Classical Control Methods Research Articles

Motion Control of Autonomous Underwater Helicopter Based on Linear Active Disturbance Rejection Control with Tracking Differentiator

As a new disk-shaped autonomous underwater vehicle (AUV), the autonomous underwater helicopter (AUH) is devoted to subsea operations, usually diving into the seabed and docking with a subsea docking system. Due to the motion control’s performance, the AUH’s stability and steady-state accuracy are affected remarkably while docking. Moreover, considering the difficulties of hydrodynamic modeling of AUHs, the classical model-based control method is unsuitable for AUHs. Moreover, there is a large gap between the hydrodynamic simulation results and real situations. Hence, based on the data-driven principle, the linear active disturbance rejection control with a tracking differentiator (LADRC-TD) algorithm is employed for AUH depths and heading control. As the simulation experiments prove, LADRC and LADRC-TD have better anti-interference performance when compared with PID. According to the pool experiments, overshoots of the LADRC-TD are 20 cm and 3° for the depth control and heading control, respectively, which are superior to PID and LADRC. Meanwhile, the steady-state accuracy of the LADRC-TD is ±21 cm and ±2.5° for the depth and heading control, respectively, which is inferior to PID and the same as LADRC.

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control.

Control of a jet engine using predictor-based adaptive strategy in different flight modes

In this article, a predictor-based model reference adaptive control method is proposed for a JetCat SPT5 turboshaft engine in full thrust, cruise, and idle modes. In the predictor-based model reference adaptive control method, in addition to the tracking error, the predictor error is also utilized in adaptive laws to achieve the desired control objectives, such as improving the tracking performance and control signals, and reducing the tracking error. The proposed method is implemented on a turboshaft engine system with actual dynamic values. First, three properly separated equilibrium points are selected on the nominal plant equilibrium manifold to linearize the plant model at the equilibrium points. Then, the predictor-based model reference adaptive control controller is designed and investigated for the three equilibrium points generating three operating modes of the engine, that is, full thrust, cruise, and idle. The stability of the control system is proved by the Lyapunov method. To evaluate the efficiency of the state predictor method in the simulation scenarios, the proposed method is compared with the classic model reference adaptive control method. The simulation results illustrate the superiority of the predictor-based model reference method due to the reduction of unwanted fluctuations in tracking performance, faster convergence of the tracking error to zero, and smoother control signals for all the equilibrium points.

Multi-Agent Optimal Control for Central Chiller Plants Using Reinforcement Learning and Game Theory

To conserve building energy, optimal operation of a building’s energy systems, especially heating, ventilation and air-conditioning (HVAC) systems, is important. This study focuses on the optimization of the central chiller plant, which accounts for a large portion of the HVAC system’s energy consumption. Classic optimal control methods for central chiller plants are mostly based on system performance models which takes much effort and cost to establish. In addition, inevitable model error could cause control risk to the applied system. To mitigate the model dependency of HVAC optimal control, reinforcement learning (RL) algorithms have been drawing attention in the HVAC control domain due to its model-free feature. Currently, the RL-based optimization of central chiller plants faces several challenges: (1) existing model-free control methods based on RL typically adopt single-agent scheme, which brings high training cost and long training period when optimizing multiple controllable variables for large-scaled systems; (2) multi-agent scheme could overcome the former problem, but it also requires a proper coordination mechanism to harmonize the potential conflicts among all involved RL agents; (3) previous agent coordination frameworks (identified by distributed control or decentralized control) are mainly designed for model-based control methods instead of model-free controllers. To tackle the problems above, this article proposes a multi-agent, model-free optimal control approach for central chiller plants. This approach utilizes game theory and the RL algorithm SARSA for agent coordination and learning, respectively. A data-driven system model is set up using measured field data of a real HVAC system for simulation. The simulation case study results suggest that the energy saving performance (both short- and long-term) of the proposed approach (over 10% in a cooling season compared to the rule-based baseline controller) is close to the classic multi-agent reinforcement learning (MARL) algorithm WoLF-PHC; moreover, the proposed approach’s nature of few pending parameters makes it more feasible and robust for engineering practices than the WoLF-PHC algorithm.

An Efficient Supervisory Power Management Scheme for a Wind–Battery-Assisted Hybrid Autonomous System

This article presents an efficient power management and protection scheme for a wind–battery-assisted hybrid autonomous system. To operate the wind power generation system at the maximum power point (MPP), a drift-free perturb and observe algorithm is adapted. A unified controller for a bidirectional converter (BDC) is incorporated for the dc-bus voltage regulation along with the charge/discharge control of the battery. The small-signal analysis of the BDC and the buck converter is presented in detail. The performance of the proposed power management scheme (PMS) is validated through a time-domain simulation in MATLAB/Simulink along with the hardware implementation using the DS1103 digital platform. Initially, the PMS with only maximum power point tracking (MPPT) and pitch controller is unable to maintain the dc-bus voltage during the battery overcharge or deep-discharge condition. To address this issue, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -MPPT controller is proposed to regulate the dc-bus voltage. The effectiveness of all the operating modes of the proposed PMS is verified in the developed laboratory prototype, and the performance is also compared with that of the classical droop control method. The experimental results confirm the ability of the PMS to handle power sharing and dc-bus voltage fluctuation during wind speed and load change and satisfy the load demand. This ensures that the improved performance of the developed hybrid autonomous system and changeover from the MPP to the generator or battery protection mode is seamless and fast.

Optimal Control of Dynamical Systems using Calculus of Variations

This paper explores the application of calculus of variations techniques for optimizing the control of complex dynamical systems. Determining control strategies that minimize cost metrics and satisfy constraints is critical across engineering disciplines like robotics, aerospace, and process operations. Classical optimal control methods, such as Pontryagin's Maximum Principle, transform an optimal control problem into a calculus of variations framework amenable to analytical and numerical optimization. We present a unified framework for applying these techniques, enabling dynamical systems defined by ordinary and partial differential equations to be optimized by conversion into a nonlinear programming form. Analytical approaches provide theoretical guarantees on control performance while numerical methods, like direct collocation and transcription, enable large-scale optimal control problems to be efficiently solved. The connections between dynamical systems, calculus of variations, and modern numerical optimization methods establish a holistic methodology for control engineers and applied mathematicians to design optimal controllers for physical systems. Case studies on real-time trajectory optimization, adaptive path planning, and dynamic process operations demonstrate the efficacy of the proposed optimal control framework across robotics, aerospace, power systems, and other applications.

Optimal Control of Dynamical Systems using Calculus of Variations

This paper explores the application of calculus of variations techniques for optimizing the control of complex dynamical systems. Determining control strategies that minimize cost metrics and satisfy constraints is critical across engineering disciplines like robotics, aerospace, and process operations. Classical optimal control methods, such as Pontryagin's Maximum Principle, transform an optimal control problem into a calculus of variations framework amenable to analytical and numerical optimization. We present a unified framework for applying these techniques, enabling dynamical systems defined by ordinary and partial differential equations to be optimized by conversion into a nonlinear programming form. Analytical approaches provide theoretical guarantees on control performance while numerical methods, like direct collocation and transcription, enable large-scale optimal control problems to be efficiently solved. The connections between dynamical systems, calculus of variations, and modern numerical optimization methods establish a holistic methodology for control engineers and applied mathematicians to design optimal controllers for physical systems. Case studies on real-time trajectory optimization, adaptive path planning, and dynamic process operations demonstrate the efficacy of the proposed optimal control framework across robotics, aerospace, power systems, and other applications.

A Modular Circuit Synthesis Oriented Modelling Approach for Non-Isolated DC-DC Converters in CCM

The continued commissioning of DC microgrids in an effort to achieve net-zero carbon levels in the atmosphere demands the large-scale deployment of converters to make the power from renewable energy sources, such as solar PV, usable. To control these inherently non-linear converters using classical linear control methods, averaged modelling techniques are employed. These methods are laborious and easily become intractable when applied to converters with increased energy storage elements. A modular modelling approach is proposed. This approach is based on the synthesis of converters using refined basic building blocks. The refined basic building blocks are independently modelled as two-port networks and used in a circuit synthesis-oriented manner to derive power stage models of commonly used DC-DC converters. It is found that most of the converters considered in the study can be described as a cascade combination of these basic building blocks. As such, transmission parameters are mainly used to model the two-port networks. Moreover, it is also found that using this modelling technique enables the computation of generalized expressions for all power stage models of interest. The use of two-port networks curtails the size of the matrices describing the basic building blocks to 2 × 2, and thus simplifies the entire modelling procedure. Additionally, two-port network analysis makes this modelling technique modular, thus making it more suited to be employed in DC microgrids. The independence of the two-port models on the circuit topology and functionality makes it possible to even model new converters containing the described basic building blocks solely based on circuit connection.

Energy Storage Management for Microgrids Using n-Step Bootstrapping

Microgrids offer superiorities such as reducing energy costs and increasing the quality of energy, with the use of renewable energy sources and the effective use of energy storage unit created with innovative batteries. Furthermore, this structure, which helps to reduce the carbon footprint, will become undeniably critical to use in near future with the nanogrid and smart grid. As another development, an artificial intelligence (AI)-based control infrastructure brought to us by machine learning stands out as more beneficial than classical control methods. With this framework, which is called reinforcement learning (RL), it is promised that the system to be controlled can be more efficient. At this point, the thrifty use of energy storage unit, which is the most important tool that will increase the profitability of microgrids and enhance the proficiency of energy use, is associated with an RL-based energy control system. While this study focuses on an AI-based control infrastructure, it proposes a method utilizing an RL agent trained with a novel environmental model proposed specifically for the energy storage unit of microgrids. The advantages of this method demonstrated with the results are obtained, are shown and examined.

Neural Network Based Predictive Current Controllers for Three Phase Inverter

With Predictive Current Controllers, system behavior or current reference is predicted. In this way, it is aimed to prevent hardware and software related delays. In this study, new Artificial Neural Network (ANN) based Predictive Current Controllers are designed using four different methods for voltage source inverters. The training of the networks is done offline using the data obtained from the simulation results for different parameters in the Matlab environment. In the first proposed method, a static ANN based current controller is designed and trained using the data obtained from the Finite Control Set Model Predictive Control (FCS-MPC) method. Then, a feed-forward reference current predictor ANN (PRefNN) is designed to make sinusoidal reference current prediction in the other three methods. The other proposed predictive ANN methods are trained by taking the data offline from the inverter system in which PRefNN and the classical current controllers (Hysteresis, PR, and PI) are used. In this way, three different predictive current controllers named as Hysteresis based predictive ANN (Hist-PNN), PR based predictive ANN (PR-PNN), and PI based predictive ANN (PI-PNN) are designed. Classical current control methods have been given predictive properties with these three different network structures. And also, it is improved the performance of classical methods against parameter changes and noises. A three phase 5kVA inverter circuit with a 7MBP50RJ120 IPM module in the power stage and STM32f407 as a controller is designed for the experimental setup. The methods are tested in simulation and validated in the experimental setup.

Generalised Regret Optimal Controller Synthesis for Constrained Systems

This paper presents a synthesis method for the generalised dynamic regret problem, comparing the performance of a strictly causal controller to the optimal non-causal controller under a weighted disturbance. This framework encompasses both the dynamic regret problem, considering the difference of the incurred costs, as well as the competitive ratio, which considers their ratio, and which have both been proposed as inherently adaptive alternatives to classical control methods. Furthermore, we extend the synthesis to the case of pointwise-in-time bounds on the disturbance and show that the optimal solution is no worse than the bounded energy optimal solution and is lower bounded by a constant factor, which is only dependent on the disturbance weight. The proposed optimisation-based synthesis allows considering systems subject to state and input constraints. Finally, we provide a numerical example which compares the synthesised controller performance to H2- and H∞-controllers.

Deep Reinforcement Learning Based Active Pantograph Control Strategy in High-Speed Railway

The pantograph-catenary system (PCS) is the essential power supply system in the high-speed railway, but its coupling performance is influenced significantly by the rapidly increasing train speed. The actively controlled pantograph is one of the promising technologies to suppress the fluctuation of the pantograph-catenary contact force (PCCF). In this paper, we propose a novel pantograph control strategy based on deep reinforcement learning (DRL) to overcome the complex time-varying characteristic of PCS, which distorts the system identification of the classical control methods. First, a non-linear pantograph-catenary system model is established based on the finite element and multi-body dynamics theory as the simulation environment in DRL. Then, the state space, action space, and reward in DRL are redesigned to train the agent, which is suitable for PCS. Finally, the effectiveness and robustness of our proposed method are verified under various working conditions and parameter disturbances. The experiment results show that our control strategy can reduce the PCCF fluctuation up to 40% and reject parametric perturbation while achieving state-of-the-art performance on the benchmarks.

Performances analysis of a classical charge controller and a modified fuzzy logic charge controller applied to a PV system

Solar photovoltaic energy is one of the most used renewable energies and represents a very promising field of research, the installations can include storage batteries or not, these batteries are very expensive and charge and discharge according to the need of the load and the energy of the photovoltaic generator available. The state of these batteries will be controlled by classical or intelligent control techniques, in our work we will make a simulation of an autonomous photovoltaic system with storage using Matlab simulink software where we will see a comparison between a classical control method of the state of charge of the storage batteries and another fuzzy logic technique where we will see the impact of these two techniques on the storage bus. &nbsp

Cooperative Multi-Objective Control of Heterogeneous Vehicle Platoons on Highway with Varying Slopes

Stability, vehicle safety, energy saving, and passenger comfort are the major objectives of vehicle platooning control. These objectives are coupled, interrelated, and even conflicting, so integrated optimization of multiple objectives is quite challenging. Particularly for heterogeneous platoons, the difficulties are intensified for the differences in vehicle dynamics. In this paper, the concept of symmetry is utilized in the platooning control, that is, the design method of each vehicle’s controller is the same. For each controller, it is to solve the optimal solution of multi-objective collaborative optimization. The concept of asymmetry is meanwhile embodied in the parameter setting of each controller, for the vehicle heterogeneity. The contents of this study are as follows. First, a mathematical model is established, in which the differences in vehicle dynamic characteristics of heterogeneous platoon, road slope, and aerodynamics are all taken into account. Then, based on distributed nonlinear model predictive control (DNMPC) method, multi-objective control strategies are proposed for the leader and followers, cooperatively. Furthermore, a weight coefficient optimization method is presented, to further improve the platoon’s multi-objective synthesis performance. Finally, comparative experiments are carried out. Results demonstrate that, compared with the classic cruise control method of vehicle platoons, the proposed approach can reduce energy consumption by more than 5% and improve tracking performance on the premise of passenger comfort. Real-road experiments verify that the proposed control system can function effectively and satisfy the computational requirements in real applications.

A Novel Fuzzy-SAE Control Method for an Improved Test Wind Tunnel Simulating Sand/Dust Environment

The sand/dust environment is an important cause of aircraft failure. A sand/dust environment simulation experiment must be devised to meet the standard technical requirements. Therefore, this article designs the control system for a sand/dust environment test tunnel, including a wind speed control system and a pneumatic conveying and concentration control system. A fuzzy intelligent control method and a deep neural network are used to track and control experimental parameters. Compared to the classic PID algorithm, this method achieves smaller overshoot, faster response speed, no steady error and a better dynamic response curve, as demonstrated by both the test result in the wind tunnel and a simulation result. Both the classic PID control method and the high-precision fuzzy control method are fast, stable, and robust. The fuzzy-SAE intelligent control method not only has the high accuracy of the classic PID control method but also has the high speed, stability, and robustness of fuzzy control, which can meet the intelligent control requirements of the sand/dust environment test equipment.

Walking Speed Control of Planar Bipedal Robot with Phase Control

In this study, we aim to provide the dynamic walking of a five-link planar bipedal robot without the need for optimization and regardless of initial conditions. Since bipedal robots are underactuated systems, classical control methods cannot be applied. Joint trajectories must be determined to control the robot. The trajectories that will allow the biped robot to walk can be derived with a trajectory optimization. Periodic stability and dynamic balance are provided by following the trajectories with virtual constraints method. However, determining the optimization-based trajectories before walking is the weakness of the virtual constraints method. As a solution, the method we call “phase control” is newly proposed. Thanks to the phase control method, the need for trajectory optimization is eliminated. Walking speed can be adjusted adaptively depending on user input without interrupting walking. Although the walking speed changes, it is shown with the limit cycle and Poincaré return map that the periodic stability is provided. In addition to mathematical model-based simulation, a physics engine-based Gazebo simulation is also performed to test the feasibility of the method. The advantage of the proposed phase control method is that the planned trajectories can provide limit cycles even if they are not optimized. This advantage provides freedom of trajectory selection that does not require optimization.

Efficient Simplified Current Sensorless Dynamic Direct Voltage MTPA of Interior PMSM for Electric Vehicles Operation

In this paper, an energy efficiency improvement strategy is developed using a simplified current sensorless dynamic direct voltage maximum torque per ampere (MTPA) speed control method of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle applications. Tracking the MTPA angle using a unique voltage amplitude without current sensing at any electric vehicle's speed yields minimum current/power consumption and high energy efficiency. These aims are achieved by considering the dynamic model of the motor that improves the controller reaction and accuracy during the transient states as contrary to the existing literature. Moreover, the simplified current sensorless dynamic direct voltage control (DDVC) technology contains a couple of control gains versus three times that with the classical MTPA Field-oriented control (FOC) method. Moreover, a comparative validation of the simplified current sensorless dynamic direct voltage MTPA methodology and the FOC strategy is executed. Experimental results with energy consumption measurements and energy efficiency study prove that the proposed simplified DDVC MTPA strategy is a promising alternative to the existing MTPA technologies of IPMSMs' drives as it preserves high energy efficiency during transient.

A Dual-Loop Robust Control Scheme With Performance Separation: Theory and Experimental Validation

A dual-loop robust control scheme and its property of performance separation are presented in this article. The dual-loop control scheme consists of two degrees of freedom for nominal and robust performances, with the nominal controller being any stabilizing controller in the observer-based state-feedback form and robust controller being a standard <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> controller. When there is model error and/or disturbance, the robust controller is activated to compensate the nominal controller; otherwise, the dual-loop control returns to a single-loop nominal controller. We also show that the nominal and robust performances of the dual-loop control are independent of one another. As a result, the nominal and robust controllers can be designed separately offline, and then, online coordinated in the dual-loop control. Furthermore, the state-space realization and controller implementation are also provided. Finally, a two-wheeled robot with varying slip effect is considered as an illustrative example. Both simulation and experimental results show that the dual-loop control outperforms the classical robust control methods.

Optimized control of three-phase inverters to minimize total harmonic distortion in a grid-connected photovoltaic system

Currently, the energy transfer process to the grid of the PV system is based on the importance of less harmonics and high efficiency. The evaluation of harmonics distortion of current is based on the value of THDi (&amp;lt;&amp;lt;5%), this last becomes very high if the current leakage is high, which causes losses of the grid and safety problems. Therefore, our job is to improve the classic control method of PV inverters used to reduce grid loss and improve electricity prices. This study is a proposal toward the modelization and improvement of the three-phase two-level, and multi-level photovoltaic (PV) inverter command, using space vector, and sinusoidal control based on controlling the active and reactive current delivered into the grid indirectly with a resonant controller (PIR) for a nonlinear load (NLL). The results of the simulations obtained by the new control methodology, SPWM, and SVPWM show that its performance is better compared with the simple modulation (PWM); the total harmonic distortion (THD) of current in both methods (SVPWM) and (SPWM) is better than that obtained with conventional commands. Also, multilevel converters control produces better quality waveforms, reducing current harmonic distortion.

Development of Discrete-Time Waterjet Control Systems Used in Surface Vehicle Thrust Vectoring

To create an autonomous surface vehicle, the microcontroller that will be responsible for the vessel’ s response will have access to both engine thrust controls and steering controls. In the case of a vehicle with waterjet propulsion, the term “controls” refers to engine RPM and nozzle angle. The latter allows the thrust vectoring. Having performed the mathematical modelling of the surface vessel, a method must be found such that the microcontroller actions on the controls create the desired outputs, based on predefined performance objectives. A commonly selected control algorithm considered an industry standard for control applications—at least from the perspective of classical control methods—is the PID control scheme. Many times, a PID control scheme is sufficient for achieving the required performance. There are also many methods that extend the functionality of the PID to robust control schemes, to accomplish more specific targets, such as noise rejection. The vast majority of all these implementations, will have to be implemented using a digital microcontroller or an FPGA. This paper assists theoretically in implementing PID controllers digitally, by elaborating on the discrete-time perspective of PID control. It also provides a test method for evaluating the algorithmic implementation.