Efficient Control Design Research Articles

Experimental closed-loop flow separation control: Data- and phenomenological-driven approaches

Flow control aims at modifying a natural flow state to reach an other flow state considered as advantageous. In this paper, active feedback flow separation control is investigated with two different closed-loop control strategies, involving a reference signal tracking architecture. Firstly, a data-driven control law, leading to a linear (integral) controller is employed. Secondly, a phenomenological/model-driven approach, leading to a non-linear positive (integral) control strategy is investigated. While the former benefits of a tuning simplicity, the latter prevents undesirable effects and formally guarantees closed-loop stability. Both control approaches were validated through wind tunnel experiments of flow separation control over a movable NACA 4412 plain flap. These control laws were designed with respect to hot-film measurements, performed over the flap for different deflection angles. Both control approaches proved efficient in avoiding flow separation. The main contribution of this work stands in providing practitioners, simple but yet efficient control design methods for the flow separation phenomena. Equivalently important, a complete validation campaign data-set is also provided.

Switching and non-switching dead-beat sliding mode control with monotonic convergence

Performance requirements necessitate control designs that assure not only transient response specifications but also steady-state accuracy. Monotonic convergence of the tracking error is crucial for an efficient control design to prevent the performance degradation caused by overshooting. This needs a balanced consideration of both reaching conditions and the monotonic convergence, in the context of sliding mode control. In this paper, the dynamic behaviour of the dead-beat sliding mode control is characterized and the signum function is replaced by employing a non-switching one, in order to reduce chattering. The paper conducts a thorough analysis of monotonic convergence of both the switching and the non-switching error dynamics. By deriving the conditions for monotonic convergence, the control parameters can be strategically chosen to ensure monotonic convergence of the tracking error. Numerical and experimental results are presented to validate effectiveness of the proposed control scheme, which evaluate the tracking performance achieved by both the switching and the non-switching control methods.

Wing Kinematics-Based Flight Control Strategy in Insect-Inspired Flight Systems: Deep Reinforcement Learning Gives Solutions and Inspires Controller Design in Flapping MAVs.

Flying insects exhibit outperforming stability and control via continuous wing flapping even under severe disturbances in various conditions of wind gust and turbulence. While conventional linear proportional derivative (PD)-based controllers are widely employed in insect-inspired flight systems, they usually fail to deal with large perturbation conditions in terms of the 6-DoF nonlinear control strategy. Here we propose a novel wing kinematics-based controller, which is optimized based on deep reinforcement learning (DRL) to stabilize bumblebee hovering under large perturbations. A high-fidelity Open AI Gym environment is established through coupling a CFD data-driven aerodynamic model and a 6-DoF flight dynamic model. The control policy with an action space of 4 is optimized using the off-policy Soft Actor-Critic (SAC) algorithm with automating entropy adjustment, which is verified to be of feasibility and robustness to achieve fast stabilization of the bumblebee hovering flight under full 6-DoF large disturbances. The 6-DoF wing kinematics-based DRL control strategy may provide an efficient autonomous controller design for bioinspired flapping-wing micro air vehicles.

An efficient controller design of a PV integrated single inductor multiple output DC-DC converter for dynamic voltage and low power applications.

In this article, a Single Inductor Multiple Output (SIMO) DC-DC boost converter for driving independent three outputs of dynamic voltage and low power is proposed. Compared with the traditional SIMO DC-DC converter, the proposed work accomplishes (i) independent control of power at each output, (ii) small switch count, (iii) relatively better scalability with increasing output channel, (iv) fast response time, and (v) relatively better efficiency. The core aim of the current article is to implement an efficient controller design of the SIMO DC converter circuit operating in continuous conduction mode for dynamic voltage applications. The SIMO circuit comprises a photovoltaic (PV) array as a renewable energy harvesting source at the input. A hysteretic controller based on direct control with seamless transition control topology is implemented in this model for SIMO operation. This scheme is popular due to its less cost, simple, easy-to-use design architecture, and fast speed of close loop control. The configuration of the solar array is designed to deliver maximum power on the input of the SIMO DC-DC converter. For tracking the maximum power point, incremental conductance with integral control configuration is applied on the PV array with fast speed and efficiency. The MATLAB/Simulink environment is utilized for model configuration. The simulation results show that the proposed SIMO configuration with the PV array provided 100.5 W and 21.7 W for 1000 W/m2 and 250 W/m2, respectively.

Controlling fluidic oscillator flow dynamics by elastic structure vibration

In this study, we introduce a design of a feedback-type fluidic oscillator with elastic structures surrounding its feedback channel. By employing phase reduction theory, we extract the phase sensitivity function of the complex fluid–structure coupled system, which represents the system’s oscillatory characteristics. We show that the frequency of the oscillating flow inside the fluidic oscillator can be modulated by inducing synchronization with the weak periodic forcing from the elastic structure vibration. This design approach adds controllability to the fluidic oscillator, where conventionally, the intrinsic oscillatory characteristics of such device were highly determined by its geometry. The synchronization-induced control also changes the physical characteristics of the oscillatory fluid flow, which can be beneficial for practical applications, such as promoting better fluid mixing without changing the overall geometry of the device. Furthermore, by analyzing the phase sensitivity function, we demonstrate how the use of phase reduction theory gives good estimation of the synchronization condition with minimal number of experiments, allowing for a more efficient control design process. Finally, we show how an optimal control signal can be designed to reach the fastest time to synchronization.

A study on a computationally efficient controller design for a surgical robotic system

A study on a computationally efficient controller design for a surgical robotic system

Cascaded Fractional Model Predictive Controller for Load Frequency Control in Multiarea Hybrid Renewable Energy System with Uncertainties

The rapid utilization of electricity has forced the stability of the power system for continuous operation. Due to difference in demand and generation, the frequency reference point changes that needs to be restored to its locus point for the stable operation of the power system. Therefore, a novel efficient control design is propounded to counter the phenomena of load frequency control. The designing of cascaded fractional model predictive controller coupled with fractional-order PID controller (CFMPC-FOPID) is designed for an efficient response of the power system under load disruption and system parameter variations. The controller is optimized by sooty tern optimization algorithm to identify the optimal parameters of the controller. The controller is tested under power mixing of renewable energy sources (i.e., PV and wind) and under varying load scenarios in multiarea hybrid power system. The proposed controller has effectively handled the frequency disruption under distinct load change by stabilizing it in 1.34 sec, 0.60 sec, and 0.41 sec for area-1 with an average time of 0.78 sec, and for area-2, the stabilizing time is 1.40 sec, 0.89 sec, and 0.56 sec with an average time of 0.95 sec, whereas the average time for MPC/PI, DSA-FOPID, GWO: PI-PD, and SCA: FOPI-FOPID is 7.67 sec, 4.68 sec, 1.77 sec, and 4.72 sec, respectively, for area-1 and 6.47 sec, 5.13 sec, 3.45 sec, and 5.02 sec, respectively, for area-2. The outcome result justifies the superiority of the studied technique.

Mathematical Modeling and Thermal Control of a 1.5 kW Reversible Solid Oxide Stack for 24/7 Hydrogen Plants

Solid oxide technology has gained importance due to its higher efficiencies compared to other current hydrogen technologies. The reversible mode allows working with both technologies (SOEC-SOFC), which makes it very attractive for mixed operations, both storage and generation, increasing its usage and therefore the viability of the technology implementation. To improve the performance of reversible stacks, developing adequate control strategies is of great importance. In order to design these strategies, suitable models are needed. These control-oriented models should be simple for an efficient controller design, but also they should include all phenomena that can be affected by the control law. This article introduces a control-oriented modeling of a reversible solid oxide stack (rSOS) for the implementation of control strategies considering thermal and degradation effects. The model is validated with experimental data of a 1.5 kW laboratory prototype, analyzing both polarization curves and dynamic responses to different current profiles and compositions. An error of less than 3% between the model and experimental responses has been obtained, demonstrating the validity of the proposed control-oriented model. The proposed model allows performing new and deeper analysis of the role of reversible solid oxide cells in 24/7 generation plants with renewable energy sources.

Regularization scheme for uncertain fuzzy differential equations: Analysis of solutions

<abstract><p>In this paper a regularization scheme for a family of uncertain fuzzy systems of differential equations with respect to the uncertain parameters is introduced. Important fundamental properties of the solutions are discussed on the basis of the established technique and new results are proposed. More precisely, existence and uniqueness criteria of solutions of the regularized equations are established. In addition, an estimation is proposed for the distance between two solutions. Our results contribute to the progress in the area of the theory of fuzzy systems of differential equations and extend the existing results to the uncertain case. The proposed results also open the horizon for generalizations including equations with delays and some modifications of the Lyapunov theory. In addition, the opportunities for applications of such results to the design of efficient fuzzy controllers are numerous.</p></abstract>

Safe and Efficient Switching Controller Design for Partially Observed Linear-Gaussian Systems

Switching control strategies that unite a potentially high-performance but uncertified controller and a stabilizing albeit conservative controller are shown to be able to balance safety with efficiency, but have been less studied under partial observation of state. To address this gap, we propose a switching control strategy for partially observed linear-Gaussian systems with provable performance guarantees. We show that the proposed switching strategy is both safe and Efficient, in the sense that: (1) the linear-quadratic cost of the system is always bounded even if the original uncertified controller is destabilizing; (2) in the case when the uncertified controller is stabilizing, the performance loss induced by the conservativeness of switching converges super-exponentially to zero. The effectiveness of the switching strategy is also demonstrated via numerical simulation on the Tennessee Eastman Process.

Efficient PID Control Design for Frequency Regulation in an Independent Microgrid Based on the Hybrid PSO-GSA Algorithm

Microgrids are a part of the power system that consists of one or more units of distributed generation and are expected to remain in operation after being disconnected from the system. Since they rely on overlying networks, frequency control is very important for network-independent operation. Some of the most common problems in independently operating microgrids are frequency sustainability and its fluctuations. The main purpose of this study is to control the frequency of a microgrid in island mode in different scenarios. The objective function is defined based on time and changes in the system frequency. Thus, the variable parameters of the PID controller are transformed into an optimization problem and are solved through the hybrid PSO-GSA algorithm. The study considers four scenarios: (a) a microgrid dynamic model and optimal PID controller coefficients; (b) variable velocity disturbance applied to the studied system in order to observe power changes and the microgrid frequency; (c) stepped load changes applied to the studied system; and (d) the proposed methods on the standard test function. Simulations under different operating conditions are performed, indicating improvements in the stability of microgrid frequency fluctuations by means of the proposed control method.

Design and comparison of model-based controllers for an automotive air conditioning system in an electric vehicle

The air conditioning system in an electric vehicle has to fulfill multiple tasks, which are ensuring thermal comfort for the passengers and tempering the battery. In this paper four model-based control approaches for the air conditioning system are developed. The approaches are compared regarding their ability to track desired references, reject disturbances and avoid windup effects. Two approaches are feedback controllers, a decentralized proportional-integral control strategy and a centralized linear-quadratic-integral control strategy. The other two approaches combine the feedback controllers with an inversion based feedforward controller in a two degree of freedom control structure. In addition, the four concepts are complemented by the Hanus-Conditioned anti-windup mechanism.Three of the four proposed controllers explicitly consider the couplings of the multiple-input-multiple-output system, which allows for a high-performance control. Additionally, all approaches have the benefit of being based on a physical model of the system to be controlled. The development and identification of this physical model is part of the paper. The physical basis for the controllers ensures a high level of reusability and therewith an efficient controller design process. The proposed control concepts are validated and compared by measurement data from a testbench.

A multi-input single-output thermal management system design for liquid metal batteries

Liquid metal battery, with three-liquid-layer structure and high operating temperature, is a novel battery technology that shows great potential in grid energy storage. However, unlike lithium-ion batteries, liquid metal batteries are supposed to be heated to a high temperature before operation. Both temperature uniformity among cells and total energy consumption minimization is demanded in the preheating process. In this paper, an efficient thermal management controller design is proposed to optimize the temperature uniformity and energy consumption. Different from previous work, this design replaces the conventional single-input single-output controller with a multi-input single-output controller, which samples temperatures at different locations to control the heating rate. Experimental results show that the final controller error reaches a value of 1.2%. Based on the thermal management system design, a preheating strategy is further proposed to achieve the optimal balance between temperature uniformity and energy consumption. Compared with common strategies, the maximum temperature difference is reduced by 13.7% to 49.85 °C, and the energy consumption is reduced by 6.7% of the total module energy to 6.33 kWh. It is demonstrated in this study that the proposed thermal management system and heating strategy essentially improves the heating process of liquid metal battery stack.

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.

Stability and stabilization for switched positive systems under a weighted MDADT method

The stability and stabilization synthesis problems of the switched positive systems (SPSs) with external disturbances are studied in this paper. For the studied SPSs, a weighted mode-dependent average dwell time (WMDADT) switched strategy has been adopted to analyze the above-mentioned issue, based on which the deficiencies of the existing ADT and MDADT switching techniques can be overcomed. By using the adopted strategy, some improved stability conditions that have less conservativeness are presented for the systems under investigation. Moreover, based on the developed stability conditions, an efficient controller design method avoiding computational complexity and eliminating the rank requirement of the controller is presented. In the end, the effectiveness of the method is verified by two numerical examples.

On the design of efficient congestion control for the Constrained Application Protocol in IoT

The Constrained Application Protocol (CoAP) is one of the main candidates for a lightweight communication protocol for the Internet-of-Things. CoAP provides a simple congestion control mechanism based on successive retransmissions and binary exponential timeouts. This simple mechanism significantly reduces CoAP performance especially in networks with high packet loss, and thus preventing an efficient deployment of the protocol. Enhanced mechanisms for CoAP were proposed in the literature. Some considered improving retransmission timeout estimation whereas others focused on augmenting the retransmission procedure. In this work, we analyze deeply main and recent proposals to highlight their shortcomings. Then, we propose and implement two congestion control algorithms IDC-CoAP and MBC-CoAP which improve retransmission timeout estimation for congestion detection, and adopt adequately a rate-based approach for congestion counteraction, while maintaining simplicity required by constrained devices. The two proposed algorithms are evaluated by means of pure simulations considering several network scenarios, and also using the realistic environment Cooja/Contiki. All results show that our algorithms achieve a much better tradeoff between goodput, reliability and overhead.

Controller Design for Unstable Time-Delay Systems with Unknown Transfer Functions

This study developed a method for designing parallel two-degree-of-freedom proportional-integral-derivative controllers for unstable time-delay processes with unknown dynamic equations. First, a performance index accounting for both transient response performance and disturbance rejection was developed. To obtain useful data even if the output of the system exceeds the allowable range, an effective penalty function was included in the performance index. The N–M simplex method was used to iteratively determine the optimal controller parameters. The proposed approach has the following advantages: (1) it can be used regardless of the stability of the open-loop system; (2) the mathematical model and parameters of the process need not be known in advance; (3) it can be used for processes that include measurement noise; (4) it has good transient response performance and is also robust against external disturbances; and (5) it enables more efficient controller design and reduces costs.

Nonlinear Model Order Reduction for Feedforward Control of an Air Conditioning System in an Electric Vehicle

Air conditioning systems are indispensable in all modern production cars, as they ensure thermal comfort for the passengers. Besides that, extended air conditioning systems allow for additional tasks like tempering the battery in hybrid and electric vehicles. To fulfill these multiple tasks, an advanced control structure has to be applied. Since the components of the air conditioning system are constantly optimized and new structures are developed, an efficient controller design requires a model-based approach to handle the diversity and allow for a fast adaptation to different structures.This paper considers such an approach: For the usage in a two degree of freedom control structure a feedforward controller is derived. This controller is designed as a multiple-input-multiple-output controller with a flatness based approach. To apply the concept on the model of the air conditioning system, a nonlinear model order reduction is performed. The presented reduction method is extended by a frequency scaling on the reduction data and the reduced model is validated with experimental data.

A novel improved version of hunger games search algorithm for function optimization and efficient controller design of buck converter system

There is a continuous effort to develop efficient control mechanisms for power converters as it is a challenging task due to nonlinear nature of such systems. As part of the ongoing effort in the related field, a novel control method is presented in this paper. The proposed method with this study involves a fractional order proportional-integral-derivative (FOPID) controller and the development of a novel metaheuristic algorithm for tuning in order to achieve optimum performance of a buck converter system. In terms of the metaheuristic algorithm, an improved version of the hunger games search (IHGS) algorithm is developed by enhancing the intensification and the diversification abilities of the hunger games search (HGS) algorithm with the aid of Nelder-Mead simplex method and the random learning mechanism, respectively. Several classical unimodal, multimodal, and fixed dimensional benchmark functions along with CEC2019 test suite are used to confirm the improved structure of the developed IHGS algorithm. A novel objective function is also constructed in this study by modifying the structure of the performance index known as integral of squared error. The novel IHGS algorithm together with the novel objective function are proposed as an efficient tool to design a FOPID controller employed in a buck converter system. The proposed method's superiority for the operation of a buck converter system is confirmed comparatively with transient and frequency responses along with robustness analysis in terms of parametric uncertainties and measurement noise along with input and load voltage fluctuations using the best performing methods of artificial ecosystem optimization algorithm based PID, Lévy flight distribution algorithm based PIDA and Harris hawks optimization algorithm based FOPID controllers. The other capable approaches reported in the literature are also used to further confirm the excellent ability of the proposed method for controlling a buck converter system.

Derivative-Based Koopman Operators for Real-Time Control of Robotic Systems

This article presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states. Using higher order derivatives of general nonlinear dynamics that need not be known, we construct a Koopman-operator-based linear representation and utilize Taylor series accuracy analysis to derive an error bound. The resulting error formula is used to choose the order of derivatives in the basis functions and obtain a data-driven Koopman model using a closed-form expression that can be computed in real time. Using the inverted pendulum system, we illustrate the robustness of the error bounds given noisy measurements of unknown dynamics, where the derivatives are estimated numerically. When combined with control, the Koopman representation of the nonlinear system has marginally better performance than competing nonlinear modeling methods, such as SINDy and NARX. In addition, as a linear model, the Koopman approach lends itself readily to efficient control design tools, such as linear–quadratic regulator, whereas the other modeling approaches require nonlinear control methods. The efficacy of the approach is further demonstrated with simulation and experimental results on the control of a tail-actuated robotic fish. Experimental results show that the proposed data-driven control approach outperforms a tuned proportional–integral–derivative controller and that updating the data-driven model online significantly improves performance in the presence of unmodeled fluid disturbance. This article is complemented with a video available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/9_wx0tdDta0</uri> .