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We propose a novel robust Model Predictive Control (MPC) scheme for nonlinear multi-input multi-output systems of relative degree one with stable internal dynamics. The proposed algorithm is a combination of funnel MPC, i.e., MPC with a particular stage cost, and the model-free adaptive funnel controller. The new robust funnel MPC scheme guarantees output tracking of reference signals within prescribed performance bounds--even in the presence of unknown disturbances and a structural model-plant mismatch. We show initial and recursive feasibility of the proposed control scheme without imposing terminal conditions or any requirements on the prediction horizon. Moreover, we allow for model updates at runtime. To this end, we propose a proper initialization strategy, which ensures that recursive feasibility is preserved. Finally, we validate the performance of the proposed robust MPC scheme by simulations. [ABSTRACT FROM AUTHOR]

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Summary: Data‐driven feedforward control for tracking of varying and non‐resetting point‐to‐point references requires continuous updating of feedforward parameters instead of task‐by‐task updating. The aim of this paper is to develop an adaptive feedforward controller for non‐resetting point‐to‐point motion tasks by a data‐driven feedforward controller. An approximate optimal instrumental variable (IV) estimator with real‐time bootstrapping is employed in a closed‐loop setting to update the feedforward parameters. A case study on a wafer‐stage and experimental validation on a benchmark motion system show the performance benefit. [ABSTRACT FROM AUTHOR]

Mobility using wheelchair is a crucial feature for disabled people. Between a manual wheelchair and an electric wheelchair, there is a very interesting and promising solution consisting of an electrical kit that converts a manual wheelchair to an electric wheelchair. Autonomad‐Mobility is a company selling such kits, where the wheels are equipped with a brushless motor. It presents new possibilities for mobility, especially for crossing obstacles and running on different types of soil. Several methods have been tested, from linear to nonlinear ones including the linear parameter‐varying control framework. Nevertheless, the most dangerous and critical issue is to be able to produce a so‐called safe and robust swing‐up: the transition from standard mode (four wheels on the ground) to gyroscopic mode (the wheelchair balancing on the rear push‐wheels). The gyroscopic mode is a unique feature improving mobility by providing a way to overcome obstacles that would normally be unsurpassable due to the caster wheels. Whereas some results exist, the solution must be compatible with any wheelchair and any disabled person whatever is her/his height and weight. Robustness of this unstable feature is therefore the key point of the work knowing that "exact" modeling is unrealistic. With the model in descriptor form and using the quasi‐linear parameter‐varying framework new stabilization conditions formulated as linear matrix inequality constraints are developed for attaining the goal. Simulations and real‐time experiments demonstrate the interest of this approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the tracking control problem for the lower-triangular systems with unknown fractional powers and nonparametric uncertainties. A prescribed performance control approach is put forward as a means of resolving this problem. The proposed control law incorporates a set of barrier functions to guarantee error constraints. Unlike the previous works, our approach works for the cases where the fractional powers, the nonlinearities, and their bounding functions or bounds are totally unknown; no restrictive conditions on the powers, such as power order restriction, specific size limitation or homogeneous condition, are made. Moreover, neither the powers and system nonlinearities nor their bounding functions or bounds are needed. It achieves reference tracking with the preassigned tracking accuracy and convergence speed. In addition, our controller is simple, as it does not necessitate parameter identification, function approximation, derivative calculation, or adding a power integrator technique. At the end, a comparative simulation demonstrates the effectiveness and advantage of the proposed approach. [ABSTRACT FROM AUTHOR]

Data-driven approaches like reinforcement learning (RL) allow a model-free, self-adaptive controller design that enables a fast and largely automatic controller development process with minimum human effort. While it was already shown in various power electronic applications that the transient control behavior for complex systems can be sufficiently handled by RL, the challenge of non-vanishing steady-state control errors remains, which arises from the usage of control policy approximations and finite training times. This is a crucial problem in power electronic applications which require steady-state control accuracy, e.g., voltage control of grid-forming inverters or accurate current control in motor drives. To overcome this issue, an integral action state augmentation for RL controllers is introduced that mimics an integrating feedback and does not require any expert knowledge, leaving the approach model free. Therefore, the RL controller learns how to suppress steady-state control deviations more effectively. The benefit of the developed method both for reference tracking and disturbance rejection is validated for two voltage source inverter control tasks targeting islanded microgrid as well as traction drive applications. In comparison to a standard RL setup, the suggested extension allows to reduce the steady-state error by up to 52% within the considered validation scenarios.

Control augmentation systems (CAS) are conventionally built with classical controllers which have the following drawbacks: dependence on domain specific knowledge for tuning and limited self-learning capability. Consequently, these drawbacks lead to sub-optimal aircraft stability and performance when exposed to time varying disturbances. Hence, to curb the stated problems; this paper proposes the development of a deep reinforcement learning (DRL) pitch-rate CAS (qCAS), aimed at guaranteeing adaptive stability, pitch-rate control tracking and disturbance rejection across the longitudinal dynamics of an aircraft. This stated aim was actualized by developing a CAS with a deep deterministic policy gradient (DDPG) agent. Subsequently, this proposed method was compared with two classical qCAS methods (a developed PID-aCAS and a benchmark PIqCAS obtained from literature). The results show that the developed DDPG-qCAS method outperformed the classical methods in peak overshoot, reference command tracking and disturbance rejection as well as mean absolute error (MSE) and mean steady state error (MSSE). Hence, it can be inferred that it is important to apply artificially intelligent controllers to the flight control systems of aircraft in order to achieve superior time response, control command tracking accuracy and disturbance rejection. [ABSTRACT FROM AUTHOR]

The objective of this work is to design an Integral Sliding Mode Controller based on barrier function (ISMCbf) for a human Heart Rate (HR) during a treadmill exercise. ISMCbf commands the speed of the treadmill such that the individual HR follows a time-varying profile. This profile is pre specified as part of rehabilitation exercises for patients with cardiovascular diseases. ISMCbf is chosen due to its well-known robustness properties as well as to its simple design procedure as compared to classic SMC and ISMC. It does not require the upper bounds of the uncertainties and perturbations in its design. Moreover, it does not have discontinuous function, hence it is a chattering-free controller. ISMCbf designed in this work for the first time for this system and its performance is compared to Quasi SMC (QSMC) and Super Twisting SMC (STSMC) from previous studies. The simulated exercises were conducted on a nonlinear model describing HR response to the walking speed of a treadmill. For ISMCbf, the model parameters and their upper bound of uncertainties are considered unknown. During two different exercise scenarios, the three controllers guided HR to follow the time-varying reference profile. However, ISMCbf showed higher quantitative performance by recording less Integral Squared Error (ISE) and Integral Time Absolute Error (ITAE) indices as compared to the other controllers. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to supply reference inputs to feedback systems. In this approach, modified inputs are used to ensure enhancing the capability of the system to achieve the actual desired inputs. Traditionally, the difference between the reference inputs to a system and the outputs are used to decide the suitable correcting actions. In this paper, modified reference inputs are used to cancel out the steady state error of the system. Accordingly, the control effort is evaluated based on a different set of errors. As a result, more effective control actions are implemented to the system. The results show that, the performance specifications of the systems are remarkably enhanced. The enhancement results obtained, include, much limited bounds of the control effort, smaller delay, enhanced transient response, and zero steady state error. [ABSTRACT FROM AUTHOR]

We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We provide explicit bounds on the maximum input signal and the required uniform sampling time. A key strength of this approach is its capability to serve as a safety filter for various learning-based controller designs, enabling the use of learning techniques in safety-critical applications. We illustrate its versatility by integrating it with two different controllers: a reinforcement learning controller and a non-parametric predictive controller based on Willems et al.'s fundamental lemma. Numerical simulations illustrate effectiveness of the combined controller design. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) scheme funnel MPC enables output tracking of smooth reference signals with prescribed error bounds for nonlinear multi-input multi-output systems with stable internal dynamics. Earlier works achieved the control objective for system with relative degree restricted to one or incorporated additional feasibility constraints in the optimal control problem. Here we resolve these limitations by introducing a modified stage cost function relying on a weighted sum of the tracking error derivatives. The weights need to be sufficiently large and we state explicit lower bounds. Under these assumptions we are able to prove initial and recursive feasibility of the novel funnel MPC scheme for systems with arbitrary relative degree — without requiring any terminal conditions, a sufficiently long prediction horizon or additional output constraints. [ABSTRACT FROM AUTHOR]

We show that Funnel MPC, a novel model predictive control (MPC) scheme, allows tracking of smooth reference signals with prescribed performance for nonlinear multi-input multioutput systems of relative degree one with stable internal dynamics. The optimal control problem solved in each iteration of funnel MPC resembles the basic idea of penalty methods used in optimization. To this end, we present a new stage cost design to mimic the high-gain idea of (adaptive) funnel control. We rigorously show initial and recursive feasibility of funnel MPC without imposing terminal conditions or other requirements like a sufficiently long prediction horizon. [ABSTRACT FROM AUTHOR]

A common assumption about the function of gender agreement in discourse is that it serves referent identification and reference tracking. There is some evidence in favour of this view, and an increasing body of research which finds no foundation for it; this study further supports the latter perspective. It is based on a fully annotated corpus of the Papuan language Mian and uses a noteworthy property of the Mian agreement system: object agreement in transitive verbs is "sporadic", i.e. it depends on the lexical type of a transitive verb whether it agrees with its object. Therefore we can measure whether speakers of Mian manipulate overt vs. null arguments in discourse to compensate whenever lack of agreement might make argument reference ambiguous. The results clearly show that the proportions of overtly realized objects for agreeing verbs and non-agreeing verbs do not differ significantly, thus lending little support to the claim that gender agreement serves a major function in reference tracking in discourse. [ABSTRACT FROM AUTHOR]

This paper is concerned with the tracking control problem for the lower-triangular systems with unknown fractional powers and nonparametric uncertainties. A prescribed performance control approach is put forward as a means of resolving this problem. The proposed control law incorporates a set of barrier functions to guarantee error constraints. Unlike the previous works, our approach works for the cases where the fractional powers, the nonlinearities, and their bounding functions or bounds are totally unknown; no restrictive conditions on the powers, such as power order restriction, specific size limitation or homogeneous condition, are made. Moreover, neither the powers and system nonlinearities nor their bounding functions or bounds are needed. It achieves reference tracking with the preassigned tracking accuracy and convergence speed. In addition, our controller is simple, as it does not necessitate parameter identification, function approximation, derivative calculation, or adding a power integrator technique. At the end, a comparative simulation demonstrates the effectiveness and advantage of the proposed approach.

This paper considers a class of space fractional partial differential equations (FPDEs) that describe gas pressures in fractured media. First, the well-posedness, uniqueness, and stability of the considered FPDEs are investigated. Then, the reference tracking problem is studied to track the pressure gradient at a downstream location of a channel. This requires manipulation of gas pressure at the downstream location and the use of pressure measurements at an upstream location. To achieve this, the backstepping approach is adapted to the space FPDEs. The key challenge in this adaptation is the nonapplicability of the Lyapunov theory, which is typically used to prove the stability of the target system as the obtained target system is fractional in space. In addition, a backstepping adaptive observer is designed to jointly estimate both the system's state and the disturbance. The stability of the closed loop (reference tracking controller/observer) is also investigated. Finally, numerical simulations are given to evaluate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This work addresses the reference tracking problem for uncertain systems with quasi one-sided Lipschitz nonlinearity. The uncertainty is assumed to be of a norm bound parametric type. Moreover, transient response shaping using the concept of 'return time' is also proposed. The controller design relies on the solution of Linear Matrix Inequalities (LMIs) and hence is computationally efficient. The proposed control law is linear in states, and thus the implementation is often straightforward. To illustrate the capability and simplicity of the proposed theory, three design examples are included. [ABSTRACT FROM AUTHOR]

The increasing complexity of modern technical systems can exacerbate model uncertainty in model-based control, posing a great challenge to safe and effective system operation under closed loop. Online learning of model uncertainty can enhance control performance by reducing plant–model mismatch. This article presents a learning-based stochastic model predictive control (LB-SMPC) strategy for reference tracking of stochastic linear systems with additive state-dependent uncertainty. The LB-SMPC strategy adapts the state-dependent uncertainty model online to reduce plant–model mismatch for control performance optimization. Standard reachability and statistical tools are leveraged along with the state-dependent uncertainty model to develop a chance constraint-tightening approach, which ensures state constraint satisfaction in probability. The stability and recursive feasibility of the LB-SMPC strategy are established for tracking time-varying targets, without the need to redesign the controller every time the target is changed. The performance of the LB-SMPC strategy is experimentally demonstrated on an atmospheric pressure plasma jet (APPJ) testbed with prototypical applications in plasma medicine and materials processing. Real-time control comparisons with learning-based MPC with no uncertainty handling and offset-free MPC showcase the usefulness of LB-SMPC for predictive control of safety-critical systems with hard-to-model and/or time-varying dynamics. [ABSTRACT FROM AUTHOR]

We present two studies exploring how participants respond when a speaker contrasts two ideas, then expresses an ambiguous preference towards one of them. Study 1 showed that, when reading a speaker's preference as text, participants tended to choose whatever was said last as matching the speaker's preference, reflecting the recent-mention bias of anaphora resolution. In Study 2, we asked whether this pattern changed for audio versions of our stimuli. We found that it did not. We then asked whether observers used gesture to disambiguate the speaker's preference. Participants watched videos in which two statements were spoken. Co-speech gestures were produced during each statement, in two different locations. Next, an ambiguous preference for one option was spoken. In 'gesture disambiguating' trials, this statement was accompanied by a gesture in the same spatial location as the gesture accompanying the first statement. In 'gesture non-disambiguating' trials, no third gesture occurred. Participants chose the first statement as matching the speaker's preference more often for gesture disambiguating compared to non-disambiguating trials. Our findings add to the literature on resolution of ambiguous anaphoric reference involving concrete entities and discourse deixis, and we extend this literature to show that gestures indexing abstract ideas are also used during discourse comprehension. [ABSTRACT FROM AUTHOR]

Today’s world is now more interested in the robustness and automation of machines, robots, and vehicles for accurate performance in the events of external disturbances and uncertainties in the system parameters. For that purpose, a Proportional–Integral–Derivative (PID) control has found many applications in every field of industry and academics. Many methods can be used for tuning of PID, however, these methods suffer from continual variations of parameters and exterior disturbances. Therefore, a PID controller is needed that is robust to external disturbances and possess auto-tuning feature according to varying process conditions. The main motivation of this paper is to present a PID controller that is robust, continuously auto-tuned by considering uncertainties of the plant, optimize the response by minimizing the overshoot in reference tracking, and achieve stability in minimum settling time by rejecting disturbance. The earlier models can only do two tasks, tuning and optimization but this paper is proposing a contribution to such models by rejecting external disturbances and making sure the smooth running of the system. In this paper, we have done the simulation work to demonstrate the desired behaviour of the system and evaluate the effectiveness of the proposed model and a brief analysis is done by discussing the comparison between the previous work in literature and the proposed work.

This research investigates the discrete-time adaptive backstepping control to track the reference clutch torque and estimate the clutch touchpoint displacement simultaneously for a transfer case clutch. Note that the clutch touchpoint changes due to the clutch wear, temperature variation, and other factors. As a result, it needs to be estimated in real time for accurate clutch torque control. Instead of using a separate algorithm to estimate touchpoints, this brief proposes to utilize an integrated scheme for both tracking reference torque and estimating varying touchpoints. More specifically, the deadbeat-based control law is designed based on a systematic non-Lyapunov-function-based backstepping scheme. The stability of the closed-loop system is guaranteed, and all closed-loop states are bounded. In addition, due to the deadbeat design, the tracking and estimation convergence rates are faster than or equal to any other backstepping designs. Furthermore, the closed-loop system performance is robust to external disturbances. Finally, the proposed method is validated through simulation studies using experimental data. [ABSTRACT FROM AUTHOR]

Today's world is now more interested in the robustness and automation of machines, robots, and vehicles for accurate performance in the events of external disturbances and uncertainties in the system parameters. For that purpose, a Proportional–Integral–Derivative (PID) control has found many applications in every field of industry and academics. Many methods can be used for tuning of PID, however, these methods suffer from continual variations of parameters and exterior disturbances. Therefore, a PID controller is needed that is robust to external disturbances and possess auto-tuning feature according to varying process conditions. The main motivation of this paper is to present a PID controller that is robust, continuously auto-tuned by considering uncertainties of the plant, optimize the response by minimizing the overshoot in reference tracking, and achieve stability in minimum settling time by rejecting disturbance. The earlier models can only do two tasks, tuning and optimization but this paper is proposing a contribution to such models by rejecting external disturbances and making sure the smooth running of the system. In this paper, we have done the simulation work to demonstrate the desired behaviour of the system and evaluate the effectiveness of the proposed model and a brief analysis is done by discussing the comparison between the previous work in literature and the proposed work. [ABSTRACT FROM AUTHOR]

Ngarrindjeri, a language from southern South Australia, is being revived on the basis of material recorded from 1840 until the 1960s. This material shows a heavy use of three types of pronouns, suggesting a language that is 'pronoun happy'. When reviving a language, it is essential to know how pronouns work, but the earliest source does not include the kinds of texts that allow analysis of how speakers use pronouns. Texts representing actual connected free speech in Ngarrindjeri are not attested until nearly a century later, by Ronald and Catherine Berndt and Norman Tindale. We compare the forms, meanings and second position distribution of Ngarrindjeri pronouns over time and across sources, considering dialect variation and language change. We show that the pronoun form-meaning pairs in texts recorded in the 1930s and 1940s are consistent with those recorded in the nineteenth century, and so we can have some confidence in using the Berndt and Tindale texts to reconstruct pronoun function. Confidence is further enhanced by showing the similarity in pronoun functions in texts recorded on the same topic from the same speaker, Albert Karloan, by the Berndts and Tindale. This review of Ngarrindjeri pronouns opens up possibilities for language revivalists. [ABSTRACT FROM AUTHOR]

This work examines stabilisation and reference tracking problems for uncertain deterministic as well as switched stochastic nonlinear systems. The concept of input state stability is employed here in the tracking problems. Additionally, an attempt is made to improve the transient performance of deterministic nonlinear systems by employing the notion of return time which is applicable to a broader class of nonlinearities. The transient performances are compared using the Integral Square Error (ISE). The control law is linear in states and thus is easy to implement. The optimisation problem is formulated in a framework of Linear Matrix Inequalities (LMIs). The proposed design is tested on simulation examples. The advantages are established on benchmark problems from the literature like Control of the Wing Rocket Aircraft and the Continuously Stirred Tank Chemical Reactor (CSTR). As per the author's knowledge, not much work is reported on these problems. [ABSTRACT FROM AUTHOR]

This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update the input trajectory until desired tracking performance is achieved. Unlike existing approaches, the proposed method determines necessary parameters automatically, i.e., AI-MOLE works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE only requires input/output information, but can also exploit available state information to accelerate learning. While other approaches are typically only validated in simulation or on a single real-world testbed using manually tuned parameters, we present the unprecedented result of validating the proposed method on three different real-world robots and a total of nine different reference tracking tasks without requiring any a priori model information or manual parameter tuning. Over all systems and tasks, AI-MOLE rapidly learns to track the references without requiring any manual parameter tuning at all, even if only input/output information is available. [ABSTRACT FROM AUTHOR]

Multi-Input Multi-Output (MIMO) Linear Time-Invariant (LTI) controllable and observable systems where the controller has access to some plant outputs but not others are considered. Analytical expressions of coprime factorizations of a given plant, a solution of the Diophantine equation and the two free parameters of a two-degrees of freedom (2DOF) controller based on observer stabilizing control are presented solving a pole placement problem, a mixed sensitivity criterion, and a reference tracking problem. These solutions are based on proposed stabilizing gains solving a pole placement problem by output feedback. The proposed gains simplify the coprime factorizations of the plant and the controller, and allow assigning a decoupled characteristic polynomial. The 2DOF stabilizing control is based on the Parameterization of All Stabilizing Controllers (PASC) where the free parameter in the feedback part of the controller solves the mixed sensitivity robust control problem of attenuation of a Low-Frequency (LF) additive disturbance at the input of the plant and of a High-Frequency (HF) additive disturbance at the measurement, while the free parameter in the reference part of the controller assures that the controlled output tracks the reference at LF such as step or sinusoidal inputs. With the proposed expressions, the mixed sensitivity problem is solved without using weighting functions, so the controller does not increase its order; and the infinite norm of the mixed sensitivity criterion, as well as the assignment of poles, is determined by a set of control parameters. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for the state estimation and reference tracking of nonlinear dynamic systems over the packet erasure channel, which is an abstract model for transmission via the Internet, WiFi wireless network and ZigBee modules. A new encoder and decoder for real time state estimation of nonlinear dynamic systems at the end of communication link when the measurements are sent through the limited capacity erasure channel, are presented. Then, using the available results from control theory, a controller for reference tracking and hence the stability of the system is also designed. That is, for nonlinear systems, almost sure asymptotic state estimation and reference tracking techniques including an encoder, decoder and a controller are presented. The satisfactory performances of the proposed state estimation and control techniques are illustrated via computer simulations by applying the proposed techniques on the unicycle model, which represents the dynamics of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Virtual reference feedback tuning (VRFT) is a data‐driven control method that optimises the response of a closed‐loop system to reference tracking at the expense of a not optimised response to disturbance rejection. This work presents the application of VRFT to two cascade control structures usually considered in the control literature to improve the overall closed‐loop performance for single‐input systems. For both structures, the authors derive the optimal and practical filter formulation to cope with the case where the controller to be designed is underparameterised. Also, in the case where the internal controller is at the feedback path, they derive a new approach that uses data from a single experiment and considers a unique reference model. Simulation examples illustrate the importance of using the VRFT filter, along with the performance improvements obtained from both cascade loops in terms of reference tracking and disturbance rejection. [ABSTRACT FROM AUTHOR]

Summary: This article deals with the model predictive control (MPC) of linear, time‐invariant discrete‐time polytopic (LTIDP) systems. The 2‐fold aim is to simplify the treatment of complex issues like stability and feasibility analysis of MPC in the presence of parametric uncertainty as well as to reduce the complexity of the relative optimization procedure. The new approach is based on a two degrees of freedom (2DOF) control scheme, where the output r(k) of the feedforward input estimator (IE) is used as input forcing the closed‐loop system ∑f. ∑f is the feedback connection of an LTIDP plant ∑p with an LTI feedback controller ∑g. Both cases of plants with measurable and unmeasurable state are considered. The task of ∑g is to guarantee the quadratic stability of ∑f, as well as the fulfillment of hard constraints on some physical variables for any input r(k) satisfying an "a priori" determined admissibility condition. The input r(k) is computed by the feedforward IE through the on‐line minimization of a worst‐case finite‐horizon quadratic cost functional and is applied to ∑f according to the usual receding horizon strategy. The on‐line constrained optimization problem is here simplified, reducing the number of the involved constraints and decision variables. This is obtained modeling r(k) as a B‐spline function, which is known to admit a parsimonious parametric representation. This allows us to reformulate the minimization of the worst‐case cost functional as a box‐constrained robust least squares estimation problem, which can be efficiently solved using second‐order cone programming. [ABSTRACT FROM AUTHOR]

Aim: This paper examines whether second-generation Turkish heritage speakers in the Netherlands follow language-specific patterns of reference tracking in Turkish and Dutch, focusing on discourse status and pragmatic contexts as factors that may modulate the choice of referring expressions (REs), that is, the noun phrase (NP), overt pronoun and null pronoun. Methodology: Two short silent videos were used to elicit narratives from 20 heritage speakers of Turkish, both in Turkish and in Dutch. Monolingual baseline data were collected from 20 monolingually raised speakers of Turkish in Turkey and 20 monolingually raised speakers of Dutch in the Netherlands. We also collected language background data from bilinguals with an extensive survey. Data and analysis: Using generalised logistic mixed-effect regression, we analysed the influence of discourse status and pragmatic context on the choice of subject REs in Turkish and Dutch, comparing bilingual data to the monolingual baseline in each language. Findings: Heritage speakers used overt versus null pronouns in Turkish and stressed versus reduced pronouns in Dutch in pragmatically appropriate contexts. There was, however, a slight increase in the proportions of overt pronouns as opposed to NPs in Turkish and as opposed to null pronouns in Dutch. We suggest an explanation based on the degree of entrenchment of differential RE types in relation to discourse status as the possible source of the increase. Originality: This paper provides data from an understudied language pair in the domain of reference tracking in language contact situations. Unlike several studies of pronouns in language contact, we do not find differences across monolingual and bilingual speakers with regard to pragmatic constraints on overt pronouns in the minority pro-drop language. Significance: Our findings highlight the importance of taking language proficiency and use into account while studying bilingualism and combining formal approaches to language use with usage-based approaches for a more complete understanding of bilingual language production. [ABSTRACT FROM AUTHOR]

It is often claimed that reference tracking is a key function of nominal classification systems, not least because of the role such systems can play in referent disambiguation. This paper reports on the results of a comparative study of reference in texts from four languages, focusing specifically on the disambiguating function of nominal classification. The results strongly suggest that disambiguation is not a primary function of nominal classification systems. While gender and/or classifiers sometimes contribute to the avoidance of referential conflict, the reality is that the conditions have to be just right – all competing references must be of opposing genders, and those genders must be formally distinct – and this happens with surprisingly low frequency. We are better off viewing disambiguation as a convenient by-product of nominal classification systems that a language can exploit when conditions allow. [ABSTRACT FROM AUTHOR]

This brief presents a reconfigurable transient optimizer (RTO) applied to a four-phase buck converter for optimizing both dynamic-voltage-scaling (DVS) and load transient responses to approach the theoretically minimum output-voltage undershoot ${ \Delta }\text{V}_{\mathrm{US}}$ , overshoot ${ \Delta }\text{V}_{\mathrm{OS}}$ , and settling time $\text{t}_{\mathrm{S}}$. The DVS and load transients are instantly detected by a voltage sensor and calibrated capacitor-current sensor, respectively. When a large DVS or load transient occurs, the RTO enables all four phases, reconfigures its circuit architecture, and controls the optimal ON–OFF times of the power switches, thereby settling the output voltage in a single ON–OFF switching with ${ \Delta }\text{V}_{\mathrm{US}}$ , ${ \Delta }\text{V}_{\mathrm{OS}}$ , and $\text{t}_{\mathrm{S}}$ close to their respective theoretical minima. The converter is fabricated in a 0.18- $ {\mu }\text{m}$ CMOS process with a 2.3-mm2 chip area. For a 1-to-1.8 V (1.8-to-1 V) DVS transient, the measured ${ \Delta }\text{V}_{\mathrm{OS}}$ (${ \Delta }\text{V}_{\mathrm{US}}$) is not observable, while the measured $\text{t}_{\mathrm{S}}$ is 182 ns (192 ns). For a 1.8-A step-up (step-down) load transient, the measured ${ \Delta }\text{V}_{\mathrm{US}}$ (${ \Delta }\text{V}_{\mathrm{OS}}$) and $\text{t}_{\mathrm{S}}$ are 56 mV (45 mV) and 85 ns (76 ns), respectively. Compared with other state-of-the-arts, this brief’s $\text{t}_{\mathrm{S}}$ in the DVS transient response is the closest to its theoretical minimum, while the ratios of ${ \Delta }\text{V}_{\mathrm{US}}$ , ${ \Delta }\text{V}_{\mathrm{OS}}$ , and $\text{t}_{\mathrm{S}}$ to their respective theoretical minima in the load transient response are comparable. [ABSTRACT FROM AUTHOR]

The locomotion of people with amputation is slower, less stable and requires more metabolic energy than the locomotion of physically fit individuals. Individuals with amputation of the lower extremities fall more often than able individuals and often have difficulty moving on uneven terrain and stairs. These challenges can mostly be attributed to the use of passive mechanical prosthetic legs that do not react actively to perturbations. Latest submitted solutions for active prosthetic devices of the lower extremities can significantly improve mobility and quality of life for millions of people with lower limb amputation, but challenges in control mechanisms of such devices are currently limiting their clinical viability. In this paper we present a prototype of an above-knee prosthetic leg with active knee and ankle joints while using the proposed robust control law. [ABSTRACT FROM AUTHOR]

In the control field, most of the research papers focus on feedback control, but few of them have discussed about feedforward control. Therefore, a review of the most commonly used feedforward control algorithms in industrial processes is necessary to be carried out. In this paper, in order to benefit researchers and engineers with different academic backgrounds, two most representative kinds of feedforward controller design algorithms and some other typical industrial feedforward control benchmarks are presented together with their characteristics, application domains and informative comments for selection. Moreover, some frequently concerned problems of feedforward control are also discussed. An industrial data driven example is presented to show how feedforward controller works to improve system performance and achieve the maximum economic profits. [ABSTRACT FROM AUTHOR]

In this paper, we propose a performance-based activation mechanism for anti-windup compensation. The signals representing the transient performance of the closed-loop system are passed through a static performance compensator and then injected into the traditional anti-windup activation module and a performance-based anti-windup design framework is developed. The resulting equivalent controller contains the information of the anti-windup compensator and has the potential for improving the performance of the closed-loop systems. Under this new anti-windup design framework, sufficient conditions are established for estimating the domain of attraction and the nonlinear L 2 gain of saturated systems. Based on these conditions, we formulate optimization problems to determine the optimal gains of anti-windup and performance compensators for enlarging the domain of attraction and reducing the nonlinear L 2 gain. Simulation results show that, compared with the existing methods, our proposed approach has the ability to acquire a significantly larger estimate of the domain of attraction, a significantly tighter estimate of the nonlinear L 2 gain, and significantly higher transient quality in reference tracking. [ABSTRACT FROM AUTHOR]

The present study sheds new light on advanced control methods of photovoltaic (PV) emulators using finite set model predictive control (FS‐MPC). In the first part of the study, a predictive PV emulator (P‐PVE) based on a Buck converter is proposed and tested under hard climatic conditions and load variations. The high performance of the P‐PVE in terms of dynamic response, reference tracking, accuracy, simplicity, and efficiency is confirmed experimentally when compared with those of the commonly used one based PI controller. The second part of the study proposes an efficient cascaded predictive control (CPC) method applied[AQ ID=Q1] on two topologies of PV systems, namely the stand‐alone system and the grid‐connected system. In each topology, the P‐PVE is cascaded to a maximum power point tracking Boost converter in order to track efficiently the maximum power point. In addition to the high performance offered by the FS‐MPC, the proposed control strategy allows to control all cascaded converters at the same time in one stage instead of controlling them separately, thus providing more flexibility and simple controllability. Extensive experimental results are done confirming the correctness and the effectiveness of the proposed CPC under hard climatic conditions, even in the presence of distorted grid voltage. [ABSTRACT FROM AUTHOR]

Many researches use T-S fuzzy models to accurately represent nonlinear dynamic systems. However, T-S fuzzy makes the implementation of fuzzy controller more complex as system order and nonlinearities increase. Thus, the present work is aimed to overcome these limitations by using an Interval Type-2 Fuzzy Rule-Based System in which the membership functions and the number of rules can be freely chosen simplifying the implementation of the technique. To this end, it is established a direct state feedback control with reference tracking to generate the nonlinear control action using parallel distributed compensation techniques with no need to include T-S fuzzy models to describe the dynamic system. The proposed strategy is applied to a synchronous generator and also to a magnetic levitation system. From the results, it was verified that IT2FRBSs are able to stabilize the systems analyzed at different equilibrium points with higher performance and less settling times, given the uncertainties in the linearized model. In fact, the IT2FRBS proved to be a proper way to accomplish this task, because fuzzy logic control itself does not depend on an accurate model. [ABSTRACT FROM AUTHOR]

In this work, the coupled control of the lateral and the longitudinal dynamics of an autonomous vehicle is addressed. As a first step, a multi-body modeling technique is used to develop a four wheeled vehicle planar model. This technique considers the vehicle as a robot consisting of articulated bodies. The geometric description of the vehicle system is derived using the modified Denavit–Hartenberg parameterization and then the dynamic model of the vehicle is computed by applying a recursive method used in robotics, namely Euler–Lagrange based Algorithm. The validation of the developed vehicle model was then conducted using an automotive simulator, the Scaner-Studio simulator. The developed vehicle model is then used to derive coupled control laws for the lateral and the longitudinal vehicle dynamics. Two coupled controllers are proposed: In the first controller, the control is realized using Lyapunov control techniques while in the second one an Immersion and Invariance with sliding mode approach is used. Both of the controllers aim to ensure a robust tracking of the reference trajectory and the desired speed while taking into account the strong coupling between the lateral and the longitudinal vehicle dynamics. In fact, the coupled controller is a key step for the vehicle safety handling, especially in coupled maneuvers such as lane-change maneuvers, obstacle avoidance maneuvers and combined maneuvers in critical driving situations. The developed controllers were validated in simulation under Matlab/Simulink using experimental data. Subsequently, an experimental validation of the proposed controllers was conducted using a robotized vehicle (Renault-ZOE) present in the Heudiasyc laboratory within the Equipex Robotex project. [ABSTRACT FROM AUTHOR]

In this paper, the practical mean-square convergence of active disturbance rejection control for a class of uncertain stochastic nonlinear systems modelled by the Itô-type stochastic differential equations with vast stochastic uncertainties is developed. We first design an extended state observer (ESO) to estimate both the unmeasured states and the stochastic total disturbance which includes unknown internal system dynamics, external stochastic disturbance without known statistical characteristics, unknown stochastic inverse dynamics, and uncertainty caused by the deviation of control parameter from its nominal value. The stochastic total disturbance is then cancelled (compensated) in the feedback loop. An ESO-based output-feedback control is finally designed analogously as for the system without uncertainties. The practical mean-square reference tracking and practical mean-square stability of the resulting closed-loop system are achieved. The numerical experiments are carried out to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]