Simple PID Controller Research Articles

Non-linear cascade control with gain-scheduling and startup control strategy study for thermionic space reactor TOPAZ-II

The TOPAZ-II thermionic space reactor system, which was designed by the Soviet Union, is characterized by its high degree of nonlinearity and positive temperature reactivity feedback. The thermionic space reactor exhibits characteristics of high inertia and significant delay in controlling its electrical power and outlet temperature. The simple PID controller is difficult to achieve good performance. To carry out controller design for thermionic space reactor, the simulation platform for thermionic space reactor is developed based on coupling between reactor system thermal-hydraulic code RESYS and control system simulator in this study. After that, based on the cascade control strategy and gain-scheduling, the reactor thermal controller, electric power controller, outlet temperature controller applicable for the full power range is designed with transfer function model and frequency domain analysis method. To validate the nonlinear electric power controller performance, the continuous minor step disturbances, major step disturbances, and ramp variation of electric power setpoint is simulated. The performance of outlet temperature controller is verified with the simulation result of step and ramp variation of outlet temperature setpoint. Thereafter, the start-up process of the thermionic space reactor TOPAZ-II is simulated and analyzed. The simulation result reveals that the controller designed in this paper can overcome the nonlinearity of the thermionic space reactor system and has good performance throughout the entire power range. Compared to traditional simple PID controller, the cascade controller has better performance and can achieve good control performance even in situations where simple PID controllers cannot function properly.

Adaptive PID controller based direct torque control of fuel cell fed BLDC drive for industrial drives applications

Fuel cell fed Brushless DC motors (BLDC) are widely used in electrical applications due to their more efficiency, simple and compact volume. With a simple traditional PID controller, it is challenging to adjust the settings and produce highlights with full power. In order to effectively regulate the torque and speed of a BLDC drive, the adaptive PID (APID) Controller based Direct Torque Control (DTC) is proposed in this paper. The proposed controller depended on the extra error of a spin around controller indication to understand non-linearity, parameter fluctuations, and burden transit difficulties that arise in the BLDC motor drive framework, to maintain good torque control performance, DTC is used as a suitable control technique in industrial drives systems. The comparison tests are step changes in the motor drive’s reference speed and torque. Simulations with MATLAB/Simulink and also experimental outcomes are utilized to validate the operation and performance of various vector control methods.

Advanced Multi-Sampling PWM Technique for Single-Inductor MIMO DC-DC Converter in Electric Vehicles

Amongst the various topologies of multi-input multi-output (MIMO) DC-DC converters, single-inductor MIMO (SI-MIMO) converters have the advantages of a reduced component count, a simpler structure, and low cost. These converters are suitable in electric vehicle (EV) applications involving variable ports, essential for performing different functions. Digital control in SI-MIMO converters is promising for enhancing transient performance due to its numerous benefits. However, delays in digital control, particularly computational and pulse width modulation (PWM) delays, can negatively impact the performance of DC-DC converters. Multi-sampling and double PWM update methods can mitigate these control delays, but they often necessitate complex control schemes, adding computational burden. In this work, an advanced multi-sampling PWM technique, integrating sample shift and multi-sampling, is proposed while employing a simple digital PID control scheme. The proposed method was tested for a shared-switch SI-MIMO converter with battery discharging and charging modes in the MATLAB/Simulink environment and compared with the conventional single- and multi-sampling PWM methods. The results demonstrated that the proposed method significantly improved the converter performance, surpassing the conventional single- and multi-sampling PWM methods. In the battery discharging mode, utilizing the proposed method, the output voltage achieved a settling time of 0.075 s in response to a step change in its reference, significantly outperforming multi-sampling, which yielded a settling time of 0.124 s, and single sampling, which exhibited an even longer settling time of 0.898 s. It also demonstrated a minimal overshoot of 0.06 volts compared to 1.5 volts with multi-sampling during the step change in the input voltage. Similarly, in the battery charging mode, upon a step change in the reference output voltage, the proposed method effectively minimized the overshoot of the output voltage to 0.845 volts compared to 1.175 volts with multi-sampling, and it decreased the inductor current settling time to 0.296 s from 0.330 s recorded under multi-sampling. These findings underscore the potential of the proposed method in enhancing the digital control performance of SI-MIMO DC-DC converters in electric vehicles.

Research to Simulate the Ship’s Vibration Regeneration System using a 6-Degree Freedom Gough-Stewart Parallel Robot

This article presents research results on building a model to reproduce ship vibrations based on a Parallel robot with 6 degrees of freedom Gough – Stewart form. Vibration data at the ship’s center of gravity calculated by simulation software will be the input to the model. The regenerative control system uses a simple PID controller to control input trajectory tracking. Simulation results on Matlab/Simulink software have demonstrated the reproduction of ship vibrations within the allowable error.

A fast dynamic pose estimation method for vision-based trajectory tracking control of industrial robots

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector’s pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (±0.11 mm for position and ± 0.05°for orientation).

Sic!PI – simple intuitive PI controller

The PID algorithm constitutes the backbone of process control since more than 100 years. The literature considers hundreds, if not thousands, of its variants, analyzing both theoretical and practical aspects. However, the practice of the process industry is not so rich and is generally limited to its basic formulations, and mostly only PI. This study tries to return to the roots of industrial automation by proposing an extremely simple variant of the PI law, but in a non-linear version. We name it sic!PID: simple intuitive PID controller. The research had two independent sources: scientific and practical. We propose a simple and intuitive formulation, which is analyzed through simulations and practically. The proposed structure is successfully used in several industrial facilities, which easily proves its effectiveness and resilience.

A Proposed Controller for Pitch Angle of Wind Turbine

Wind turbines are complicated non-linear systems with certain random disruptions. The pitch control system is a commonly employed method for regulating the electricity generated by a wind turbine. Many researchers have observed developments in the pitch control field during the last few decades. Traditional PID controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this research evolved a mathematical model that illustrates HAWT's pitch angle control system and applied a proposed Simple Optimal Intelligent PID Controller (SOI-PID). Under various operating conditions, the proposed SOI-PID controller was tested with the Traditional PID, Fuzzy Logic Controller (FLC), and Fuzzy-Adaptive-PID controller. For system simulation, the MATLAB/Simulink software was used. According to simulation results, compared to PID, FLC, and Fuzzy-Adaptive-PID controllers, the proposed SOI-PID controller responds faster and has a better rise and settling time. Other benefits of the SOI-PID controller are its simplicity of implementation and design, distinguishing it from other intelligent algorithms.

Load Emulation with Independent Metering for a Pump Test Bench

For the investigation of new types of internal gear pumps under realistic conditions, a test bench is presented that enables dynamic load emulation via the pressure with simultaneous dynamic speed control of the pump. For pressure control, a hydraulic half-bridge with separate control edges is used on both sides of the pump and a pressure control is presented. An error-based adaptive controller is used for pressure control and tested experimentally. It is shown that the error-based adaptive controller has a better performance compared to a simple PID control.

Hydrodynamic interactions coordinate the swimming of two self-propelled fish-like swimmers

A muscle-contraction model of pre-strains is utilized to conduct a numerical investigation on the collective swimming of two self-propelled fish-like swimmers. To simulate the conscious swimming control exhibited by live fish, a PID control algorithm is integrated with the numerical simulation model. Specifically, in a scenario involving straight swimming, we conduct a comprehensive simulation study on the hydrodynamic interactions between the leading and following swimmers. The research focuses on the effects of phase difference and initial spacing between the two fish on their collective behavior. Notably, our simulation model considers both the alteration of muscular activities and the energy-saving effect during collective fish swimming. The results demonstrate that a simple PID controller can effectively imitate the conscious swimming control of live fish, ensuring the formation of collective mode of two freely swimming fish. By varying the initial spacing and phase difference, the two fish can attain the same swimming speed, and a similar steady flow field structure can be observed within a certain range of parameters. The passive hydrodynamic interactions, acting as both facilitators and impediments, aid in coordinating the swimming of fish individuals. Furthermore, for efficient swimming of the following fish, a ‘lazy’ strategy proves to be essential.

Is Regulatory Approval without Autonomous Operation for Natural Extract Manufacturing under Economic Competitiveness and Climate-Neutrality Demands Still Permissible?

Natural extracts are broadly utilized as remedies, nutrition additives, cosmetics or flavors as well as natural pesticides, fungicides or herbicides. Green manufacturing technologies are of added market value and are sustainable towards the climate neutrality politically demanded for 2045. The concept of digital twins involves experimentally distinct validated process models combined with process analytical technology that is to be adapted to the existing operations. This is a key technology for the autonomous operations in industry 4.0. This paper exemplifies this approach and evaluates the results of the application and implementation efforts of regulated industries. A conductivity sensor for the measurement of the dry residue content and/or Fourier-transformed infrared spectroscopy for marker/lead or reference substance concentration determination are the most feasible and straight forward solutions. Different process control concepts from simple PID controllers (proportional, integral and differential) to advanced process control using digital twin models are evaluated and discussed in terms of industrialization efforts and benefits. The global warming potential CO2 equivalent per kg of natural product could be decreased by a factor of 5–10 as well as the cost of goods, which makes the pay-out time for the industrialization investment less than 1 year and the approach highly competitive. The success rate of the extraction process under regulatory constraints can be raised to 100%, reducing waste, overall solvent consumption, personnel efforts and energy requirements to a minimum.

Design and stability analysis of a nonlinear controller for MRI-compatible pneumatic motors

Among permissible magnetic resonance imaging (MRI) compatible actuators for surgical robots, turbine-based pneumatic motors are noticeably favorited due to its compact size, simpler structure, fast additively manufacturability, and continuous bidirectional motion ability. Yet, the system dynamics of pneumatic motors, especially pneumatic nonlinearity, is often neglected and simple PID control is used, resulting in low system bandwidth (~0.5 Hz). To tackle this obstacle, this paper proposes a model-based nonlinear control method for MRI-compatible pneumatic motors. Dynamic models for a pneumatic motor previously designed by the author are developed first, followed by establishment of the nonlinear feedback controller. An error based Lyapunov candidate function is designed and mathematically proved to be globally uniformly asymptotically stable, guaranteeing desired accuracy. Experiment control tests demonstrate that the proposed control method can achieve satisfactorily better tracking accuracy than a conventional model-independent PID controller with respect to amplitude attenuation and phase delay.

Intelligent Quadcopter Control Using Artificial Neural Networks

An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high dimensions. About ten percent improvement over state-of-the-art controllers is observed and results are reported both numerically and graphically. Promising results encourage to use the type of controller proposed for various real applications.

Modeling and Simulation of an Octorotor UAV with Manipulator Arm

In this paper, the conceptual design, modeling, and simulation are proposed for an octorotor UAV with a manipulator arm. The conceptual design of the octorotor UAV with a manipulator arm is developed, and for the study and analysis, the design is implemented and validated in Matlab (Simulink-SimMechanics) software. The kinematics and dynamics models of the octorotor UAV with a manipulator arm are obtained using the classical Denavit–Hartenberg convention and the recursive Newton–Euler method, respectively. In this sense, a cascade PID controller for the attitude and navigation of the UAV and a simple PID controller for the manipulator arm are proposed and simulated in a closed-loop system in order to highlight the performance of the proposed design. Finally, simulations show the feasibility and behavior of the mathematical model and the flight controller in a closed-loop system.

A Proposed Controller for an Autonomous Vehicles Embedded System

Many research have observable development in the automated vehicle driving field during the last few decades. This research proposed a simple optimum Intelligent PID (SO PID) controller to simplify the automated vehicle motion control. Control of an autonomous vehicle’s steering routines plays an essential key role. Several steering control procedures are proposed that improve automated vehicle performance. The design of secure embedded control systems must overcome the difficulties associated with designing both computing and control systems. Also, this research introduces a model of the autonomous car prototype controlled via an Arduino microcontroller board and the GPS Module to receive the car coordinates. The car moves safely, and autonomously consequently avoiding the risk of human faults. Several algorithms such as angle and distance calculations to the waypoint and obstacle detection are combined to control the car movement.

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

High-gain observer-based pump/valve combined control for heavy vehicle electro-hydraulic servo steering system

Electro-hydraulic servo steering system (EHSSS) has been widely used in multi-axle heavy vehicles. Noteworthy, the traditional EHSSS controlled only by servo solenoid valve has amounts of energy loss in throttling orifice. Although the steering control accuracy is ensured, it leads to low energy efficiency. In this paper, a novel pump/valve combined control (PVCC) EHSSS is proposed to increase the energy efficiency, which only uses one servo motor pump and one servo solenoid valve to drive the steering trapezoid mechanism. Based on the control objectives of low pressure difference in valve orifice and high steering tracking performance, a dual-input-dual-output control strategy is proposed. To guarantee the high steering tracking performance of PVCC steering system, a high-gain observer based sliding mode controller (HGO-SMC) is designed for controlling the spool displacement of servo solenoid valve. During the steering process, the servo motor pump is controlled by a simple speed feedforward and PID controller, so that the pressure difference in throttling orifice is kept at a low value to reduce the energy wasted. The experimental comparison results show that the proposed method can achieve the same tracking performance as valve control EHSSS with less energy consumption.

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

String Stability Guaranteed Lane Change Maneuver for Automated Vehicles with Vehicle-to-Vehicle Communication

Abstract This paper considers a specific scenario that is the incorporative operation of lane change with a vehicle platoon. In other words, the lane-change vehicle will insert into the platoon in the main lane and be a member of the platoon. Fast lane-change operation and keeping safe inter-vehicle distance are the significant issues. This work proposes a two-level control strategy to deal with the issue. The high level deals with the trajectory planing for the longitudinal movement of the vehicle platoon and for the lateral movement of the lane-change vehicle, respectively. A distributed adaptive cruise control algorithm, which guarantees string stability of the platoon system, is employed to manage the longitudinal movement. Desired lateral trajectory is generated by solving an optimization problem by using the dynamic programming (DP) method. With the obtained desired longitudinal and lateral trajectories for the lane-change vehicle, the trajectory tracking is realized by the designed low-level control laws that are simple PID controllers. Simulation tests verify the performance of the control system.

Input-output linearized spacecraft formation control via differential drag using relative orbital elements

The derivation, implementation, and performance assessment of a differential drag based algorithm, designed for spacecraft formation control using a relative orbital elements formulation, is presented. In contrast to similar approaches found in literature, the underlying motion dynamics model used to derive the control law includes J 2 perturbation as well as atmospheric drag perturbation based on a nonlinear density model. The basic idea of the control algorithm is to algebraically transform the nonlinear plant into a linear substitute. The resulting control law can be interpreted as combined feedforward and feedback PID control, applied to the linear substitute model. Motion dynamics and perturbations are accounted for in a feedforward branch while a PID feedback branch undertakes the cancellation of errors caused by model uncertainties and unmodeled physics. For linearized plant models, the computation of a feedforward branch is straight forward from the state transition matrix. In the nonlinear case proposed in this paper, the inclusion of a feedforward signal is achieved using the input-output linearization technique, yielding a nonlinear and globally valid control law. Sample numerical simulations are presented to support the validity of the controller and to demonstrate its performance compared to a simple PID controller. Simulation results show enhanced tracking performance of the mean along-track separation reference signal and reduced control effort. Moreover, input–output linearization enables the control of a large set of operating points using one set of control parameters.