Control Algorithm For Systems Research Articles

A review and comparative analysis of maximum power point tracking control algorithms for wind energy conversion systems

A review and comparative analysis of maximum power point tracking control algorithms for wind energy conversion systems

Research on AEB-P Control Strategy Considering Braking Process Comfort

A control approach for the pedestrian-oriented automobile automatic emergency braking system (AEB-P) that takes driving comfort into account is suggested in an effort to further optimize the AEB-P system's control algorithm. Firstly, for the AEB-P assessment standard of C-NCAP, a braking safety distance model that takes the driver's comfort into account during braking is created. The next step was to construct a hierarchical controller using PID and MPC principles. Lastly, CarSim and Matlab/Simulink are used to build and validate the test scenarios and control methods. The simulation results demonstrate that both ride comfort and driving safety can be guaranteed by the suggested control technique.

Determining the parameters of the functioning for a nonlinear ballistic system in a real external environment

Determining the parameters of the functioning for a nonlinear ballistic system in a real external environment

Predefined-time stabilization of stochastic nonlinear systems with application to UAVs

The paper presents a new Lyapunov-type predefined-time stabilization control algorithm for stochastic high-order nonlinear systems with asymmetric output constraints. In contrast to stochastic finite-time and fixed-time stabilization, the average value of the settling-time function for stochastic predefined-time stabilization control is independent of both the initial value and the control factors. To mitigate the significant uncertainties arising from the asymmetric output constraint, a tan-type barrier Lyapunov function is formulated. Furthermore, by harnessing the previously mentioned barrier Lyapunov function and integrating the power integrator technique, a controller design strategy is formulated based on the backstepping method. The rigorous analysis in this study proves that the designed controller ensures both the attainment of predefined-time convergence of the system states to the origin in probability and the satisfaction of the output constraint. Finally, an example of a roll angle subsystem for quadrotor UAVs and a numerical illustration are presented to corroborate the theoretical analysis.

FPGA-Based Speed Control Strategy of PMSM Using Improved Beetle Antennae Search Algorithm

To improve performance in terms of overshoot and motor response speed when a permanent-magnet synchronous motor (PMSM) with a proportional–integral (PI) controller is subjected to external disturbances, this paper proposes a speed control strategy based on an enhanced Beetle Antennae Search algorithm, which allows for adjustable parameters of the PI controller within a certain range. Firstly, to enhance the global and local search capabilities of each individual beetle, the step size was improved by linearly decreasing it. Secondly, a simulation model of a PMSM closed-loop control system was built to verify the effectiveness of the improved Beetle Antennae Search (BAS) algorithm. Finally, a linear feedback shift register model that generates four random numbers was developed on a field-programmable gate array (FPGA). The improved BAS algorithm for the PMSM control system was implemented on an FPGA using the Verilog hardware description language, and the feasibility of the system was verified through hardware simulation. Additionally, the hardware resource consumption on different FPGA platforms was analyzed. The simulation results demonstrate that the proposed new speed control strategy can reduce the overshoot and improve the motor response speed.

Delayed non‐fragile robust control of civil structure considering structural uncertainties, actuator faults, and control force time delays

Active control is an effective strategy for suppressing unwanted structural vibrations. The uncertainties in computational models, actuator faults, and time delays in control forces are the potential threats for control performance improvements. These factors are interconnected and may lead to control system instability and poor control effectiveness. The paper presents a delayed non-fragile robust control (DNR) algorithm for uncertain systems with consideration of actuator faults and time delays. To this end, the existence conditions for the DNR multi-objective controller are derived by utilizing the Lyapunov stability proof and introducing a pole constraint inequality. Then, a dynamic output feedback DNR algorithm is further developed. Finally, two numerical simulation examples are discussed for several ground motions. The results show that the control system based on the proposed algorithm exhibits better stability, robustness, and fault tolerance than the linear quadratic Gaussian (LQG) algorithm. Furthermore, there is no need for additional control energy. Significantly, the control performance of the proposed algorithm can be further enhanced with the decentralized control strategy.

An improved state space model predictive control for linear systems with input disturbance

This paper presents an improved model predictive control (MPC) algorithm for linear systems with input disturbance. Based on the developed extended non-minimum state space input disturbance (ENMSS-ID) model, the input disturbance model structure is incorporated into the MPC framework and the objective function of the MPC optimization problem is improved to weigh the system output increments. This enables the algorithm simultaneously to achieve good input disturbance rejection performance for systems with known input disturbances and reduce the controllers’ sensitivity to model mismatch. An existing optimal estimation method is introduced to estimate the input disturbance, together with the proposed strategy to improve estimation convergence. Offset-free property is also proven to show the steady-state performance of the designed control scheme. Finally, two benchmark plants are studied to illustrate the effectiveness and advantages of the proposed algorithm.

Design of ABS sliding mode control system for in-wheel motor vehicle based on road identification

Aiming at the problem that the current Anti-lock Braking System (ABS) control algorithm can not make full use of the ground friction to complete the braking when emergency braking on complex roads, an ABS sliding mode control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor regenerative braking and mechanical friction braking is designed. Based on the neural network control method, the road friction coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip ratio of different roads. The ABS sliding mode controler is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the symbol function in the traditional sliding mode control to weaken the chattering problem, and then the ABS controller is designed. The research results show that compared with the traditional ABS sliding mode controller, the designed ABS neural network sliding mode controller has the following advantages: 1. Through the identification of tire-road friction coefficient by neural network, the real-time performance and road adaptability of ABS controller are improved; 2. When the braking simulation is carried out at a given speed, the braking time is reduced by about 1.03 % and the braking distance is shortened by about 1.01%; 3. The identification of tire-road friction coefficient is realized, which weakens the chattering problem of traditional sliding mode control and improves the stability and robustness of ABS controller.

Research on Electro-hydraulic Servo Valve Structure and Control Algorithm of System with Servo Valve

Background: With the continuous progress of electronic information technology, people have begun to study the combination of electronic information technology and hydraulic technology to achieve more accurate and flexible control. Electro-hydraulic servo technology was thus developed. Much attanetion has been paid to electro-hydraulic servo valve,a key component of electrohydraulic servo technology. Objective: The number of stages of the electro-hydraulic servo valve, the structure form of the electro- hydraulic servo valve hydraulic front stage, the feedback form of the electro-hydraulic servo valve, the output form of electro-hydraulic servo valve, and the control algorithm based on the application of electro-hydraulic servo valve are analyzed and commented respectively. Combined with associated patents, the research direction and hot topics of electro-hydraulic servo valves are analyzed. Method: The electro-hydraulic servo valves were briefly classified; and then the problems of different types of valves were discussed in depth. Results: By analyzing the problems of electro-hydraulic servo valves, the problems of how to improve the control accuracy, response speed, anti-interference ability, service life and cost reduction of electro-hydraulic servo valves are analyzed comprehensively, and the future development prospect of electro-hydraulic servo valves is predicted and outlooked. Conclusion: Analyzing and discussing the existing hot issues of electro-hydraulic servo valves helps to update and change the electro-hydraulic servo valves, and makes corresponding references for the subsequent research of electro-hydraulic servo valves.

Total Energy-Based Control Design and Optimization for Lift-Plus-Cruise Aircraft

This paper presents a methodology for optimizing a Total Energy–based control system architecture for a lift-plus-cruise vertical takeoff and landing urban air mobility concept. The Total Energy Control System algorithm, which was originally developed for fixed-wing applications, is extended to also be applicable to hovering and transitioning flight. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on dynamic stability and control response characteristics. Control system optimization and flight simulations are performed using the Modular Aircraft Dynamics and Control Algorithm Simulation Platform. Results presented include simulations of the optimized flight control system operation for maneuvering scenarios representative of hover, transition, and forward flight conditions as well as during transitions between vertical and forward flight modes. The presented results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology.

A novel simplified buck power system control algorithm: Application to the emulation of photovoltaic solar panels

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT.

Tracking and Disturbance Suppression of the Radio Telescope Servo System Based on the Equivalent-Input-Disturbance Approach

This paper presents a composite control algorithm for the radio telescope servo control system to address the target tracking and matched/mismatched disturbance suppression problems. The algorithm consists of the equivalent-input-disturbance (EID) approach and the optimal control method. An EID estimator is developed using the difference between the estimated output of the state observer and the measurement output and then the estimate of the EID is fed forward into the control input to reject the disturbance. A cost function with clear physical meaning is selected and the weighting parameters are adjusted for the optimal controller to improve tracking performance. Considering the nonminimum phase characteristics of the radio telescope system, the state observer gain is computed using the linear matrix inequality (LMI) method. The system stability is analyzed using the small gain theorem, and the linear quadratic regulator (LQR) control method is utilized to determine the state feedback gain. Finally, the composite controller is designed for an identified telescope model. Simulation results show that for the tracking performance, the settling time of proposed method is 1.13 s and reduces by about 0.32 s and 0.87 s than that of the ADRC controller and PI controller, respectively. For the antidisturbance performance, the RMS value and the maximum error of the proposed method are 0.0039 and 0.0128, which are 42.86% and 40.38% of the ADRC controller and 30.71% and 27.77% of the PI controller, respectively, which indicates that the proposed method has better control performance. In addition, the proposed controller has certain robustness to systematic parameter perturbations.

Performance of deep reinforcement learning algorithms in two-echelon inventory control systems

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies.

Dynamic Event-Triggered-Based Adaptive Finite-Time Neural Control for Active Suspension Systems With Displacement Constraint.

In this article, we give an event-based control algorithm for nonlinear active suspension systems (ASSs) with the vertical displacement constraint. For in-vehicle communication networks, the controller area network (CAN) is a widely used standard for interconnecting electronic control units (ECUs). The main task in this work is, thus, to reduce the communication burden on the CAN. To this end, we develop a dynamic event-triggered communication mechanism in ASSs. Meanwhile, in practice, the vehicle safety is degraded when the body vibration exceeds the allowable maximum. Thus, the vertical displacement of ASSs should be constrained within a reliable range. For this purpose, we present a novel finite-time integral barrier Lyapunov function (FTIBLF), which not only guarantees that the vertical displacement constraint bounds are not violated, but also enables the position of the suspension to be stabilized in the neighborhood of a desired position in finite settling time. Furthermore, neural networks (NNs) are utilized to identify the unknown nonlinear characteristics in ASSs subjected to the modeling error and unknown mass. As a result, we propose an adaptive neural control technique based on the dynamic event-triggered condition, which helps to improve the ride comfort while ensuring the driving safety and handling stability. Finally, simulation results are provided to verify the validity of the presented methodology.

A semi-adaptive feedforward hybrid active noise control algorithm for multichannel systems.

Broadband active noise control systems incorporating fixed controllers exhibit limited ability to reduce sinusoids. This study presents a semi-adaptive feedforward hybrid active noise control (HANC) system to address this issue. The proposed system pairs fixed high-order optimal controllers for broadband noise with adaptive low-order FXLMS-based controllers for narrowband noise. Notably, parallel broadband and narrowband controllers work independently. The proposed semi-adaptive feedforward HANC system demonstrates low computational complexity which makes it suitable for multichannel systems. Simulations and experiments validate the effectiveness of the proposed system in controlling mixed noise.

Acceleration and deceleration control algorithm of CNC system based on deep neural network reinforcement learning and MCU

Acceleration and deceleration control, as one of the key technologies in high-speed CNC system, directly affects the machining efficiency, the stability of machining process and the error of machining follow. Therefore, it is necessary to study and explore new acceleration and deceleration control methods in high-speed CNC system to ensure the smooth feed and improve the machining accuracy. Therefore, the acceleration and deceleration control algorithm of NC system based on deep reinforcement learning and single chip microcomputer is studied. The theoretical basis of deep reinforcement learning is analyzed, and the acceleration of acceleration and deceleration is calculated based on the linear acceleration and deceleration control. The whole integer operation of single chip microcomputer is used to estimate the value range of each step. The variation characteristics of trajectory motion are predicted so that acceleration and deceleration can be processed across program segments. The velocity of transfer point is calculated by the rate of change of feed velocity vector, and the speed of multi-program is smoothed by adjusting the allowable contour error. Based on the proximal strategy optimization algorithm in deep reinforcement learning, the acceleration and deceleration control model of CNC system is established to realize the acceleration and deceleration control. The experimental results show that the proposed algorithm has better control effect, shorter time and smaller interpolation error, which can ensure the NC system to feed smoothly at high speed.

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

Passivity and higher-order sliding mode control for doubly-fed induction generation wind power system

To simplify the model of DGIG and improve the robustness, the variable speed and constant frequency (VSCF) DFIG wind power generation systems from the perspective of energy and robustness were studied. It was based on passivity and sliding mode control (SMC) theory. Firstly, the model of DFIG based on Euler-Lagrange (EL) equations was established, so the original DFIG wind turbine system is divided into electrical and mechanical subsystems. For two subsystems, the electrical subsystem was designed. In contrast, the mechanical subsystem is an energy-consuming system and is stable through selecting suitable design parameters. Hence, the system control algorithm is simplified. A high-order terminal SMC was designed to get a faster speed and better robustness for the speed loop. Simulation results show that the proposed controller is simple and effective. It can guarantee the wind power system outputs constant frequency. The motor speed can also track the reference speed quickly, so the performance of the wind turbine system is improved.

Internet of things-based study on online monitoring system of building equipment energy saving optimization control using building information modeling.

Smart building equipment monitoring is a well-established field focused on enhancing contemporary building comfort. The proliferation of Internet connectivity, facilitated by the internet of things (IoT), has transformed buildings from static structures into interactive environments. IoT has witnessed substantial growth across various aspects of daily life, from monitoring environmental conditions to managing building systems and storing data in the cloud. One critical application is the intelligent monitoring and control of building equipment, such as air conditioners, to optimize energy efficiency-a matter of increasing concern for building owners, design experts, and system integrators. Achieving comprehensive energy savings demands a meticulous approach to energy-efficient design and control. This paper's primary objective is to explore and analyze IoT-based energy-saving optimization techniques for intelligent building equipment, integrating building information modeling (BIM) technology. It particularly delves into the energy conservation control algorithm for air-conditioning systems. The research presents a challenge rooted in energy-saving optimization, established upon specific objective functions, followed by a detailed explanation of the energy-saving control algorithm. To validate their approach, the paper outlines a comprehensive experimental design. Over three sessions in August, they conducted control experiments in two distinct areas. Area 1 implemented the energy-saving control methodology discussed in the paper, utilizing virtual parameter enhancement mechanisms, while Area 2 adhered to conventional control methods. The results were enlightening. Area 1 demonstrated superior energy efficiency, consuming 735 kWh compared to Area 2's 819 kWh, signifying an impressive 11.43% reduction in energy consumption thanks to the optimized control strategy. This research underscores the practicality and significance of implementing IoT-based energy-saving strategies, with a focus on smart thermostats, HVAC controllers, and daylight sensors, in intelligent building equipment management to achieve substantial energy conservation gains.

A Discrete-Time Fractional-Order Flocking Control Algorithm of Multi-Agent Systems

In this paper, a discrete-time fractional flocking control algorithm of multi-agent systems is put forward to address the slow convergence issue of multi-agent systems. Firstly, by introducing Grünwald-Letnikov (G-L) fractional derivatives, the algorithm allows agents to utilize historical information when updating their states. Secondly, based on the Lyapunov stability theory, the convergence of the algorithm is proven. Finally, simulations are conducted to verify the effectiveness of the proposed algorithm. Comparisons are made between the proposed algorithm and other methods. The results show that the proposed algorithm can effectively improve the convergence speed of multi-agent systems.