Multi-objective Control Research Articles

A position and energy aware multi-objective controller placement and re-placement scheme in distributed SDWSN

A position and energy aware multi-objective controller placement and re-placement scheme in distributed SDWSN

Optimization of an Air Pressure System: A Multi-Objective Control and Modeling Approach

Optimization of an Air Pressure System: A Multi-Objective Control and Modeling Approach

Developing an SSVEP-Based BCI System for Multi-Objective Control via IoT Integration

Developing an SSVEP-Based BCI System for Multi-Objective Control via IoT Integration

Stackelberg exact controllability of a class of nonlocal parabolic equations

This paper deals with a multi-objective control problem for a class of nonlocal parabolic equations, where the non-locality is expressed through an integral kernel. We present the Stackelberg strategy that combines the concepts of controllability to trajectories with optimal control. The strategy involves two controls: a main control (the leader) and a secondary control (the follower). The leader solves a controllability to trajectories problem which consists to drive the state of the system to a prescribed target at a final time while the follower solves an optimal control problem which consists to minimize a given cost functional. The paper considers two cases: in the first case, both the leader and the follower act in the interior of the domain, and in the second case, the leader acts in the interior of the domain and the follower acts on a small part of the boundary. These results are applied to both linear and nonlinear nonlocal parabolic systems.

Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables. The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets. For this purpose, a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities. Then, an asynchronous fuzzy sampled-data controller, featuring distinct premise variables from the active suspension system, is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership. Furthermore, novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous H2 and H∞ performance requirements. Finally, the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

Optimization method for the length of the outsourcing concrete working plane on the main arch rib of a rigid-frame arch bridge based on the NSGA-II algorithm

In addressing the challenges of determining the length of the outsourcing concrete working planes on the main arch rib of rigid-frame arch bridges—a process often reliant on extensive engineering experience, large-scale manual calculations and lack of multi-objective control. This paper employs the NSGA-II multi-objective optimization algorithm, where the objective functions are set to minimize the maximum compressive stress, tensile stress, and vertical deformation of the arch crown after the bridge was built. Optimization studies of the outsourcing concrete working plane length were conducted in different pouring schemes. Based on the case study of a main arch rib specimen with a span of 60 m, the optimal results for different construction schemes were derived. The uneven stress distribution coefficient of key sections was used as the evaluation index to filter the Pareto-optimized solutions from the NSGA-II optimization results. The experimental results indicated that: (1) Compared with the original pouring scheme, the optimized scheme respectively reduced the structure's maximum compressive stress, tensile stress, and vertical deformation of the arch crown by 4.19 MPa, 0.12 MPa, and 1.74 mm, showing a decline of 24.19%, 19.67%, and 2.82%. (2) Seven distinct outsourcing concrete pouring schemes were devised by altering the number of working planes, segments per working plane, and concrete pouring direction. All schemes achieved favorable optimization results through length optimization, demonstrating the broad applicability of the NSGA-II algorithm. (3) The stress distribution coefficients non-uniformity for the outsourcing concrete of the main arch rib are mostly located in the range of [0.7, 0.8], surpassing the original scheme's 0.69. Therefore, the NSGA-II algorithm not only ameliorates the primary arch rib's stress condition and diminishes the arch crown's vertical deformation, but also elevates the post-construction stress distribution state of the bridge's outsourcing concrete.

Multi-objective distributed control of WT-PV-BESS integrated weak grid via finite time containment

This paper proposes a finite time containment control scheme to achieve distributed control of a weak grid which is integrated with photovoltaic (PV), wind turbine (WT) and battery energy storage system (BESS). Taking advantage of the containment control in multi-dimensional control, we proposed a multi-objective control scheme of the WT-PV-BESS system in a weak grid, wherein four control objectives are specified, namely, bus voltage regulation, grid frequency control, peak load shaving and economic benefits optimization for the system operator. Firstly, the sizing approach of BESSs to fulfill the operation mission requirements is proposed; meanwhile, the general coordination strategy for the PV, WT and BESS to achieve the demand–supply balance is proposed. Subsequently, the predefined four control objectives are modeled and transferred into the long/short time frame operational constraints of the BESSs. Afterwards, the finite time containment control algorithm is proposed to force the BESSs into achieving containment status within prescribed time. Simulation case studies are conducted on a modified IEEE 9-bus system to show the effectiveness and performance of the proposed scheme.

A Multi-Terminal Control Method for AC Grids Based on a Hybrid High-Voltage Direct Current with Cascaded MMC Converters

The hybrid high-voltage direct current (HVDC) transmission system with cascaded MMC converters has become a promising alternative for possessing the technical merits of both line-commuted converter (LCC) and voltage source converter (VSC), resulting in favorable characteristics and potential control of good prospect. This paper pays heightened attention to the feasible power and DC voltage control modes of a hybrid HVDC system; characteristics of master–slave control show higher flexibility than the LCC-VSC HVDC system, which demonstrates that the exceptional potential can serve to stability support the AC power grids. To optimize the control effect, besides damping level to attenuate power oscillations, the robustness suitable for various faults is also considered to obtain a multi-objective control problem. A detailed solution is proceeding using the TLS-ESPRIT identification algorithm and H2/H∞ hybrid robust control theory. This motivates multi-terminal controllers in the LCC rectifier and MMC inverters, which immensely improve the stability of both sending and receiving girds at the same time. According to the parameters of the actual hybrid HVDC project, the simulation model is established in PSCAD v4.6.2 software, and proposed control methods have been verified to satisfy damping objectives and perform well in multiple operating scenarios.

Reparameterized multiobjective control of BCG immunotherapy

Bladder cancer is a cancerous disease that mainly affects elder men and women. The immunotherapy that uses Bacillus of Calmette and Guerin (BCG) effectively treats bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG relies on drug dosing, and the design of an optimal BCG regimen is an open question. In this study, we propose the reparameterized multiobjective control (RMC) approach for seeking an optimal drug dosing regimen and apply it to the design of BCG treatment. This approach utilizes constrained optimization based on a nonlinear bladder cancer model with impulsive drug instillation. We compare the performance of RMC with Koopman model predictive control (MPC) and validate the efficacy of optimal BCG dosing regimens through numerical simulations, demonstrating the efficient elimination of cancerous cells. The proposed control framework holds the potential for generalization to other model-based treatment designs.

Applications of the Order Reduction Optimization of the H-Infinity Controller in Smart Structures

In this paper, our strategy is to look for locally optimum answers to a non-smooth optimization problem that has been constructed to include minimization goals and restrictions for smart structures’ vibration suppression. In both theoretical analysis and practical implementation, it is widely recognized that designing multi-objective control systems poses a considerable challenge. In this study, we assess the effectiveness of this method by employing the open-source Matlab toolbox Hifoo 2.0 and juxtapose our findings with established industry standards. We start by framing the control problem as a mathematical optimization issue and proceed to identify the controller that effectively addresses this optimization. This approach introduces the potential application of intelligent structures in tackling the challenge of vibration suppression. This study makes use of the most recent version of the freely available application Hifoo which tries to study vibration suppression with the limits outlined above in the context of multi-objective controller design. A controller directive is initially set, allowing for a lower order.

Safety critical control design for nonlinear system with tracking and safety objectives

This paper proposes a safety critical control law for the tracking with safety of an affine nonlinear control system, which demonstrates how to sacrifice the tracking performance to keep the system trajectory in the safe zone and how to design a performance indicator function for the tradeoff between decreasing the tracking error and satisfying the safety requirement. Our design is essentially a nonlinear multi-objective control to achieve both safety and asymptotic tracking if there is no conflict between the tracking and safety, or to decrease the tracking error under the safety constraints otherwise. Different from existing methods for safety critical control problems, the design idea is to find a tracking controller without safety consideration, and then design a safety controller based on a control barrier function, which is capable of dealing with both tracking and safety issues. The efficiency of our design is illustrated by a mechanical system in both non-conflicted and conflicted cases.

Null controllability and Stackelberg–Nash strategy for a $2\times 2$ system of parabolic equations

This paper is dedicated to solving a multi-objective control problem for a 2\times2 system of parabolic equations. Here, we have many objectives, possibly conflictive, and we adopt a concept of hierarchy for the controls. We have the leader control, responsible for objectives of controllability type, and the follower controls which intend to be a Nash-equilibrium with respect to some given cost functionals. The novelty here is that we formulate this problem for systems of parabolic equations, meaning that we have many variables and naturally much more objectives to accomplish.

Impact-aware task-space quadratic-programming control

Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot’s linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques; which can compromise controllers’ stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.

Comparison of Multi-Object Control Methods Using Multi-Objective Optimization

The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of the greater effectiveness of bi- and tri-objective optimization compared to single-objective optimization, reflecting the compromises taken into account when choosing between objects and achieving a minimum risk of collision when passing them.

Demand side flexibility for a Heat Booster Substation with ultra low temperature district heating

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this study, Domestic Hot Water (DHW) preparation at a Heat Booster Substation (HBS) supplied with ULTDH is investigated for generating demand side flexibility. The main components of the HBS are a Hot Water Storage Tank (HWST) and an electric-driven heat pump. A genetic algorithm based control strategy is developed to optimally utilize the thermal energy storage capability of the HBS by intelligently scheduling the charging of the HWST based on time-varying electricity price signals. A multi-objective control strategy is designed to reduce energy costs while generating flexibility on the demand side. The components of the HBS are numerically modeled and experimentally validated to form a dynamic non-linear model of the HBS. Further, the data-driven seasonal ARIMA model, ARIMA(0,1,1)x(0,1,1)168, is developed to forecast the DHW consumption based on a statistical analysis of the historic consumption profiles over a two-year period. The forecast RMSE of the DHW prediction model is less than 0.03kgs−1 over the two-year test period, indicating that the prediction model is able to accurately forecast the DHW consumption of the residents. With the control-oriented HBS numerical model and the DHW prediction model serving as decision support tools, the developed control strategy is implemented at the HBS. Results show that in comparison to a rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility while attaining energy cost savings of more than 91% and 84% at time resolutions of 15 and 30 min for the control interval respectively. With increased volatility in electrical prices due to higher penetration of RES, greater energy cost savings of more than 150% are observed.

Circular economy-based multi-objective decentralized controller for activated sludge wastewater treatment plant

Introduction: Water scarcity and water pollution are two major issues in India. Circular economy-based wastewater treatment technology provides the most sustainable solutions for solving these issues. In this paper, a novel multi-objective decentralized controller (MODC) is proposed for benchmarking a multi-input multi-output (MIMO) activated sludge wastewater treatment plant (WWTP) to achieve maximum effluent quality with minimum cost. WWTPs with conventional control schemes consume more energy to achieve the desired effluent quality.Methods: In this study, a MIMO model is developed for the activated sludge process (ASP) from a physics-based model, and relative gain array (RGA) analysis are carried out to determine the interaction between the loops to identify a suitable control scheme for the MIMO process. In addition, a multi-objective decentralized control problem is formulated to achieve the conflicting multiple objectives of improving effluent quality and minimizing operational costs by efficient usage of energy.Results and discussion: The desired quality and cost reduction are verified by comparing the integral square error (ISE) and control effort (CE) values of a closed-loop WWTP. A multi-objective evolutionary algorithm (MOEA), namely, the non-dominated sorting genetic algorithm (NSGA)-II, successfully solves the multi-objective control problem. NSGA-II provides several optimal solutions in the Pareto front. In order to demonstrate the feasibility of the proposed controller, three optimal solutions are selected from the Pareto-optimal front, and their closed-loop performances are evaluated qualitatively and quantitatively for both servo and regulatory operations. Improving the quality of effluent enhances active sludge production, which in turn increases the methane production in the anaerobic digester.

Stability-Oriented Multiobjective Control Design for Power Converters Assisted by Deep Reinforcement Learning

Stability-Oriented Multiobjective Control Design for Power Converters Assisted by Deep Reinforcement Learning

Assessment of automated shading systems’ utilization and environmental performance: An experimental study

This paper presents an experimental study conducted in a full-scale test cell “btga-box” from July until September 2020 at Wuppertal University, Germany. This study focused on evaluating occupant interaction and satisfaction with automated shading systems in office environments, and the underlying thermal and visual conditions under different scenarios to optimize automated shading design and operation for improving occupant comfort and energy efficiency. Six different scenarios were evaluated using several performance metrics. Twenty-eight participants of varying ages, gender, and ethnicity took part in the experiments. After each scenario, the participants were asked to complete a web-based questionnaire to report their behaviour, their perceived thermal and visual comfort, satisfaction, and preferences concerning the performance of automated shading controls. Concurrently, indoor environmental parameters, weather data, and system and user-triggered actions were recorded. The key findings of this study suggest that a robust shading system (i.e., few override actions) can be achieved by: deploying a multi-objective control strategy with an intermediate position, an acceptable range of irradiance thresholds, and a decent level of other adaptive behavioural control options (i.e., window opening, fan usage, and light control). Providing active cooling or ventilation systems (i.e., operable windows, exhaust ventilation, and ceiling fan) can improve user satisfaction with the indoor environment, including shade operation. Using a small window size combined with a high irradiance shade-lowering threshold decreased the user-raising actions by 49% compared to the low threshold. However, energy implications should be considered for further studies since user satisfaction with blind control should be balanced with energy efficiency.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Deep Reinforcement Learning and NOMA-Based Multi-Objective RIS-Assisted IS-UAV-TNs: Trajectory Optimization and Beamforming Design

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.