Presence Of Uncertain Parameters Research Articles

Optimal operation of multi-carrier energy systems considering demand response: A hybrid scenario-based/IGDT uncertainty method

The optimal operation of multi-carrier energy systems (MCESs) has opened new horizons for energy network management and the satisfaction of consumers. In this paper, the optimization of the MCES's operation cost is considered by combining several energy hubs (EHs). To make optimal use of thermal and electrical demand response programs (TDRPs and EDRPs), the cost factors are extracted using a new sensitivity analysis (SA) method. Then, taking into account the optimized cost coefficients in EDRP and TDRP, the optimal operation of MCES is investigated in the presence of uncertain parameters. In this paper, a two-stage uncertainty method is used for solving the uncertain problem. First, the uncertainty of wind, photovoltaic (PV), and electrical and thermal loads is modeled with a scenario-based method, in the second step, the price uncertainty of generated electricity is added to the previous model using the information gap decision theory (IGDT). A hybrid scenario-based/IGDT uncertainty has been performed simultaneously at the GAMS platform. The results show the impact of each uncertainty parameter and system resilience due to the optimization done in the problem and the EDRP and TDRP programs. The results show about 135$ difference between the lowest and highest scenarios at a total operation cost.

Reactive power management in distribution networks in the presence of distributed generation sources based on information gap decision theory

The presence of uncertain parameters in power systems has led to many challenges for the designers and operators of these systems. One of these challenges is reactive power management in the presence of distributed renewable generation sources.In this article, the management of reactive power in distribution networks in the electricity market and the presence of distributed renewable generation sources, including wind and solar power plants, is performed considering the uncertainties in the network load, power generation of distributed generation sources, and active and reactive power market prices. Furthermore, reactive power cost modeling of reactive power compensation equipment is carried out.A hybrid stochastic/robust optimization method is employed to model the uncertainties in the problem. Finally, the efficiency of the method is confirmed by numerical examinations using the IEEE 33-bus distribution network and the GAMS optimization software. Simulation results indicate that in the risk-averse strategy, for a certain increase in cost, the radius of uncertainty in the active and reactive power market prices increases. Also, in this strategy, as β increases, the total cost of network operating increases by 81.72 %, while in a risk-seeking strategy, with the increase of β, the total operating cost of the network decreases by 77.78 %.

Robust Control of Motion in Presence of Uncertain Parameters and Control Constraints

The paper describes a novel, simple servo drive position controller, using only knowledge about the structure of the nonlinear model and the constraints met by individual components of the model. The desired behaviour of the position and velocity signals is obtained by imposing a time-varying constraint on the signal aggregating information about position and velocity tracking errors. The method allows you to determine the maximum control (servo drive current) necessary to achieve the control goal under the existing initial conditions and the selected reference trajectory. The control is constrained and consists in appropriate reaction when the trajectory approaches the barrier, the shape of which is responsible for the imposed properties of the transient and quasisteady state tracking error. In addition to the derivation of the control, a discussion of its possible variants and basic properties is presented. Control with time-varying constraints has been introduced, which allows the control objectives to be met with limited conservatism of the imposed constraints. The influence of technical factors related to actual speed and position measurements was discussed and the operation of the real drive on a laboratory stand was presented.

The Impact-driven Atmospheric Loss of Super-Earths around Different Spectral Types of Host Stars

A planet’s mass loss is important for the its formation and evolution. The radius valley (RV) is believed to be triggered by evaporation-induced mass loss. As an alternative mechanism for RV, the mass loss of post-impact planets is thoroughly investigated in this work. The impact energy is converted to the planet’s internal energy, enhancing its core energy and accelerating mass loss and orbital migration. As the host star changes from K type to F type, the planet’s mass loss and orbital migration increase. When the initial gas-to-core-mass ratio is small, the migration efficiency for planets around K-type stars will increase, which helps to suppress mass loss and retain the planet’s mass and radius within a specific range. On the contrary, planets around more massive F-type stars experience more substantial mass loss, potentially leading to complete mass loss, and migrate to orbits with longer periods. Our calculation shows that planets around different spectral types of host stars give rise to an RV ranging from 1.3 to 2.0 R ⊕, consistent with the observed range of 1.3–2.6 R ⊕. Despite the presence of uncertain parameters, the planetesimal impact can promote the RV establishment for planets around host stars of different spectral types.

A Discrete-Event Simulation Study of Multi-Objective Sales and Operation Planning Under Demand Uncertainty: A Case of the Ethiopian Automotive Industry

Sales and operation planning is one of the major categories of supply chain planning that enables the balancing of demand with supply through the integration of internal functions as well as external supply chain members. The major issues are its large scale, dynamic, multi-objective nature and presence of uncertain parameters. Handling uncertainty, extending the level of integration, enhancing collaboration, and contextualization of the models already developed in different industrial situations are the gaps identified in the literature. This research aims to develop a discrete-event simulation model from the literature in the context of the Ethiopian automotive industry and extend the level of collaboration to suppliers and customers. The industry’s sales and operation planning process is surveyed to develop the model as a decision support system that can be utilized for understanding the system behavior, evaluation of manufacturing flexibility, and inventory control policies. The research findings demonstrate that the customer service level and total profit can be significantly improved through the proposed joint primary and negotiated backup supply policy with price revision. Managerial implications that are expected to improve the technical capability of Ethiopian automotive industries are also highlighted.

Learning From Human Demonstrations for Wheel Mobile Manipulator: An Unscented Model Predictive Control Approach.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

Automotive disc-brake squeal noise modeling for shape optimization under uncertainties

Disc-brake systems in automotive vehicles tend to exhibit undesirable squeal noise during braking operations. Although this noise does not affect braking efficiency, it creates noise pollution and poses problems from a sales point of view since noise is commonly associated with malfunction by consumers. Squeal noise can be minimized by optimizing the shape of the system's mechanical parts. However, uncertainties related to manufacturing processes inevitably affect the system's response, and must be considered. The presented work aims at numerically quantifying and minimizing disc-brake squeal noise in the presence of uncertain parameters. A simplified disc-brake model is constructed using the novel Isogeometric Analysis method which is well-suited to shape optimization problems. The prediction of squeal noise propensity is based on the fact that squeal noise is directly related to unstable structural modes and uses Complex Eigenvalue Analysis to determine the extent of squeal occurrences. A surrogate-based optimization strategy is used to handle the high computational cost in the context of optimization, as well as to propagate uncertainties. The constructed model shows good capability for representation and quantification of squeal noise phenomena. A Pareto front of robust squeal minimizing shapes is obtained through the proposed approach.

Adaptive super-twisting control for leader-following consensus of second-order multi-agent systems based on time-varying gains

In this paper, robust distributed consensus control is designed based on adaptive time-varying gains for a class of nonlinear multi-agent systems (MAS) in the presence of uncertain parameters and external disturbances with unknown upper bounds. Due to various conditions and constraints, different dynamical models for the agents can be considered in practice. On the basis of a continuous homogeneous consensus method which has been proposed for the nominal nonlinear MAS, the discontinuous and continuous adaptive integral sliding mode control strategies are particularly designed and extended to accomplish exact and precise consensus for non-identical MASs influenced by imposed perturbations. However, it is noted that in practical problems, the exact upper bound of perturbations is unknown. Then, the proposed controllers have been improved in an adaptive scheme to overcome this drawback. In addition to the adaptive estimation strategy and time-varying gains, which address considered uncertain parameters in the dynamics of the following agents, the designed distributed super-twisting sliding mode strategy for nonlinear agents adjusts the gain of the control inputs and guarantees that the proposed protocol performs properly without any setbacks of the chattering phenomenon. The illustrative simulations depict the robustness, accuracy, and effectiveness of the designed methods.

Joint chance-constrained multi-objective multi-commodity minimum cost network flow problem with copula theory

This study focuses on a specific problem in network optimization, namely the minimum cost multi-commodity network flow (MCNF) problem. The problem is complicated by the presence of uncertain parameters, including various types of costs associated with each arc in the network. The study presents a multi-objective approach to solving this problem, where the coefficients of the capacity constraints are modelled as random variables with a normal distribution, and the dependence between them is modelled using an Archimedean copula. The capacity constraints are presented as joint chance constraints, and a multi-objective problem is formulated to deal with the uncertainty. This uncertain multi-objective problem is then converted into a certain multi-objective problem using fuzzy programming. The resulting certain multi-objective MCNF problem is converted to a certain single objective problem using second-order cone programming (SOCP), which is solved using either piecewise tangent approximation or piecewise linear approximation methods. The proposed approaches are tested using numerical examples and experimental tests to demonstrate their effectiveness in solving large-scale network problems efficiently. The results show that the proposed approaches are a useful tool for solving uncertain multi-objective MCNF problems in real-world applications.

Input reduction for nonlinear thermal surface loads

A multiplicity of simulations is required to optimize systems with thermal transient processes in the presence of uncertain parameters. That is why model order reduction is applied to minimize the numerical effort. The consideration of heat radiation and convection with parameter-dependent heat transfer coefficients results in a nonlinear system with many inputs as these loads are distributed over the whole surface limiting the attainable reduced dimension. Therefore, a new input reduction method is presented approximating the input matrix based on load vector snapshots using singular value decomposition. Afterward, standard reduction methods like the Krylov subspace method or balanced truncation can be applied. Compared to proper orthogonal decomposition, the number of training simulations decreases significantly and the reduced-order model provides a high accuracy within a broad parameter range. In a second step, the discrete empirical interpolation method is used to limit the evaluation of the nonlinearity to a few degrees of freedom and proper orthogonal decomposition allows the fast adaptation of the emissivity. As a result, the reduced system becomes independent of the original dimensions and the computation time is reduced drastically. This approach enables an optimal method combination depending on the number of simulations performed with the reduced model.

Pacejka-Like Curve-Based Speed Reference Generators for Electric Vehicles Powered by in-Wheel Motors

Recently, motor speed reference generators have been designed for the cruise control of electric vehicles with either centralized electric motors (straight manoeuvres) or in-wheel motors (bend manoeuvres with sufficiently small constant steering angles). Steady-state operation at a safe (though conservative) tire longitudinal slip can be achieved, with no a priori knowledge regarding the occurrence of a specific external condition. Here, an innovative solution is presented. It is based on an ingenious use of Pacejka-like curves – representing the torque current-slip characteristics of the vehicle – to design a new contraction-mapping-based automatic tuning procedure for the longitudinal velocity of the vehicle. Such an innovative procedure overcomes the highly conservative nature of the previous approach in terms of unduly small longitudinal velocities (and yaw rates) under relatively favourable external conditions while guaranteeing a safe operating condition close to the maximum of the torque-slip characteristics with no knowledge of the tire-road adhesion coefficient. Comparative CarSim simulations illustrate the effectiveness of the proposed approach, in the presence of uncertain parameters and complex vehicle dynamics that are neglected at the control design stage.

Optimal stochastic operation of technical virtual power plants in reconfigurable distribution networks considering contingencies

Virtual Power Plants (VPPs) are one of the concepts introduced in modern power systems to handle the increasing number of the distributed generation (DG) units. Technical VPPs (TVPPs) consider both financial and technical perspectives of using DGs in the system. Besides, secure and reliable operation of the system is a priority. In this paper, optimal operation of technical virtual power plants in a reconfigurable network is formulated as an optimization problem to resolve the probable contingency problem in the lines of the system. The VPP is assumed to be a multi-carrier energy system including combined heat and power (CHP), renewable DGs and dispatchable DGs beside thermal and electrical storage systems and loads. The uncertainties of renewable based DGs and demand levels are handled using chance constrained programming (CCP). By using CCP in presence of uncertain parameters, the security of the system can be guaranteed in predefined level of probability. Finally, to evaluate the effectiveness, quality and applicability of the proposed methodology, the problem is structured as a mixed-integer nonlinear programming (MINLP) problem which is solved using General Algebraic Modeling System (GAMS) software via Baron solver.

Adaptive Fuzzy Observer Control for Half-Car Active Suspension Systems with Prescribed Performance and Actuator Fault

In this paper, an adaptive fuzzy observer-based fault-tolerant controller is designed for a half-car active suspension system under the presence of uncertain parameters, unknown masses of passengers, and actuator failures. To improve the control performance, fuzzy logic systems (FLSs) are employed to approximate the unknown functions caused by uncertain dynamics of the suspension system. Then, an adaptive control design is developed to compensate for the effects of a non-ideal actuator. To improve passenger comfort, both vertical and angular motions are guaranteed simultaneously under the predefined boundaries by the prescribed performance function (PPF) method. Besides, the objectives of handling stability and driving safety are also considered to enhance the suspension performance. The system stability is proved according to the Lyapunov theory. Finally, the effectiveness of the developed approach is evaluated by comparative simulation examples on the half-car model. The simulation results show that the proposed control can improve the suspension performance as the RMS acceleration value is decreased by 68.1%.

Robust ellipsoidal set-membership fault estimation for time-varying systems with uniform quantization effects over sensor networks.

This article is concerned with the robust set-membership fault estimation problem for a class of uncertain discrete time-varying systems over sensor networks. The system measurements are subject to the uniform quantization which results in the unknown-but-bounded noises. Attention is focused on the design of a set-membership fault estimator such that, in the simultaneous presence of uncertain parameters, unknown-but-bounded noises and uniform quantization effects, the estimation errors are confifined to a certain ellipsoidal region. By using the mathematical induction, a suffificient condition is derived for the existence of the desired fault estimator at each time step in terms of a set of recursive matrix inequalities. Moreover, an optimization problem is formulated by minimizing the ellipsoid of the estimation error. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed fault estimator design scheme.

State recovery and disturbance estimation-based fast trajectory tracking of autonomous surface vehicles: A finite-time approach

This paper investigates the problem of finite-time trajectory tracking for fully actuated autonomous surface vehicles without the velocity measurements, in the presence of uncertain parameters and complex environmental disturbances. Firstly, a generalized nonlinear finite-time extended state observer is proposed to recover the velocities and synthetic disturbances simultaneously. The generalized structure can further enhance the estimation accuracy and the observation errors are proved to be finite-time uniformly ultimately bounded stable. Then, a two-loop control law is developed by using virtual velocity command and fast non-singular integral terminal sliding controller. The observer is integrated with the controller to overcome the problem of unavailable surge, sway, yaw velocities and to rapidly reject disturbances, thereby contributing to fast and accurate trajectory tracking of the vehicle. Theoretical analysis for finite-time stability of the closed-loop system is rigorously derived through the Lyapunov’s stability theory. Finally, comparative simulation results are given to illustrate the effectiveness and superiority of the proposed control approach.

Tube-model predictive control based on sum of squares for hypersonic vehicle with state-dependent input constraints

In this paper, tube-model predictive control based on the sum of squares technique is developed for hypersonic vehicles with state-dependent input constraints. Firstly, the longitudinal non-linear model in the presence of uncertain parameters is transformed into the polytopic linear parameter varying model with bounded disturbance by feedback linearization. Then the actual input constraints are converted to the virtual state-dependent input constraints in linear multivariable polynomial. A composite feedback control law based on tube-model predictive control is designed into a convex combination of unconstrained and constrained control. The real control law can be obtained by inversion. The sum of squares technique is used to transform the polynomial constraints into the convex matrix sum of squares condition via linear matrix inequality. Finally, simulation results verify the effectiveness of the proposed controller.

Controlling network ensembles

The field of optimal control typically requires the assumption of perfect knowledge of the system one desires to control, which is an unrealistic assumption for biological systems, or networks, typically affected by high levels of uncertainty. Here, we investigate the minimum energy control of network ensembles, which may take one of a number of possible realizations. We ensure the controller derived can perform the desired control with a tunable amount of accuracy and we study how the control energy and the overall control cost scale with the number of possible realizations. Our focus is in characterizing the solution of the optimal control problem in the limit in which the systems are drawn from a continuous distribution, and in particular, how to properly pose the weighting terms in the objective function. We verify the theory in three examples of interest: a unidirectional chain network with uncertain edge weights and self-loop weights, a network where each edge weight is drawn from a given distribution, and the Jacobian of the dynamics corresponding to the cell signaling network of autophagy in the presence of uncertain parameters.

Global Distributed Attitude Tracking Control of Multiple Rigid Bodies via Quaternion-Based Hybrid Feedback

This article investigates the leader-following attitude tracking control problem of multiple rigid body systems in the presence of uncertain parameters and disturbances. The communication constraint is considered that only a subset of followers have access to the leader, and a quaternion-based nonlinear hybrid distributed observer is first proposed to estimate the leader's trajectory for each follower. In particular, the proposed distributed attitude observer always evolves on the 3-D unit sphere $\mathbb {S}^{3}$ . This property guarantees the feasibility of the observer-based distributed control scheme, since the separation principle is satisfied. By incorporating a hysteresis-based switch of a binary logic variable for each pair of neighboring rigid bodies, the proposed hybrid distributed observer achieves the global asymptotic stability for any initial attitude and avoids the unwinding phenomenon. Next, an observer-based attitude tracking control law is developed by combining adaptive technique and hybrid theory, which does not require precise model information and has good robustness to the disturbances. In addition, it is shown that the attitude tracking errors converge to the origin globally asymptotically. Finally, simulation results are provided to validate the theoretical results.

Adaptive Finite-Time Containment Control of Uncertain Multiple Manipulator Systems.

This article is concerned with the containment control of multiple manipulators with uncertain parameters. A novel distributed adaptive backstepping strategy is given in the finite-time control framework. The finite-time command filters (FTCFs) used in the strategy can avoid the explosion of complexity problem for conventional backstepping. To further improve the control performance, the filtering errors caused by the used FTCFs are removed by using the error compensation mechanism (ECM). The proposed virtual control signal, the control torque, and the adaptive updating law can guarantee the set tracking errors converge to an adjustable neighborhood of the origin in finite time in the presence of uncertain parameters. Because the virtual control signal and ECM only use the local information, the established method is completely distributed. Two simulation examples are given to show the effectiveness of the proposed scheme.

Adaptive control of coupled dynamical systems: a mixed state and output feedback framework

This paper studies a class of coupled dynamical systems consisting of an actuated dynamical system interconnected with an unactuated dynamical system. The actuated dynamical system is considered to have a measurable state for feedback control, whereas only the output is available from the unactuated dynamical system. Specifically, the control objective for this class of dynamical systems is to allow a desired subset of the state of the actuated dynamical system to follow given commands in the presence of uncertain parameters and unactuated dynamical system coupling. To this end, our contribution is a mixed state and output feedback framework in the context of model reference adaptive control, which is predicated on integrated nominal and adaptive control laws. The key feature of the proposed model reference adaptive control architecture is to guarantee stability of the closed-loop coupled dynamical system as well as to assign user-defined bounds on system error trajectories utilised by the adaptive control law for achieving a prescribed level of overall system performance.