Optimized model-based control of main mine ventilation air flows with minimized energy consumption

In early 2018,the Boliden Garpenberg operation implemented an optimized control strategy as an addition to the existing ventilation on demand system.The purpose of the strategy is to further minimize energy use for main and booster fans,whilst also fulfilling airflow setpoints without violating constraints such as min/max differential pressure over fans and interaction of air between areas in mines.Using air flow measurements and a dynamical model of the ventilation system,a mine-wide coordination control of fans can be carried out.The numerical model is data driven and derived from historical operational data or step changes experiments.This makes both initial deployment and lifetime model maintenance,as the mine evolves,a comparably easy operation.The control has been proven to operate in a stable manner over long periods without having to re-calibrate the model.Results prove a 40%decrease in energy use for the fans involved and a greater controllability of air flow.Moreover,a 15%decrease of the total air flow into the mine will give additional proportional heating savings during winter periods.All in all,the multivariable controller shows a correlation between production in the mine and the ventilation system performance superior to all of its predecessors.

Electronic Throttle Control System: Modeling, Identification and Model-Based Control Designs

Electronic throttle control (ETC) system has worked its way to becoming a standard subsystem in most of the current automobiles as it has contributed much to the improvement of fuel economy, emissions, drivability and safety. Precision control of the subsystem, which consists of a dc motor driving a throttle plate, a pre-loaded return spring and a set of gear train to regulate airflow into the engine, seems rather straightforward and yet complex. The difficulties lie in the unknown system parameters, hard nonlinearity of the pre-loaded spring that pulls the throttle plate to its default position, and friction, among others. In this paper, we extend our previous results obtained for the modeling, unknown system parameters identification and control of a commercially available Bosch’s DV-E5 ETC system. Details of modeling and parameters identification based on laboratory experiments, data analysis, and knowledge of the system are provided. The parameters identification results were verified and validated by a real-time PID control implemented with an xPC Target. A nonlinear control design was then proposed utilizing the input-output feedback linearization approach and technique. In view of a recent massive auto recalls due to the controversial uncontrollable engine accelerations, the results of this paper may inspire further research interest on the drive-by-wire technology.

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Model-Based Systems Engineering Approach to Design a Human Settlement to Better Serve Displaced People

The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.

Model-based adaptive locomotion and clustering control of microparticles through ultrasonic topological charge modulation

We present a novel motion control technique for microrobot clusters to exploit the characteristics of the ultrasonic field.The method comprises two steps,i.e.,introducing an ultrasonic actuation(UA)linear model for three-dimensional(3D)locomotion and controlling the topological charge(TC)in the ultrasonic vortex for microrobot clustering.Here,the TC is a controllable parameter for the expansion and contraction of the pressure null space inside the vortex.We present a TC control method to cluster sporadically distributed microrobots in a specific workspace.To validate the concept,a UA system composed of 30 ultrasonic transducers with 1 MHz frequency is fabricated,and the characteristics of the generated acoustic pressure field are analyzed through simulations.Subsequently,the performances of the adaptive controller for precise 3D locomotion and the TC control method for clustering are evaluated.Finally,the UA technology,which performs both clustering and locomotion in a complex manner,is validated with a gelatin phantom in an in-vitro environment.

Virtual sample generation for model-based prognostics and health management of on-board high-speed train control system

In view of class imbalance in data-driven modeling for Prognostics and Health Management(PHM),existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment.A virtual sample generation solution based on Generative Adversarial Network(GAN)is proposed to overcome this shortcoming.Aiming at augmenting the sample classes with the imbalanced data problem,the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models.Under the PHM framework of the on-board train control system,the virtual sample generation principle and the detailed procedures are presented.With the enhanced class-balancing mechanism and the designed sample augmentation logic,the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status.Practical data from a specific type of on-board train control system is employed for the validation of the presented solution.The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance(CBM)operations.

Statistical key variable analysis and model-based control for improvement performance in a deep reactive ion etching process

This paper proposes to develop a data-driven via＇s depth estimator of the deep reactive ion etching process based on statistical identification of key variables.Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology（VM） model building process.With the available on-line VM model,the model-based controller is hence readily applicable to improve the quality of a via＇s depth.Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method.The results demonstrate that the proposed method can decrease the MSE from 2.2×10^（-2） to 9×10^（-4） and has great potential in improving the existing DRIE process.

Feedback Scheduling of Model-based Networked Control Systems with Flexible Workload

In this paper,a novel control structure called feedback scheduling of model-based networked control systems is proposed to cope with a flexible network load and resource constraints.The state update time is adjusted according to the real-time network congestion situation.State observer is used under the situation where the state of the controlled plant could not be acquired.The stability criterion of the proposed structure is proved with time-varying state update time.On the basis of the stability of the novel system structure,the compromise between the control performance and the network utilization is realized by using feedback scheduler. Examples are provided to show the advantage of the proposed control structure.

On the model-based networked control for singularly perturbed systems

In this paper, the control of a two-time-scale plant, where the sensor is connected to a linear controller/ actuator via a network is addressed. The slow and fast systems of singularly perturbed systems are used to produce an estimate of the plant state behavior between transmission times, by which one can reduce the usage of the network. The approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are generally asymptotically stable as long as their slow and fast systems are both stable. These results are also extended to the case of network delay.

Adaptive Robust Motion Trajectory Tracking Control of Pneumatic Cylinders with LuGre Model-based Friction Compensation

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation（RLSE） to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

Model-Based Fault Tolerant Control for Hybrid Dynamic Systems with Sensor Faults

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Formal management-specifying approach for model-based safety assessment

In the field of model-based system assessment,mathematical models are used to interpret the system behaviors.However,the industrial systems in this intelligent era will be more manageable.Various management operations will be dynamically set,and the system will be no longer static as it is initially designed.Thus,the static model generated by the traditional model-based safety assessment(MBSA)approach cannot be used to accurately assess the dependability.There mainly exists three problems.Complex:huge and complex behaviors make the modeling to be trivial manual;Dynamic:though there are thousands of states and transitions,the previous model must be resubmitted to assess whenever new management arrives;Unreusable:as for different systems,the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same.Motivated by solving the above problems,this research studies a formal management specifying approach with the advantages of agility modeling,dynamic modeling,and specification design that can be re-suable.Finally,three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.

Characteristic Model-based Discrete-time Sliding Mode Control for Spacecraft with Variable Tilt of Flexible Structures

In this paper, the finite-time attitude tracking control problem for the spacecrafts with variable tilt of flexible appendages in the conditions of exogenous disturbances and inertia uncertainties is addressed. First the characteristic modeling method is applied to the problem of the spacecraft modeling.Second, a novel adaptive sliding mode surface is designed based on the characteristic model. Furthermore, a discrete-time sliding mode control(DTSMC) law, which makes the tracking error converge into a predefined bound in finite time, is proposed by employing the parameters of characteristic model associated with the sliding mode surface to provide better performances,robustness, faster response, and higher control precision. The designed DTSMC includes the adaptive control architecture and is chattering-free. Finally, digital simulations of a sun synchronous orbit satellite(SSOS) are presented to illustrate effectiveness of the control strategies as well as to verify the practical feasibility of the rapid maneuver mission.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Mitochondrial dysfunction and quality control lie at the heart of subarachnoid hemorrhage

The dramatic increase in intracranial pressure after subarachnoid hemorrhage leads to a decrease in cerebral perfusion pressure and a reduction in cerebral blood flow.Mitochondria are directly affected by direct factors such as ischemia,hypoxia,excitotoxicity,and toxicity of free hemoglobin and its degradation products,which trigger mitochondrial dysfunction.Dysfunctional mitochondria release large amounts of reactive oxygen species,inflammatory mediators,and apoptotic proteins that activate apoptotic pathways,further damaging cells.In response to this array of damage,cells have adopted multiple mitochondrial quality control mechanisms through evolution,including mitochondrial protein quality control,mitochondrial dynamics,mitophagy,mitochondrial biogenesis,and intercellular mitochondrial transfer,to maintain mitochondrial homeostasis under pathological conditions.Specific interventions targeting mitochondrial quality control mechanisms have emerged as promising therapeutic strategies for subarachnoid hemorrhage.This review provides an overview of recent research advances in mitochondrial pathophysiological processes after subarachnoid hemorrhage,particularly mitochondrial quality control mechanisms.It also presents potential therapeutic strategies to target mitochondrial quality control in subarachnoid hemorrhage.