Kind Of Control Algorithm Research Articles

NClSilico: A Closed-Loop neuromodulation platform in silico

The closed-loop electrophysiology is gradually applied to targeted stimulation of nervous system. According to desired neural biomarkers acquired by biological signal measurement, the stimulus pattern can be automatically adjusted with the suitable closed-loop control algorithms. Moreover, the neural computing uses the numerical computing models to simulate the structures, functions and relations of the neurons and the neural networks, as the substitute of biological brain. As the prevalence of the hardware-in-loop (HIL), the HIL closed-loop electrophysiology not only bypasses the problem of biological experiments but also makes up for the insufficient efficiency and real-time performance of software digital simulation. This paper proposes a hardware experiment platform mainly made up of TMS320F28377D digital signal processor (DSP) and AMD Ryzen 5 5600X 6-Core Processor (CPU) for closed-loop electrophysiological research. The hardware platform is programable, portable, real-time and supports the repeated experiments. Closed-loop electrophysiological experiments with different kinds of control algorithms, stimulation waveforms, feedback variables or neuron models can be implemented on the platform, it may promote the development of researches in the field of closed-loop neuroscience and biomedical engineering.

Control of the Vehicle Inertial Suspension Based on the Mixed Skyhook and Power-Driven-Damper Strategy

With the development of the traditional semi-active suspensions, different kinds of control algorithms have been proposed and applied, including skyhook (SH), acceleration-driven-damper (ADD), power-driven-damper (PDD), the mixed SH and ADD (SH-ADD), mixed SH and PDD (SH-PDD), and others. Among them, the vibration suppression in a wide frequency domain is realized by the SH-ADD or the SH-PDD, but the latter is more effective. Subsequently, the vehicle inertial suspension has been found to have better vibration suppression performance than the conventional suspension in the low frequency band. In order to suppress body vibration in a wide frequency domain to improve overall ride comfort, the inertial suspension using SH-PDD is proposed to further improve the vibration isolation performance due to its superiority of low frequency resistance in this article. First, an inertial suspension model of SH-PDD strategy is established, and the superior characteristics of an inertial suspension in a low frequency range are verified in numerical analysis. Then, the impact of the damping coefficient of the SH-PDD control strategy on the dynamic output performance of an inertial suspension is studied. In addition, the parameters of the proposed inertial suspension are optimized by means of the particle swarm optimization (PSO) method. By designing the error dynamic equation and sliding mode switching function, a sliding mode variable structure controller for an inertial suspension is finally constructed. Simulation results show that compared with the SH-PDD conventional suspension and the conventional suspension, the RMS value of body acceleration of the SH-PDD inertial suspension is reduced by 16.6% and 32.2% respectively, and the reduction is mainly in a low frequency range. Therefore, the proposed inertial suspension possesses better attenuation ability for the vehicle body vibrations to improve ride comfort.

Nonlinear PI Control for High-Order Agents With Unknown High-Frequency Gain Signs Under Switching Topologies

Existing results on dealing with unknown high-frequency gain signs (UHFGSs) mainly adopt the Nussbaum-type functions. A new kind of control algorithms with nonlinear PI functions are presented to cope with UHFGSs. It is rigorously proven that the proposed algorithms with properly selected nonlinear PI functions can guarantee consensus for high-order multi-agent systems (MASs) under switching topologies with uniformly quasi-strongly $\delta $ -connected graphs (UQSGs). Furthermore, we also investigate the output leaderless consensus of heterogeneous agents with UHFGSs. Finally, the numerical examples are illustrated to show the validity of the proposed results.

Intelligent Method of Computer-Aided Design and Checking of Real-Time Spacecraft Control Algorithms

The paper is devoted to description of the method for computer aided design of consistent real-time spacecraft onboard control algorithms. This kind of control algorithms should organize synchronized cooperation of different onboard apparatus and functional software for successful completion of space exploration mission. We introduce the mathematical models for onboard resources, equipment and apparatus, and for real-time control algorithm to be implemented by onboard real-time software. The important feature of the proposed model is considering that each onboard apparatus consumes some onboard resources, thus consistent control algorithm must not violate limits of these resources. There is discussion devoted to construction of effective and consistent real-time control algorithm which guarantees completion of the mission in case of limited accessible onboard energy, computer power, etc. We also present the specially developed CAD/CASE tool allowing automated checking of existing algorithms whether they violate the resource constraints as well as design of new algorithms from scratch.

Current development on using Rotary Inverted Pendulum as a benchmark for testing linear and nonlinear control algorithms

Abstract Rotary Inverted Pendulum (RIP) is an under-actuated mechanical system which is inherently nonlinear and unstable. For decades, it has been widely used as an experimental setup to explain and test different kinds of control algorithms. The main control objectives of RIP are: Swing-up control, stabilization control, switching control and trajectory tracking control. All these control objectives are described in this study. State-of-the art works proposed for each control objective have also been reviewed. These comprise the linear, nonlinear time invariant, self-learning and adaptive nonlinear controllers. Moreover, different kinds of nonlinear dynamic models of the RIP together with the developed linear models in the literature have been analyzed. This is because most of the proposed controllers applied on RIP are found to be model dependent since they are mainly based on integral and/or invariant motion. Other types of RIP are also reported along with their advantages. Future research opportunities and challenges of the previous approaches in this area of research are presented. We believe that expert researchers can use this paper as starting point for further advancement while graduate scholars can use it as an initial point.

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

SUMMARYModel predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

Height control for a two-mass hopping robot based on stable limit cycle

Differing from the commonly used spring loaded inverted pendulum model, this paper makes use of a two-mass spring model considering impact between the foot and ground which is closer to the real hopping robot. The height of upper mass which includes the upper leg and body is the main control objective. Then we develop a new kind of control algorithm acting on two levels: The upper level aims to achieve the desired velocity of the upper mass based on a stable limit cycle, where three different controllers are used to regulate the limit cycle; the target of the lower level is to drive the system to converge to the desired state and control the contact force between the foot and ground within an appropriate range based on the inner force control at the same time. Simulation results presented in this paper confirm the efficiency of this control algorithm.

Research on Stability Control Algorithm of Slotted ALOHA

Because of its simplification, slotted ALOHA is comprehensive used in the satellite and wireless communication. While slotted ALOHA is essentially unstable. Therefore various kinds of control algorithms are applied in order to keep stable throughput of the communication system. In this paper, p- Persistent Control Algorithm (pPCA) of slotted ALOHA is analyzed using binomial distribution model, design of algorithm is finished and throughput is simulated. Markov Model of Binary Exponential Backoff (BEB) Algorithm is established on the basis of which Binary Exponential Backoff Algorithm is designed whose performance is tested through numerical analysis and simulation. Finally, Operating principle of Pseudo- Bayesian Control Algorithm (PBCA) is analyzed utilizing Poisson distribution model of input stream, corresponding algorithm is designed and throughput is done simulation test. Then comparisons are done on the properties of throughput, adjustment process and complexity of computation among Binary Exponential Backoff Algorithm, Pseudo-Bayesian Control Algorithm and p-Persistent Control Algorithm whose results show that overall performance of Pseudo-Bayesian Control Algorithm is better than that of the other two. Index Terms—Slotted ALOHA; throughput; stability; complexity of computation; Markov Model; adjustment process

Optimal Orientation Planning and Control Deviation Estimation on FAST Cable-Driven Parallel Robot

The paper is devoted theoretically to the optimal orientation planning and control deviation estimation of FAST cable-driven parallel robot. Regarding the robot characteristics, the solutions are obtained from two constrained optimizations, both of which are based on the equilibrium of the cabin and the attention on force allocation among 6 cable tensions. A kind of control algorithm is proposed based on the position and force feedbacks. The analysis proves that the orientation control depends on force feedback and the optimal tension solution corresponding to the planned orientation. Finally, the estimation of orientation deviation is given under the limit range of tension errors.

크루즈선의 횡동요 제어시스템에 미치는 포드 각속도의 영향

Recently, the application and installation of the pod propeller to the cruise ship is dramatically increased. It is because pod propulsion system allows a lot of flexibility in design of the internal arrangement of a ship. To reflect this trend, many researches have conducted to use the pod propeller for the roll stabilization of a ship. In the paper, a roll stabilization controller is designed by using fins and pod propellers as the control actuators for cruise ships. Two kinds of control algorithms are adopted for the roll control system; LQR (Linear Quadratic Regulator) algorithm and frequency-weighted LQR algorithm. Through the numerical simulation, the effect of the turning rate of the pod propeller on the roll control system is analyzed. Analysis of the simulation results indicated that the turning rate of the pod propellers is one of the important parameters which give the significant effects on the roll stabilization.

Research on Steering Control and Simulation of Vehicle Steer-by-Wire System

A simulation model of Steer-by-Wire(SBW) is built, including steering motor model, steering executive system model, vehicle model and Fiala tire model. Based on the Linear Active Disturbance Rejection Control(LADRC) technique, a kind of control algorithm on steer angle of vehicle SBW is designed. On the simulation system, the control algorithm is simulated and test. The results show that the LADRC controller can implement high precision and strong robust control effects on steer control of vehicle SBW system.

A High Performance Control System for Pressurized Headbox of Paper Machines

The pressurized headbox is one of the most important pieces of equipment on a modern paper machine. It is a special two-inputs-twooutputs process, and proper matching of input and output of control loops is very important. Relative gain analysis and computer simulation based on a mathematics model of the pressurized headbox shows that the common used configuration in paper industry is not the best. For this kind of configuration, a multi variable predictive control algorithm is designed. Computer simulation shows that this kind of control algorithm can deal with the headbox control very well. A control system with this kind of controller for full scale pressurized headbox has a good performance with excellent control precision, very short response time and very small offsets.

Vehicle Following Control in Lateral Direction for Platooning

Simulation and experimental study on lateral control for autonomous driving is presented in this paper. The objective of the lateral control system is to make the controlled vehicle follow the preceding vehicle. Simulation study is conducted for investigating the relationship between information for lateral control and the tracking accuracy obtained based on the information. Simulation results of vehicle-following control based on five kinds of control algorithms, each of which is designed using some information for lateral control, are compared and discussed. Experimental study is carried out by use of two vehicles to validate results of the simulation study. By both studies, it is clarified that side slip angle is the essential information for implementing an accurate vehicle-following control.

PID-like controller using a modified neural network

This paper presents an adaptive PID-like controller (PIDLC) using a modified Neural network (MNN) for learning the characteristics of a dynamic system. A PID-like cost function is proposed, and it can be shown that the Proportional-Integral-Dijferential (PID) algorithm is the gradient descent method if a specific cost function is selected. The PIDLC can cope with parametric variation and uncertainty in the controlled plant through online learning. The MNN's learning algorithm is considerably faster through the introduction of a recursive least squares (RLS) algorithm. A simulation shows that this kind of control algorithm is very effective, especially when there are variations in the plant dynamics.

Adaptive model following control of the mold filling process in an injection molding machine

AbstractThis paper is concerned with the practical application of the adaptive model following control (AMFC) theory on the cavity pressure control of the mold filling process in an injection molding machine. The experimental results indicate that the AMFC technique based on the modified Popov‐Landau method is very useful for the cavity pressure control. Two kinds of control algorithms are implemented on a 16 bit microcomputer to control the cavity pressure. The results also show that the AMFC algorithm is superior to the classical PI control in this system when the acrylonitrile‐butadiene‐stryrene (ABS) is injected into a test specimen mold which is designed according to the ASTM code.