Search

This paper addresses the control of a nonlinear system affected by deadzone effects, using a constrained actuator. The system itself incorporates a second-order oscillatory dynamic actuator, with an unknown nonlinear input-output relationship. The proposed algorithm not only accommodates the deadzone constraints on control inputs but also considers the actuator's saturation limits in control input calculations. It introduces a trajectory tracking mechanism that, instead of directly following the primary trajectory, adheres to an alternative trajectory capable of stable tracking, gradually converging to the main trajectory while accounting for operational constraints. In practical control systems, the actuator's input-output relationship is often nonlinear and unknown, requiring inversion for model-based control. This paper employs an offline-trained neural network trained on synthetic data to identify and approximate the actuator's behavior. To optimize the control system's performance and ensure stability during sudden error changes, the control input operates in two modes: position and velocity control. This dual-mode control allows for continuous switching between the two, facilitated by an innovative optimization technique based on the gradient descent method with a variable step size. Simulation results validate the effectiveness of the proposed algorithm in controlling systems constrained by hard limits and featuring nonlinear oscillatory actuators, providing a valuable contribution to the field of control systems. • Developing a dual mode non-predictive control algorithm based on discrete-time system equations. • Utilizing an offline-trained neural network for estimating the inverse of the nonlinear actuator relationship. • Stable desired trajectory mapping based on inherent and actuation constraints while maintaining asymptotic stability. • Substituting the trajectory within a fixed range outside of the constant deadzone and inside the variable saturation. • Optimized balancing position and velocity control modes through a weighted average of two control forces. [ABSTRACT FROM AUTHOR]

The safety, health, and well-being of human workers are crucial for socially sustainable production systems, especially in Industry 5.0. Occupational exoskeletons, particularly back-support devices, are increasingly being adopted to reduce musculoskeletal disorders and human fatigue. To reduce costs and weight, optimized exoskeleton design is being explored. A 3D-printed cycloidal reduction stage for the actuation unit is proposed, focusing on an interaction torque observer and an impedance-based controller for human-robot interaction. The device's dynamic characterization and control are analyzed to evaluate its applicability to a sensorless back-support occupational exoskeleton. [ABSTRACT FROM AUTHOR]

This paper focuses on the stable tracking control of the manipulator with constrained communication, unmeasurable velocity, and nonlinear uncertainties. An NN observer-depended output feedback scheme in the discrete-time domain is developed by virtue of the model-based dynamic event-triggered backstepping technique in the channel of sensor to controller. For generalizing the zero-order-holder (ZOH) implementation, a plant model is built to approximate the triggered states in the time flow, and according to which, the control law is fabricated. Based on model-based error events, we construct a dead-zone triggered condition with a dynamically adjustable threshold, making the threshold evolve with the system performance, to achieve flexible communication scheduling and avoid the accumulation of triggers in small tracking errors. The internal and external nonlinear uncertainties are online compensated by the neural network, and the aperiodic adaptive law is derived in the sense of control stability to save the computation. Finally, the conditions for semi-global ultimate uniform bounded (SGUUB) of all variables are given via impulse Lyapunov analysis, and a positive lower bound in the time interval between consecutive executions to guarantee the Zeno free behavior is obtained. Simulations are conducted on a three-link manipulator to illustrate the effectiveness of our method. • Adaptive model-based dynamic event-triggered feedback control NN is proposed. • A NN-based observer is applied to estimate the velocity. • Dead-zone triggered condition with dynamically threshold is constructed. • The internal and external nonlinear uncertainties are online compensated by our NN. • The aperiodic adaptive law is derived in the sense of control stability. [ABSTRACT FROM AUTHOR]

When in a digital control strategy there are samples lost due to limitations, different multirate (MR) control options can be used for solving the problem: Dual-rate inferential control (IC) and model-based dual-rate control (MBDR). The objective of this contribution is to analyze, compare, and to assess their behavior under different perspectives. Is a dual-rate inferential control better than a model-based dual-rate control? Both options lead to a periodically time-varying discrete-time system and for this reason a lifted modeling is considered. An efficient algorithm is used for computing a MR system's frequency response for these control structures. The robust performance and disturbance effects are studied in detail under sample losses and process uncertainty, and some considerations are reported. A new QFT (quantitative feedback theory) procedure for dual-rate systems analysis is also described. Analysis and simulation examples and experimental results for UGV path tracking are introduced in this work, revealing that MBDR outperforms IC when the model contains important uncertainties. • A frequency analysis is used to compare dual-rate strategies for slow measured systems. • New application of quantitative feedback theory techniques to dual-rate systems. • A discussion of robust stability and performance on practical problems is reported. • Under important model uncertainties, model-based dual-rate control is the best option. • Dual-rate control reveals energy saving in a low-cost mobile robot control application. [ABSTRACT FROM AUTHOR]

This paper presents a predictor based structure that is suitable to deal with both time-delayed and delay-free systems with saturating actuators. Such condition, well known in the literature to cause the windup phenomenon, is usually coped within the control area by techniques that commonly lead to the controller augmentation. However, it seems to exist a lack of simple approaches to deal with the design of linear controllers for both systems with and without delay. Thus, this paper addresses the saturation problem by both proposing a linear controller design and an associated anti-windup compensator design. The controller tuning is realized in three distinct phases, initially considering set-point tracking adjustment, then robustness and disturbance rejection tuning, and finally the associated anti-windup compensator synthesis. Furthermore, linear matrix inequalities (LMIs) which include both performance and stability requirements are employed for the anti-windup synthesis. The proposed controller presents better results when compared with recently published anti-windup controllers as well as a constrained MPC algorithm. Experimental results on a neonatal intensive care unit are also presented in order to validate the usefulness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract Model-based optimal control (MBOC) is a promising method to reap the potential of the waterside free cooling system in data center. While the theoretical energy savings are impressive, uncertainties in the model, sensors, and actuators could hinder this control strategy's practical application. This study quantified the impact of various uncertainties on the supervisory control method in two steps. First, sensitivity analysis was conducted using the Morris method and the one-at-a-time method to identify the influential impact of uncertain elements. Next, Monte-Carlo simulation was designed to perform an uncertainty study and evaluate the robustness and energy efficiency of the control method. A virtual emulator for simulating the proposed novel hybrid cooling system in data centers in combination with different supervisory control methods was developed. Full-year simulations indicate that the impact of uncertainty on the control performance of the MBOC strategy is greater than that of conventional control strategy; under various uncertainties, the energy consumption and operation mode prediction error rate of the MBOC method were increased by 43.6% and 99.2%, respectively. The research suggests that, if MBOC is adopted for the hybrid cooling system control, more efforts should be placed on reducing the uncertainties from various sources. Highlights • A model-based optimal supervisory control strategy for hybrid cooling plant using lake water source was developed. • The impacts of model uncertainties, sensor errors and execution errors on control performance were quantified. • The key impacts for control performance were identified based on the Morris and the one-at-a-time method. • Monte-Carlo simulation was used to evaluate the robustness and energy efficiency of the control method. [ABSTRACT FROM AUTHOR]

In this paper we propose a model-based scheme to control the depth of hypnosis in anesthesia that uses the BIS signal as controlled variable. In particular, the control scheme exploits the propofol pharmacokinetics/pharmacodynamics model of the patient so that the estimated effect-site concentration is used as a feedback signal for a standard PID controller, which compensates for the model uncertainties. The tuning of the parameters is performed off-line using genetic algorithms to minimize a performance index over a given data set of patients. The effectiveness of the proposed method is verified by means of a Monte Carlo method that takes into account both the intra-patient and inter-patient variability. In general, we obtain a fast induction phase with limited overshoot and a good disturbance rejection during maintenance of anesthesia. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for energy efficiency improvement of three-phase induction motors (TIM). The main control is based on the indirect field-oriented approach. A loss model-based controller is proposed in order to reduce the cooper and iron losses of TIM for different load values. A nonlinear equation based only on quadrature currents is derived and used to obtain suboptimal flux reference in steady-state conditions. Thus, for dynamic implementation (motor transient), a low-pass filter is included and designed based on the rotor time constant. MATLAB simulations are performed to test the proposed strategy for several operation conditions. To verify the approach in real situations, a 4hp motor drive platform was implemented using F28069 DSP. The effectiveness of the proposed model is demonstrated through simulations and experimental results. [ABSTRACT FROM AUTHOR]

Measures for an improved indoor climate control can potentially increase the energy efficiency in commercial buildings while comfort is maintained. Substantial energy reductions are possible by revising conventional building automation systems for an enhanced adaptation to process characteristics and current conditions. However, maintaining a low complexity to facilitate installation and commission of new technologies is also necessary to promote a widespread utilization. This work suggests a compromise between these two aspects by providing an experimental evaluation of a simplified model-based controller for indoor climate control in office environments. A working day was resembled in a test facility, and the task of the controller was to automate the heating, cooling and ventilation system in order to maintain a desirable indoor climate. The investigation considered two office environments, representing a meeting and an office room, as well as two weather seasons. From the investigation, it was concluded that the suggested model-based controller had the possibility of reducing energy usage and improve the indoor climate compared to a conventional feed-back controller as benchmark. But, these benefits were highly dependent on the investigated scenarios, which is important to consider in practice. [ABSTRACT FROM AUTHOR]

Solenoid current regulation is well-known and standard in any proportional electro-hydraulic valve. The goal is to provide a wide-band transfer function from the reference to the measured current, thus making the solenoid a fast and ideal force actuator within the limits of the power supplier. The power supplier is usually a Pulse Width Modulation (PWM) amplifier fixing the voltage bound and the Nyquist frequency of the regulator. Typical analog regulators include three main terms: a feedforward channel, a proportional feedback channel and the electromotive force compensation. The latter compensation may be accomplished by integrative feedback. Here the problem is faced through a model-based design (Embedded Model Control), on the basis of a wide-band embedded model of the solenoid which includes the effect of eddy currents. To this end model parameters must be identified. The embedded model includes a stochastic disturbance dynamics capable of estimating and correcting the electromotive contribution together with parametric uncertainty, variability and state dependence. The embedded model which is fed by the measured current and the supplied voltage becomes a state predictor of the controllable and disturbance dynamics. The control law combines reference generator, state feedback and disturbance rejection to dispatch the PWM amplifier with the appropriate duty cycle. Modeling, identification and control design are outlined together with experimental result. Comparison with an existing analog regulator is also provided. [ABSTRACT FROM AUTHOR]

In this paper, the method for simple additive modeling of the first principle model inaccuracies for the offset-free process control is presented. Starting from transformation of the general nonlinear state model into the input-affine dynamical equation describing directly the controlled variable, it is shown how to compensate for the potential modeling inaccuracies by lumping them into a single additive parameter. Its on-line estimation procedure based only on the measurement data collected from the process is very simple and effective and the estimate converges without any additional excitation of the process. The discussion on how to apply the suggested model as a basis for the chosen model-based control techniques is presented, and for the processes of the higher relative order, the practical simplification of this approach is shown. The experimental results show the practical applicability of the considered approach for the synthesis of the open loop Internal Model Controller (IMC) and of the Balance-Based Adaptive Controller (B-BAC). [ABSTRACT FROM AUTHOR]

Abstract: The operation of a CSP-plant is mainly driven by the available direct normal irradiation (DNI) over the day. Although the DNI is a volatile factor, it is very good predicable within a certain time horizon considering daytime, location and environmental boundary conditions. Aim of every plant control system is the stable and save operation of the CSP plant. One way to achieve a good control loop performance is to split the control problem into a feed forward and a feedback control system. Within this paper a feed forward control system for a direct steam generation CSP system with a mid and long term storage is developed. The feed forward control system is using an optimization based on a genetic algorithm. The behavior of the feed control system is shown with different examples, using different operation modes with focus on a flexible operation and on the maximization of the energy output. [Copyright &y& Elsevier]

Abstract: Multi-fingered robot hands have been one of the major research topics since holding an object and manipulating it, are crucial functionalities of several robotic systems, including service robots, industrial robots and wheel-type mobile robots. In this paper, we consider the problem of model-based control for a multi-fingered robot hand grasping an object with known geometrical characteristics. Two main topics are presented. Firstly, this paper attempts to derive a mathematical model of the dynamics of a designed multi-fingered robot hand with five fingers with twenty DOF (three for each finger, two for the thumb and six for the wrist) which grasps a rigid object. We exploit our methodology based on the Lagrange formulation, which is presented in previous work, to identify the parameters of dynamic models of hand-object system. These models are instrumental for the design problems of control for dynamic stable grasping that is discussed on second part. Finally, several simulation results demonstrate the controller performance based on the derived model. [Copyright &y& Elsevier]

Abstract: This paper discusses the challenges to net zero energy houses to the next level, which is to take them off the grid. This raises the stakes significantly as there is no reliance on the infinite storage that the grid provides, but only on the local energy storage. Overall reliability and resilience are the basic demands that need to be satisfied by the design and system sizing. We investigate a special aspect of power reliability, arguing that maintaining thermal comfort and guaranteeing power availability will be the two most important performance indicators in an off-grid house design. Due to the dynamic characteristic of a renewable energy generation system it is often difficult to maintain a stable thermal comfort level without investing in an oversized renewable power system. This paper presents a stochastic model based energy management system that distributes energy based on acceptable ranges of thermal comfort levels and manages electricity storage in batteries, driven by the projected energy demand and the projected energy generation. The objective of the stochastic model based control is to guide system operation and reach a balance between acceptable thermal comfort and general power reliability in an off-grid residence. Simulation results indicate that the proposed stochastic model based controller decreases the risk of power unavailability by up to 24% without sacrificing indoor thermal comfort dramatically. A follow-up sensitivity analysis demonstrates versatilities of the controller in practical application. Certain limitations in its application are discussed at the end of this paper. [Copyright &y& Elsevier]

The paper deals with a novel application of the wireless data communication for a continual control of the distributed manipulation systems. The solution is intended for industrial robotic plants. A considered way of the wireless communication is based on ZigBee protocol. The protocol is tested for a real time bidirectional data communication within a manipulation system. The system consists of several moving manipulation units, several stationary auxiliary units and one control computer. The computer provides the cooperation of all units in the system in relation to the user requirements. The system is controlled by a simple feedback multi-level control realized in MATLAB - Simulink environment. The paper is focused on the realization of the boards of power electronics, transmitters, optical positional sensors, optical gates and their networking in accordance with ZigBee protocol definition. The behavior of the ZigBee is illustrated by several records from real experiments. [ABSTRACT FROM AUTHOR]

Copyright of Wood Industry / Drvna Industrija is the property of Drvna Industrija and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Organizacija is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: This paper describes the development and evaluation of a model-based approach for minimizing peak cooling demand using energy storage inherent in building structures. On any day where the strategy is invoked, the building is precooled with zone temperature setpoints at the low end of comfort prior to a demand-limiting (DL) period. The zone temperatures are then adjusted upwards during the demand-limiting period following a trajectory that keeps the peak cooling load below a specified target. The cooling demand target and setpoint trajectory are determined using a building model that is trained using field data. The overall approach was demonstrated for a building representative of a small commercial facility. The first step involved training the inverse model using a few weeks of hourly data. The model was then used to study the potential for peak load reduction and to determine setpoint trajectories that were implemented at the site. The demand-limiting strategy resulted in approximately 30% reductions in peak cooling loads compared to night setup control for a 5-h on-peak period of 1 PM to 6 PM. [Copyright &y& Elsevier]

Abstract: This paper presents an in situ calibrated model-based approach to the control of natural ventilation in buildings. Specifically, the potential of empirically-based and numeric air flow models is explored as part of a hybrid building controls scheme. Toward this end, two case studies are described. In one case, the underlying predictive model is based on the statistical treatment of data obtained from a set of in situ measurements in a typical office space. In the second case, a multi-zone air flow model is used to predict the fresh air volume flows into the building interior. The resulting air flow models can be used as an integrated part of a control method for natural ventilation devices to predict the implications of alternative control options. [Copyright &y& Elsevier]

Different predictive control strategies have been validated on a DC motor. ICascaded predictive control, which consists of a cascaded loop where the traditional servo algorithms are replaced by PFCs, could enhance the cycle time. Predictive Functional Control alone is simpler to tune and can exhibit comparable performances, except that the controller is more sensitive to nonlinear phenomena such as dry friction, which were not taken into account into the model and generate a static error. [ABSTRACT FROM AUTHOR]

Parallel structure robots have been receiving growing attention from both academia and industry in recent years. This is due to their advantages over serial structure robots, such as high stiffness, high motion accuracy and a high load–structure ratio. Control of parallel robots, however, produces difficulties to control engineers due to the modelling errors arising from the highly nonlinear and complex structures. This paper proposes a dual-model-based structure for error attenuation in the trajectory-tracking control of a parallel robot manipulator. In this design, a conventional model-based control algorithm employing an estimated robot dynamic model is first implemented in the inner loop of the control structure. Then, in order to reduce the unwanted effects caused by modelling errors, another model-based structure, developed based on the concept of the internal model control, is appended in the outer loop of the control structure as a compensator. A combination of these two model-based components results in a novel dual-model-based structure for parallel robot control. Sensitivity analyses show that the effects due to modelling errors and external disturbances can be significantly reduced by applying this new control structure without relying on a high-gain control solution. The effectiveness of this control design is successfully demonstrated by numerical studies on a planar parallel robot with 2 degrees of freedom. [ABSTRACT FROM AUTHOR]

An automated shading system is expected to effectively prevent visual discomfort associated with glare while providing adequate daylight penetration. However, the concept of visual comfort has not been well integrated into existing commercial shading systems, leaving potential for visual discomfort and resulting in occupants' dissatisfaction. Meanwhile, advanced methods in the academic literature are not always suitable for industrial applications due to problems associated with intensive real-time computation or privacy concerns. This research aims to address this gap by developing a simulation-assisted data-driven method for glare control with automated shades. The proposed strategy utilizes data from pre-simulated daylight analyses to develop glare predictive models using machine learning algorithms. With real-time solar irradiance measurements and the sun position as the input variables to feed the predictive models, the control algorithm can predict the glare condition and set the shades to an appropriate position that maximizes daylight ingress without causing glare. The presented method was verified using climate-based simulation to adjust the slat angle of automated venetian blinds in an office building. Its performance was compared to that of the conventional cut-off angle control for glare elimination, lighting use savings, and view access. The results showed that the proposed strategy was able to prevent 86.5%–96.9% of the glare and potentially reduce lighting energy use by 80.8% while the cut-off control only resulted in 28.9% glare elimination and 67.6% lighting energy savings. The presented method also allowed for unobstructed view more frequently, outperforming the cut-off angle control in all the examined categories. • A simplified glare-based shading control strategy is proposed. • Data-driven predictive models developed from pre-simulated data are used for real-time shading control. • Simple machine learning classification models can predict daylight glare with high accuracy. • The proposed method outperforms the conventional cut off angle control. • The use of indoor sensors is eliminated and the use of outdoor sensors is minimized. [ABSTRACT FROM AUTHOR]

Addressing both daylight maximization and glare control over the entire workplace is always challenging for developing the automated shading control system. For the sake of cost and space usage, it is impractical to mount multiple sensors or cameras for real-time daylight environment monitoring to guarantee the control precision. Cut-off control is popular while it cannot attenuate the glare caused by excessive diffuse daylight. This paper introduces a model-based shading control for predetermining shading positions at each time step. A Useful Daylight Illuminance paradigm modality called rUDI is proposed as a variable criterion added to assist the cut-off strategy for further eliminating glare. The controller could be developed through real-time daylight simulations and an optimizer based on the surrogate model. This method was implemented in a full-scale office in Harbin, China. The surrogate model grounded on the Radial Basis Function Neural Network (RBF) was trained, validated and test with the experimental data sets. The control strategy was further incorporated with an adaptive light-switch model. The comparative simulations were conducted, and their corresponding results were generated for evaluating their performance in visual comfort, daylighting and electrical energy savings, demonstrating the advantages of the proposed control approach in terms of its adequate performance. • A simplified controller with better daylighting performance based on simulation modelling is proposed for automated blinds. • A new metric concerning the dynamic illuminance distribution is proposed for automated blinds controller. • An optimizer derived from a machine learning algorithm is proposed for achieving predictive control. • Optimizing the indoor horizontal illuminance can effectively alleviate the glare caused by the intense diffuse daylight. [ABSTRACT FROM AUTHOR]

• Model predictive control of total intravenous anesthesia with propofol and remifentanil. • Both analgesic and hypnotic statuses of patient are controlled. • In silico simulation via physiologically-based pharmacokinetic and pharmacodynamic models to investigate inter-patient variability. • Impact of surgical stimuli and noise of monitored variables are assessed. • A real-time decision-making system to support anesthesiologists. The application of closed-loop control systems in biomedicine unlocks prospects for optimized drug delivery based on the measurement of patients' physiological variables. However, inter-individual variability and narrow therapeutic indexes are issues that must be carefully considered. We propose an in silico study of a model-based controller of anesthesia and analgesia with propofol and remifentanil, based on bispectral index (BIS) and mean arterial pressure (MAP) measurements. A physiologically-based pharmacokinetic (PBPK) model, combined with a suitable pharmacodynamic model, allows describing and differentiating the dose-effect dependency for the virtual patients. The controller delivers a safe and fast induction of anesthesia, with mean rise times below 3 min and controlled variables within the clinical safe ranges. The PBPK model allows gaining complementary information about the dynamics of the drugs absorption, distribution, metabolism, and elimination in the body. Special attention is devoted to simulate realistic intraoperative surgical stimuli and noise on the controlled variables. The controller successfully rejects disturbances on BIS and MAP related to nociceptive stimuli (e.g., intubation and incision) via a robust control action, and is not diverted by noise. [ABSTRACT FROM AUTHOR]