approach for modelling the human-operator is proposed. This approach consists in treating the problem from a statistical viewpoint. With this methodology a novel human control model based on multiplicative dynamics is presented. The model, which was inspired on actual results in neuroscience, is validated with the tracking data obtained from test subjects and by comparing it to classical models in the literature. Hence the model is useful to analyse human performance or to reproduce human control in simulation, field tests or in the video game industry. With respect to steering control modelling, which is the main topic of this dissertation, additional experiments with test subjects were conducted in a simple vehicle simulator – with hardware and software specifically developed during this research program to test multiple hypotheses. The data were analysed with the intent of identifying which optical variables drivers employ while controlling a vehicle on public roads; it is seen that the splay angles– which are the projections of the road lines on the retina – are likely candidates for lane keeping at low speeds. This brings on a novel human-centred driver model first proposed here. This model includes multiplicative human control over the splay angles, and far-point error perception for lane keeping at higher speeds. The human-centred model has its domain of applicability in the intelligent transportation industry, in particular for the development of shared control systems and advanced driver-assistance systems for semi-autonomous ground vehicles. Additionally, the model can be employed in field testing of ground vehicles – for example, in vehicle durability tests. Furthermore, the topic of alternative steering devices for driving autonomous and semi-autonomous vehicles is investigated. This leads to another of the contributions in this dissertation. Here it is proposed that for such vehicles, and for the control of systems with a shared control perspective, anisometric steering wheel can be advantageous under certain schemes – tight rein or loose rein modes according to the H-metaphor. This is supported by additional data collected in the driving simulation experiments. Resulting from this, fractional order transfer functions are employed to increment steering stability and control accuracy with the isometric device. This prototypical steering system is applicable for the control of ground vehicles with the so-called by-wire controls, which are already incorporated in some commercially available vehicles

Dual-Active-Bridge Model and Control for Supporting Fast Synthetic Inertial Action

This article proposes a dual-active-bridge control to support the fast synthetic inertial action in DC microgrids. First of all, the selection of the isolated DC/DC converter to link an energy storage system with the DC bus in a microgrid is analyzed and the advantages of the dual-active-bridge converter controlled by a single-phase shift modulation justify its selection. An active front-end can be then adapted to connect the DC bus with an AC grid. Secondly, this paper presents the design of a discrete PI controller for supporting fast synthetic inertial action. In particular, a discrete dual-active-bridge model based on the transferred power between both converter bridges, which overcomes the approximations of the output current linearization model, is proposed. Moreover, the article introduces a novel equation set to directly and dynamically tune discrete PI parameters to fulfill the design frequency specifications based on the inversion formulae method. In this way, during the voltage/power transients on the DC bus, the controller actively responds and recovers those transients within a grid fundamental cycle. Since the developed set of control equations is very simple, it can be easily implemented by a discrete control algorithm, avoiding the use of offline trial and error procedures which may lead to system instability under large load variations. Finally, the proposed control system is evaluated and validated in PLECS simulations and hardware-in-the-loop tests