Parameter Adjustment Algorithm Research Articles

A master-slave teleoperation system control model based on model predictive control with fast dynamic response

Robot technology is advancing with constantly expanding applications. Teleoperation system demonstrates its potential to extend human capabilities in unstructured and hazardous environments as an important branch of robotics. However, there are still issues involving insufficient position tracking accuracy and dynamic response capabilities. This article proposes a master-slave teleoperation system control model based on model predictive control (MPC) with a real-time dynamic adaptive parameter adjustment algorithm. We establish a model based on the integral barrier Lyapunov function and construct a dynamic optimization model based on MPC. Then we carry out rolling optimization and feedback correction at each moment to feed the predicted values generated by MPC back to correct the actual tracking trajectories in real-time. Additionally, we utilize an adaptive neural network and robust term compensation to adjust dynamic parameters and eliminate the effects of time delays, external disturbances, and model uncertainties. Theoretical simulation experiments are conducted on the Simulink platform, analyzing position tracking error and dynamic response capabilities of the master-slave robot respectively to verify the proposed model. The results indicate a reduction in the position tracking errors of the master with 10% and slave with 11%, while also showing an improvement in their dynamic response capabilities. The speed error ranges of the master and slave are shortened with 6% and 7% respectively.

Dynamic Performance Analysis and Control Parameter Adjustment Algorithm for Flywheel Batteries Considering Vehicle Direct Action

Traditional methods often ignore the direct influences of vehicle vibration on the flywheel battery system, which leads to an inaccurate analysis of the dynamic performance of the flywheel battery system and its control effect. Therefore, to make up for the deficiencies of existing studies, a more accurate dynamic performance analysis method and efficient control parameter adjustment algorithm for flywheel batteries based on automotive direct action are proposed in this study. First, the influence of road conditions and vehicle driving conditions on the stability of a vehicle is analyzed primarily. Then, the vibration signal generated by the vehicle is transmitted to the vehicle’s magnetic flywheel battery system for analysis, and the accuracy of the analysis process is realized. Then, according to the stability analysis results for the direct action of the vehicle and the actual PID controller, the control parameter adjustment algorithm is summarized using the curve-fitting method. Finally, a performance test is carried out on the mobile experimental platform. Good experimental results show that the flywheel can quickly return to its equilibrium position and effectively reduce the influence of interference from road conditions and different working conditions and improve the robustness. Therefore, the correctness of the theoretical analysis and parameter adjustment method proposed in this paper was effectively verified.

A collaborative approach to integrated energy systems that consider direct trading of multiple energy derivatives

The cooperative model of a multi-subject Regional Integrated Energy System (RIES) is no longer limited to the trading of traditional energy, but the trading of new energy derivatives such as Green Certificates (GC), Service Power (SP), and CO2 will be more involved in the energy allocation of the cooperative model. This study was conducted for the multi- entity RIES cooperative model considering the trading of electronics, GC, SP, and CO2. First, a cooperative framework including wind-photovoltaic generation system (WG), combined heat and power system (CHP), and power-carbon-hydrogen load (PCH) is proposed, and the mechanism of energy derivatives trading is also analyzed. Then, the sub-models of each agent in the cooperative model are established separately so that WG has the capability of GC generation, CHP has the capability of GC and CO2 absorption, and PCH can realize the effective utilization of CO2. Then, the WG–CHP–PCH cooperative model is established and equated into two sub-problems of cooperative benefit maximization and transaction payment negotiation, which are solved in a distributed manner by the alternating directed multiplier method (ADMM). Finally, the effectiveness of the proposed cooperative model and distributed solution is verified by simulation. The simulation results show that the WG–CHP–PCH cooperative model can substantially improve the operational efficiency of each agent and realize the efficient redistribution of energy and its derivatives. In addition, the dynamic parameter adjustment algorithm (DP) is further applied in the solving process to improve its convergence speed. By updating the step size during each iteration, the computational cost, the number of iterations, and the apparent oscillations are reduced, and the convergence performance of the algorithm is improved.

Automatic Inverse Analysis of Asphalt Pavement Field Aging Based on System Identification

Field aging significantly accelerates distresses (fatigue cracking, water damage, etc.) of asphalt pavements due to exposure to the natural environment. Conventional studies explore field aging by laboratory tests of field cores, while little work is devoted to the inverse analysis of aging by nondestructive tests (e.g., falling weight deflectometer (FWD) test). To backcalulate the aging parameters accurately and automatically, an inverse analysis method of asphalt pavement field aging was proposed based on System Identification (SID) in this study. In this inverse procedure, a kinetics-based two-reaction field aging model was employed as the theoretical aging model and the parameter adjustment algorithm was developed based on sensitivity analysis. Fifty sections from four different climate zones in Long Term Pavement Performance (LTPP) database were selected for investigation, where backcalculated moduli from LTPP and field aging temperatures determined by a pavement hourly temperature model were utilized. The results indicated that SID was feasible to inversely analyze the field aging of asphalt pavements automatically with high accuracy and efficiency. To investigate the factors affecting aging parameters, the multiple linear regression analysis and one-dimensional sensitivity analysis were performed. Among those variables, shortwave radiation and cloud cover showed the dominant effects on field aging resistance.

Cost-and-Quality Aware Data Collection for Edge-Assisted Vehicular Crowdsensing

In view of the rapid development of edge computing and vehicular network, edge-assisted vehicular crowdsensing system has brought significant benefits to Intelligent Transportation Systems (ITS). To satisfy the requirements of ITS applications, a substantial number of sensed data are generated continuously, which incurs considerable communication and computation delays. In addition, with fast-moving vehicles and the sheer amount of data, data loss becomes an issue during the process of data uploading. To deal with these challenges, this paper aims to propose a cost-and-quality aware data collection scheme for edge-assisted vehicular crowdsensing system, so as to satisfy the accuracy and timeliness requirements of ITS applications without overloading the network. Particularly, we design a functional architecture for edge-assisted vehicular crowdsensing system, where vehicular sensing, data uploading and sensing parameter adjustment interwork to enable ITS applications. Based on the architecture, two effective algorithms are proposed, which are adaptive clustering and online sensing parameter adjustment. Adaptive clustering algorithm can support reliable and timely data uploading by taking into account the cluster stability and the communication reliability during the process of clustering vehicles. Online sensing parameter adjustment algorithm can guide the actions of intelligent vehicles based on the real-time traffic condition, which can achieve a tradeoff between sensing accuracy and communication overhead. In order to evaluate the performance of our scheme, we develop a simulation platform in which all the components in the system are built. By conducting extensive simulation, the superiority of the proposed scheme is validated.

Dynamic Phase Signal Control Method for Unstable Asymmetric Traffic Flow at Intersections

This paper addresses the limitations that the phases proposed in variable phase sequencing studies for stochastic traffic flow are all predetermined and that the variable phase sequencing is only suitable for low traffic volume environment. It presents a dynamic phase signal control method for unstable asymmetric traffic flow with two primary operational objectives: the realization of a dynamic phase scheme in each cycle and optimization of signal control parameters. First, an asymmetric state of traffic flow at signalized intersections is defined, rules governing the generation of the dynamic phase of each cycle based on asymmetric state are proposed, and the delay variations of intersections adopting dynamic phase schemes are modeled. Next, a signal control parameter adjustment algorithm for the dynamic phase is constructed to maximize the positive benefits of delay variation. Last, the operational performance of the proposed method is validated using data collected from an intersection in Harbin, China, by VISSIM simulation. Furthermore, it is found that a higher asymmetric coefficient leads to lower efficiency of a symmetrical release phase scheme at intersections, and the increase of average delay becomes significant when the asymmetric coefficient threshold is greater than 0.2.

Robotic hand posture and compliant grasping control using operational space and integral sliding mode control

SUMMARYThis paper establishes a novel approach of robotic hand posture and grasping control. For this purpose, the control uses the operational space approach. This permits the consideration of the shape of the object to be grasped. Thus, the control is split into a task control and a particular optimizing posture control. The task controller employs Cylindrical and Spherical coordinate systems due to their simplicity and geometric suitability. This is achieved by using an integral sliding mode controller (ISMC) as task controller. The ISMC allows us to introduce a model reference approach where a virtual mass-spring-damper system can be used to design a compliant trajectory tracking controller. The optimizing posture controller together with the task controller creates a simple approach to obtain pre-grasping/object approach hand postures. The experimental results show that target trajectories can be easily followed by the task control despite the presence of friction and stiction. When the object is grasped, the compliant control will automatically adjust to a specific compliance level due to an augmented compliance parameter adjustment algorithm. Once a specific compliance model has been achieved, the fixed compliance controller can be tested for a specific object grasp scenario. The experimental results prove that the Bristol Elumotion robot hand (BERUL) can automatically and successfully attain different compliance levels for a particular object via the ISMC.

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio (CR) system. Current research focus on the genetic algorithm (GA) to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.

A poly-hybrid PSO optimization method with intelligent parameter adjustment

This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.

変動安定微係数を有する航空機のためのディジタル適応飛行制御系の設計

This paper presents a new control scheme for digital adaptive flight control systems, that satisfies C* criterion for aircraft which have continuously varying stability derivatives. A number of design methods for digital adaptive flight control systems have been reported based on the assumption that aircraft are linear time-invariant systems. But the equations of real aircraft motions have wide time-varying parameters in their flight envelopes. Only recently, new adaptive control system theories for the linear time-varying systems have been developed. By modifying the new theories, this paper presents a new parameter adjustment algorithm that can estimate the coefficents of powers of dynamic pressure, which are used to synthesize the input to aircraft instead of estimating the time-varying parameters. Computer simulations were performed on a jet fighter as a time-varying system by continuously changing flight conditions in the flight envelope. The results showed remarkable adaptability.