Design Of Control Algorithms Research Articles

Centralized Model Predictive Control With Human-Driver Interaction for Platooning

Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centralized model predictive controller for a heterogeneous platoon of vehicles to reach a desired platoon velocity and individual inter-vehicle distances with driver-selected headway time. As a novel concept, we allow for interruption from a human driver in the platoon that temporarily takes control of their vehicle with the assumption that the driver will, at minimum, obey legal velocity limits and the physical performance constraints of their vehicle. The finite horizon cost function of our proposed platoon controller is inspired from the infinite horizon design. To the best of our knowledge, this is the first platoon controller that integrates human-driven vehicles. We illustrate the performance of our proposed design with a numerical study, demonstrating that the safety distance, velocity, and actuation constraints are obeyed. Additionally, in simulation we illustrate a key property of string stability where the impact of a disturbance is reduced through the platoon.

A new magnetorheological quasi-zero stiffness vibration isolation system with large zero stiffness range and highly stable characteristics

Various quasi-zero stiffness (QZS) systems have been developed and applied in the vibration control domain in recent years. However, most QZS systems are usually unstable against external disturbances, and their QZS ranges are very limited. To address these issues, this study develops a highly stable QZS vibration isolation system integrated with magnetorheological fluids (MRFs). The MRFs endow the vibration isolation system with stiffness variability in vertical and lateral directions to against external disturbances, which innovatively solves the unstable problem of QZS systems. Meanwhile, the stiffness variability also makes the system adaptable to vibrations with different frequencies, so the system can deliver the best vibration isolation performance in response to various excitations. The system consists of a vertical isolation unit and a lateral isolation unit. By paralleling a nonlinear positive stiffness QZS component with a nonlinear negative stiffness QZS component in the vertical isolation unit, a large QZS range in the vertical direction and smaller stiffness are realised, thus improving the vibration isolation performance. In this study, the vibration isolation system is designed and prototyped; its QZS characteristics and adjustable stiffness features in both the vertical and lateral directions are experimentally verified; the frequency responses of the system are obtained experimentally; and the stability and the vibration isolation performance of the system are also evaluated by experiments with the designed control algorithms. This study provides a solution to overcome the unstable problem of QZS systems and extend the limited QZS range, whilst realising QZS characteristics in both vertical and lateral directions, thus broadening the application of QZS systems.

Gripper Design and Motion Control Algorithm Development for Oyster Handling

Gripper Design and Motion Control Algorithm Development for Oyster Handling

Adaptive Predefined-Time Bipartite Consensus Tracking Control of Constrained Nonlinear MASs: An Improved Nonlinear Mapping Function Method.

This work focuses on the problem of predefined-time bipartite consensus tracking control for a class of nonlinear MASs with asymmetric full-state constraints. A predefined-time bipartite consensus tracking framework is developed, where both cooperative communication and adversarial communication among neighbor agents are implemented. Different from the finite-time and the fixed-time controller design methods for MASs, the prominent advantage of the controller design algorithm presented in this work is that our algorithm can make the followers track either the output or the opposite output of the leader within the predefined time in accordance to the user requirements. In order to obtain the desired control performance, an improved time-varying nonlinear transformed function is skillfully introduced for the first time to handle the asymmetric full-state constraints and radial basis function neural networks (RBF NNs) are employed to deal with the unknown nonlinear functions. Then, the predefined-time adaptive neural virtual control laws are constructed by using the backstepping technique, while their derivatives are estimated by the first-order sliding-mode differentiators. It is theoretically testified that the proposed control algorithm not only guarantees the bipartite consensus tracking performance of the constrained nonlinear MASs in the predefined time but also remains the boundedness of all the resulting closed-loop signals. Finally, the simulation research on a practical example shows the validity of the presented control algorithm.

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

AbstractA novel strategy using a chaotic gravitational search algorithm (CGSA) based nonlinear PID control scheme, which is validated through a laboratory helicopter model called the twin rotor system, is presented in this paper. In this work, CGSA is used as a stochastic based global optimization algorithm for controller design in the twin rotor system adopted. The fine chaotic search process used in CGSA obtains the optimal solution in the iterative process based on the current best solution. The goal of the controller design in this paper is to stabilize the twin rotor system with considerable cross couplings to reach the selected position and follow the desired trajectory effectively. The addition of nonlinear functions to the PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement a nonlinear PID control scheme for the angular displacements of the twin rotor system with minimization of the integral square error (ISE) as the fitness function in the algorithm. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. From the simulation results it is observed that the proposed work shows better performance than the other evolutionary computation techniques. The results also indicate the advantage of the proposed CGSA based tuning for the two degree of freedom MIMO control with standard reference trajectories as per the TRMS330‐10 model.

Enhancing the Performance of a Renewable Energy System Using a Novel Predictive Control Method

The current study concerns improving the performance of a renewable energy system using systematically designed control algorithms. The performance of the system under study is evaluated under two operating scenarios: the first in which the system consists of only a wind-driven synchronous generator connected to the utility grid; in the second scenario, the generator is combined with a photo-voltaic solar system and a battery for supplying a load. Each system component is modeled and thoroughly described. To maximize the benefits of solar and wind energies, two separate maximum power point tracking procedures are adopted. Furthermore, to enhance the generator’s dynamics, a novel predictive control scheme is designed and validated by comparing its performance with traditional predictive control. The novel predictive controller utilized a simple and unique cost function to avoid the shortages of traditional predictive controllers. For standalone operation, an effective procedure is adopted to ensure the power balance between the generation, storage, and isolated load units. To evaluate the effectiveness of the designed controllers under different operating regimes, Matlab/Simulink is utilized for this task. The obtained results confirm the superiority of the novel predictive scheme used with the synchronous generator over the classic control approach for the two operating scenarios. This has been shown in the form of reduced ripples and reduced current harmonics. The obtained results are also confirming the validity of the adopted maximum power tracking strategies with solar panels and wind turbines as well. Furthermore, balanced power delivery is achieved thanks to the adopted management strategy for standalone operation, which enhances the overall system performance.

Predefined-time stabilization of brushed direct current motor system affected by matched and unmatched disturbances and stochastic noises

This paper presents the predefined-time convergent robust controller design for a brushed direct current (DC) motor system affected by matched and unmatched deterministic disturbances and stochastic noises, considering both fully measurable and incompletely measurable states. The control algorithm allows the control designer to set the convergence time a priori. The convergence time is independent of initial conditions and disturbances and noises affecting the system. Numerical simulations are conducted to demonstrate efficiency of the designed control algorithm. The obtained results show that the control algorithm counteracts the matched and unmatched disturbances, and noises in case of fully measurable states and mitigates their influence in case of incompletely measurable ones.

A dynamically similar lab-scale district heating network via dimensional analysis

Strict user demands and large variability in external disturbances, along with limited richness in the data collected on the daily operating conditions of district heating networks makes the design and testing of novel energy-reducing control algorithms for district heating networks challenging. This paper presents the development of a dynamically similar lab-scale district heating network that can be used as a test bench for such control algorithms. This test bench is developed using the Buckingham π theorem to the match the lab-scale components to the full-scale. By retaining the relative thermodynamics and fluid dynamics of a full-scale network in the lab-scale system, the experimental setup allows for repeatability of the experiments being performed and flexibility in the testing conditions. Moreover, the down-scaling of the experiment is leveraged to accelerate testing, allowing for the recreation of operating periods of weeks and months in hours and days. A PID controller is implemented on the lab-scale test bench to validate its response against literature data. Results show 63% efficiency during heating operations compared to 70% efficiency for a similar full-scale system, with comparable pressure losses across the system.

The design of adaptive control algorithm for chopper power supply

The design of adaptive control algorithm for chopper power supply

Bi-fractional-order reference model parameters-based control system design

It is known that analytical manipulation of the time-domain representation of fractional order transfer function is, hitherto, a challenging task. In this study, a controller design methodology based on the direct synthesis design method is proposed using bi-fractional order transfer function as a reference model and certain time-domain criteria. First, an analysis examines the effects of bi-fractional order transfer function parameters, that is, commensurate fractional order, damping ratio and natural frequency, on the system time-domain criteria. This examination has allowed us to set up a relation between damping ratio and commensurate fractional order. Next, a polynomial function fitting is established in order to express the damping ratio in terms of this commensurate order. Bi-fractional-order reference model is regenerated using the above-mentioned relationship. Furthermore, a control design algorithm that utilizes the newly derived bi-fractional order reference model is developed by considering control signal limitations. The proposed fractional order control design method is then compared with globally optimized fractional order proportional-integral-derivative (PID) controllers under the same circumstances and performance index. Finally, the implementation of the proposed controller design algorithm is done on a real-time active suspension system. The results are very satisfactory and incoherent with the simulations.

Dynamic event-triggered-based global output feedback control for stochastic nonlinear systems with time-varying delay

The current study addresses the issue of global dynamic event-triggered control based on reduced-order observer for a class of nonstrict-feedback stochastic systems in the presence of time-varying delay. Firstly, a lemma about the variable separation technique is proposed and proved for dealing with nonlinear functions in stochastic time-delay systems, which avoids the restrictive growth assumption that nonlinear functions need to satisfy. Secondly, a reduced-order observer is devised to reconstruct partial unmeasured state variables in systems, which equilibrates the control behavior between output feedback and state feedback. Then, without imposing any constraints on the derivative of time-varying delay, a dynamic event-triggered controller based on the symbol function technique is constructed to reduce the waste of resources in data transmissions and improve the communication efficiency. Furthermore, in the light of the stability theory of stochastic systems and Lyapunov-Razumikhin lemma, it is proved that the designed control algorithm can ensure that the controlled system is globally asymptotically stable in probability. Finally, two simulation examples are provided to verify the effectiveness of the developed approach.

Developing a special microwave oven: Assessment of its performance for dough fermentation and nutrient soup elaboration

Bread and soup are two of the most important foods in daily life, thus dough fermentation and nutrient soup elaboration are more and more popular, but there is a lack of relevant low-cost and high-reliable household appliances on the market. Therefore, this paper proposes automatic control methods for dough fermentation and nutrient soup elaboration based on a special microwave oven. Fermentation theory, run-up microwave fermentation principle, microwave extraction principle, NTC temperature probe design and scalable fuzzy control algorithm are described in detail. Besides, the experimental platform is set up with a temperature chamber, an optical fiber thermometer and a power meter. Experimental results demonstrate that the relationship between the heating time and flour's mass is linear. For different ambient temperature tests, the volume ratios of the fermented dough to unfermented dough of different cases range from 2.2 to 2.62, and the inside of the dough after fermentation is fluffy, with small and dense cavities. Meanwhile, there is no acid taste and skin dryness, and the power consumption of microwave fermentation is less than half of that induced by grill, convection or steam fermentation. The detection error of the NTC temperature probe with microwave shielded is 0.48 °C, and the control error of the closed loop system is less than 0.5 °C. The temperature-rise slope of water is lower than that of ingredient, and the water's temperature is about 1 °C less than that of the ingredient. The soup after microwave elaboration is amber and clear, the ingredients are intact, the water loss is less than 50 g, and the total power consumption is 684 Wh. In short, microwave-based control methods for dough fermentation and nutrient soup elaboration are effective.

Predictive Speed Control of a DC Universal Motor Applied to Monitor Electric Vehicle Batteries

This article deals with the design of a simple predictive control algorithm applied to a bidirectional DC-DC power converter for the angular speed control of a DC motor. We used the dynamics of a DC motor but mathematically reduced them to arrive at a simple model that is ideal for our purpose, not only to meet the control objective but also to generate reliable data for further analysis. This predictive control approach is based on the discrete time mathematical model of a DC motor. A huge capacitor to emulate an electric vehicle battery was then successfully connected to our experimental platform. Due to the robustness of the proposed control algorithm, the same predictive control scheme provided sufficient information to monitor the battery’s state. On this basis, and due to the system’s efficiency, it was possible to configure a fault detection scheme in our electric car battery emulator using only classical statistical tools. A PIC18F252 microcontroller was used in our experimental platform to implement our predictive control algorithm. It was then appropriately coupled to the power electronics required by the DC-DC converter to drive the DC motor. Our experimental results proved the excellent performance of the control method and also of the health monitoring system. On the other hand, the main difficulty in achieving our main goal was the realization of discrete control, which had to be as simple as possible while maintaining the control objective and while also being capable of generating reliable data for the health monitoring stage. Thus, the primary contribution of this work was the development of the predictive control of the speed of a universal motor, followed by the modification of the experimental design to simulate an electric car battery and the introduction of a novel statistical method for fault detection.

Gaze Point Tracking Based on a Robotic Body-Head-Eye Coordination Method.

When the magnitude of a gaze is too large, human beings change the orientation of their head or body to assist their eyes in tracking targets because saccade alone is insufficient to keep a target at the center region of the retina. To make a robot gaze at targets rapidly and stably (as a human does), it is necessary to design a body-head-eye coordinated motion control strategy. A robot system equipped with eyes and a head is designed in this paper. Gaze point tracking problems are divided into two sub-problems: in situ gaze point tracking and approaching gaze point tracking. In the in situ gaze tracking state, the desired positions of the eye, head and body are calculated on the basis of minimizing resource consumption and maximizing stability. In the approaching gaze point tracking state, the robot is expected to approach the object at a zero angle. In the process of tracking, the three-dimensional (3D) coordinates of the object are obtained by the bionic eye and then converted to the head coordinate system and the mobile robot coordinate system. The desired positions of the head, eyes and body are obtained according to the object's 3D coordinates. Then, using sophisticated motor control methods, the head, eyes and body are controlled to the desired position. This method avoids the complex process of adjusting control parameters and does not require the design of complex control algorithms. Based on this strategy, in situ gaze point tracking and approaching gaze point tracking experiments are performed by the robot. The experimental results show that body-head-eye coordination gaze point tracking based on the 3D coordinates of an object is feasible. This paper provides a new method that differs from the traditional two-dimensional image-based method for robotic body-head-eye gaze point tracking.

Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control

Trajectory tracking is a crucial aspect of controlling nonlinear systems and is an important area of research. Researchers have proposed several strategies to perform this task in the presence of perturbations, which are the sum of a system’s uncertainty, modeling errors, and external disturbances. Nonlinear systems, such as robot manipulators, have complex dynamics, and deriving their exact mathematical models is a tedious task. Therefore, the objective of this research is to design a model-free form of control for such systems. To achieve this goal, a sliding mode control (SMC) with a proportional-integral-derivative (PID) sliding surface was designed and integrated with a saturation-function-based extended-state observer (ESO). In an extended-state observer (ESO), the primary concept is to define the system’s perturbation. The ESO estimates the system’s states and perturbation, including the known and unknown dynamics, uncertainties, and external disturbances, which are considered as perturbations. The estimated perturbation is used in a closed loop to cancel the actual perturbation. This perturbation-rejection technique improved the controller’s performance, resulting in reduced position error, reduced sensitivity to low-frequency elements of perturbation, and a small magnitude of switching gain. The designed control algorithm requires minimal information about the system, specifically position feedback, and, therefore, there is no need to identify the system parameters. A mathematical analysis of the designed algorithm was performed in detail, and the algorithm was compared with the existing ESO-based SMC algorithm. Simulations were conducted using MATLAB/SimMechanics on two different systems, and the comparison results validated the performance of the designed algorithm in comparison to previous research.

Adaptive backstepping dynamic sliding mode control for maglev turning motor spindle based on limited time preset performance

In the process of turning a maglev motorized spindle, there are problems such as system model time-varying, cross-coupling of control parameters, difficult measurement of system state variables, and nonlinear characteristics of active magnetic bearings, which lead to the inevitable cutting chatter phenomenon, difficult control algorithm design, and then the reduction of workpiece surface quality and accuracy, affecting machining efficiency and tool life. In this paper, the “multimodal-distributed parameter” model is extended to the “magnetic bearing-rotor-workpiece” variable mass turning process. An adaptive backstepping fast dynamic terminal sliding mode control is designed to address the model’s time-varying parameters and cross-coupling issues. In view of the difficulty in measuring the vibration displacement at the cutting point, the displacement field reconstruction method was introduced to reconstruct the vibration displacement field online and provide effective feedback for the previously designed control strategy. Finally, the proposed controller is applied to adjust and control the turning process of a maglev motorized spindle and compared with other advanced controllers. The simulation results show that the proposed control method has a better control effect than other control methods in the presence of unmodeled dynamics, uncertainties, and external disturbances.

Meta-Reinforcement-Learning-Based Current Control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes

Data-driven reinforcement learning-based controller schemes have much potential to aid the design of model-free control algorithms that can be trained without the necessity of plant-specific parameter knowledge. Unfortunately, the corresponding training phase is a time- and possibly money-consuming process which needs to be repeated whenever application to a new plant system is requested. To reduce the total training time for a large set of heterogeneous plant systems, this article proposes a meta-reinforcement learning-based approach that is to be utilized for control of hundreds of different permanent magnet synchronous motor drives ranging from a few watts to hundreds of kilowatts. So-called context variables carry the meta information about the set of considered drive systems. Their estimation using a corresponding artificial neural network as context approximator is a core aspect of this article. The context information allows the reinforcement learning-based control algorithm to automatically adapt itself to individual motor drives without requiring individual plant training. Since the found context variables can also be interpreted as an implicit system identification result they allow to determine irregular plant behavior (e.g., faulty drives) as an added bonus of the proposed meta-reinforcement learning scheme. Empirical results during this proof of concept successfully validate the potential of the proposed approach to drastically reduce the total training time and encourage further research.

Research on AGV path tracking method based on global vision and reinforcement learning.

As the key to the movement of automated guided vehicle (AGV), the design of control algorithm directly affects whether AGV can follow the preset path. Aiming at the difficulty of AGV control, an AGV path tracking control method based on global vision and reinforcement learning is proposed. Firstly, the global view is obtained by the visual sensor, and the position information of obstacles and AGV is obtained by the target detection algorithm. Secondly, the path planning algorithm is used to obtain the driving path information which is used to establish a virtual environment. Thirdly, the position and pose of the physical AGV are introduced into the virtual environment by the visual sensor, and the virtual AGV is reset. Finally, the image obtained by virtual vehicle camera is input into the reinforcement learning model and the output action is sent to the physical AGV for execution. In the experimental part, this method can not only plan the driving path in different environments but also well control AGV to drive along the specified path, which proves that this method has strong robustness and feasibility.

REALISATION OF MPC ALGORITHM FOR QUANSER QUBE-SERVO

This paper offers an in-depth look into the design and implementation of a Model Predictive Control (MPC) algorithm for the QUANSER QUBE-SERVO system. The QUBE-SERVO is a sophisticated laboratory experimental setup consisting of a servo motor, an encoder, and a rotary module. This combination provides a robust platform for investigating and testing various control strategies. In particular, the central focus of this study is the usage of the MPC algorithm for controlling the position of the QUBE-SERVO’s rotary disc load module. The MPC algorithm plays a pivotal role in this application by predicting the future behaviors of the system, and controlling the system by minimizing an objective function over a defined finite horizon. This makes it a versatile and effective tool for controlling complex systems. One of the key challenges in practical control applications is maintaining system stability in the presence of disturbances and uncertainties. To this end, we propose a MPC algorithm designed specifically to stabilize the QUBE-SERVO under such conditions. The functionality of this algorithm is not limited to the QUBE-SERVO system alone, and can be extended to other control systems exhibiting similar characteristics. The effectiveness of the proposed MPC algorithm is rigorously tested through simulation studies. These studies involve subjecting the QUBE-SERVO to various reference signals and disturbances. The results of the simulations provide strong evidence of the algorithm’s capability to effectively track reference signals, while also rejecting disturbances and uncertainties, thereby corroborating its efficacy for the QUBE-SERVO application. Moreover, the original MPC algorithm was enhanced to improve its performance for trajectory tracking tasks. We also discuss the integration of the MPC algorithm within the MatLAB and LabVIEW programming environments, which served as the base platforms for designing and running the simulations in this project. This paper, therefore, presents a comprehensive and practical approach for the successful implementation of the MPC algorithm in the QUANSER QUBE-SERVO system, and demonstrates its potential for wider application in similar control systems.

A New Robust Adaptive Control Method for Complex Nontriangular Nonlinear Systems

The existing research studies on adaptive control frequently introduce many parameter estimations and lead to a complicated controller. This paper investigates the robust regulation issue for high-order system and plants to raise a new approach for adaptive control. Specifically, the considered system has odd system power, nontriangular form, and external disturbance. By introducing the transformations of a parameter estimation, the studied system is transformed into a new dynamic system. By employing fuzzy systems and some inequality skills, the appropriate bounds of nonlinear terms are established. Based on the adaptive method and homogeneous control, a recursive control design algorithm is provided to construct a new adaptive controller, which dominates those uncertain bounds and guarantees that the closed-loop system is semiglobally uniformly ultimately bounded (SUUB). The constructed controller employs only one adaptive law and has a much simpler form. Simulation examples verify the validness of the presented method.