A synchronization position control method based on dynamic particle swarm optimization algorithm in electro-hydraulic servo system

This paper proposes a modified optimal PIDD2 controller for flexible-link manipulator. The single flexible link is modeled mathematically in which the flexible link and base rotation are modeled as stiff systems using Lagrange’s method. The system obtained as a result will have one degree of freedom. In the proposed work, the comparison of two types of controller, i.e., PID and PIDD2, is done for controlling the position and trajectory of the single-link manipulator. The main objective is to control the trajectory with minimum tip oscillation. The tuning of the controllers is done using the Ziegler–Nichols (Z-N) method and Dynamic Particle Swarm Optimization (DPSO) algorithm. The dynamic particle swarm optimization algorithm is an improved version of the particle swarm optimization algorithm which identifies and eliminates the dilemma of stagnation and local optima. The findings show that the PIDD2 controller with dynamically tuned parameters is better in controlling the position and trajectory of the single-link manipulator. All the simulations were performed on MATLAB–SIMULINK.

Risk prediction about investor portfolio holdings can provide powerful test of asset pricing theories. In this paper, we present dynamic Particle Swarm Optimization (PSO) algorithm to Markowitz portfolio selection problem, and improved the algorithm in pseudo code as well as implement in computer program. Furthermore in order to prevent blindness in operation and selection of investment, we tried to make risk least and seek revenue most in investment and so do in the program. As used in practice, it showed great application value.

Most current researches working on improving stiffness focus on the application of control theories. But controller in closed-loop hydraulic control system takes effect only after the controlled position is deviated, so the control action is lagged. Thus dynamic performance against force disturbance and dynamic load stiffness can’t be improved evidently by advanced control algorithms. In this paper, the elementary principle of maintaining piston position unchanged under sudden external force load change by charging additional oil is analyzed. On this basis, the conception of raising dynamic stiffness of electro hydraulic position servo system by flow feedforward compensation is put forward. And a scheme using double servo valves to realize flow feedforward compensation is presented, in which another fast response servo valve is added to the regular electro hydraulic servo system and specially utilized to compensate the compressed oil volume caused by load impact in time. The two valves are arranged in parallel to control the cylinder jointly. Furthermore, the model of flow compensation is derived, by which the product of the amplitude and width of the valve’s pulse command signal can be calculated. And determination rules of the amplitude and width of pulse signal are concluded by analysis and simulations. Using the proposed scheme, simulations and experiments at different positions with different force changes are conducted. The simulation and experimental results show that the system dynamic performance against load force impact is largely improved with decreased maximal dynamic position deviation and shortened settling time. That is, system dynamic load stiffness is evidently raised. This paper proposes a new method which can effectively improve the dynamic stiffness of electro-hydraulic servo systems.

Biometric models are typically designed a priori using limited number of samples acquired from complex environments that change in time during operations. Therefore, these models are often poor representatives of the biometric trait to be recognized. To circumvent this problem, ensemble of classifiers can be used to integrate solutions obtained from multiple diverse classifiers. In this paper, two dynamic particle swarm optimization (DPSO) algorithms are compared for the evolution of classifier ensembles during supervised incremental learning of newly-acquired data samples in video-based face recognition. Using the properties of these population-based optimization algorithms, an incremental DPSO learning strategy for adaptive classification systems (ACSs) is employed to evolve a pool of fuzzy ARTMAP classifiers while an heterogeneous ensemble is selected through a greedy search process that seeks to maximize both performance and diversity. The performance of dynamic niching PSO (DNPSO) and speciation PSO (SPSO) algorithms is assessed in terms of classification rate, resource requirements and diversity for different incremental learning scenarios of new data blocks extracted from real-world video streams. Simulation results indicate that both DPSO algorithms can efficiently create accurate ensembles while reducing computational complexity. In addition, directly selecting representative subswarm particles to form diversified classifier ensembles significantly reduces the computational complexity.

Electro-hydraulic servo systems have been frequently used in the fin position servo system of a missile because of their high power and good positioning capability. The objective of this paper is to realize a self-tuning fuzzy controller for the position control of an electro-hydraulic fin servo system. In this paper, we propose a new self-tuning fuzzy controller, which can improve the overall performance of the electro-hydraulic fin position servo system. The uniqueness of this self-tuning fuzzy controller is that the performance evaluation of the fin position servo system is carried out in a phase plane mapped to a decision rule table. The effectiveness of this control scheme is verified by comparison with a fixed fuzzy control with initial rules through a series of simulation studies.

Electro-hydraulic position servo system of valve-controlled symmetrical cylinder (EPSSVCS) is a very important and widely used electro-hydraulic servo system, the performance of which is often affected by friction nonlinearity and external random disturbances together with unmodeled dynamic factors such as parameter uncertainty in practical work. To improve the tracking performance of the system, a backstepping sliding mode control algorithm (BSMC) based on an extended state observer was presented in this paper. Firstly, a nonlinear mathematical model of the EPSSVCS was established which takes into account the composite disturbance induced by the systematic friction nonlinearity, external disturbances and unmodeled dynamic factors. Then, an extended state observer (ESO) was designed which can effectively estimate the velocity, acceleration, and composite disturbance of the valve-controlled cylinder online. Furthermore, a kind of BSMC was presented based on the online ESO estimates and the displacement feedback signals. The control law was given and the system stability was proved. The analysis results showed that the output signal of the system can effectively track the input signal under the influence of typical external disturbances and unmodeled dynamic factors, which illustrated the effectiveness of the established nonlinear mathematical model and the stability of the designed control system. Moreover, by taking the parameters of the electro-hydraulic servo system of a certain type of CNC machine tool as a real example, the proposed algorithm (BSMC) was compared with the conventional PID control algorithm, the backstepping sliding mode control algorithm (BSC), and the adaptive robust control algorithm (ARC), and the results verified the superiority of the BSMC. This work may provide a useful reference for the research in the field of related control systems.

Many problems in the real-world have more than one objective, with at least two objectives in conflict with one another. In addition, at least one objective changes over time. These kinds of problems are called dynamic multi-objective optimisation problems (DMOOPs). Studies have shown that both the quantum particle swarm optimisation (QPSO) and charged particle swarm optimisation (CPSO) algorithms perform well in dynamic environments, since they maintain swarm diversity. Therefore, this paper investigates the effect of using either QPSOs or CPSOs in the sub-swarms of the dynamic vector-evaluated particle swarm optimisation (DVEPSO) algorithm. These DVEPSO variations are then compared against the default DVEPSO algorithm that uses gbest PSOs and DVEPSO using heterogeneous PSOs that contain both charged and quantum particles. Furthermore, all of the aforementioned DVEPSO configurations are compared against the dynamic multi-objective optimisation (DMOPSO) algorithm that was the winning algorithm of a comprehensive comparative study of dynamic multi-objective optimisation algorithms. The results indicate that charged and quantum particles improve the performance of DVEPSO, especially for DMOOPs with a deceptive POF and DMOOPs with a non-linear POS.

The effect of varying load conditions on dynamic and static properties of a flexible electrohydraulic position control servo system is investigated theoretically and experimentally in this paper. Three different control methods were used. P control was taken into the research as a reference. P + PID/x; was used because it is a good method of damping the oscillations of flexible servos. Model reference control (MRC) was included as a new possible method of getting good dynamic results. In the mathematic analysis the aim was to develop system models that are not too complicated but still describe well this kind of heavily loaded flexible servo system. The non-linearities included were the dead band and saturation of the valve and the non-linearity of the load flow.

A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction

An electro‐hydraulic servo position and pressure compound control method was investigated considering the working principle of a hydraulic stepping motor of a shield machine gripper shoe and its practical working characteristics in the supporting process. The control targets were to improve the support efficiency and reduce the disturbance of gripper shoes on the surrounding rock. In this method, a fuzzy switching controller was used to switch between electro‐hydraulic position control and electro‐hydraulic pressure control. Numerical and prototype simulation experiments were conducted on the control method. The theoretical analysis and experimental results showed that the control method could effectively convert the gripper shoes from an unsupported state to a supported state in a short amount of time, as well as realize surge‐free switching between position control and pressure control. Thus, disturbance of the gripper shoes on the surrounding rock could be reduced. The results of this study provide a theoretical basis for research on control strategies of hydraulic stepping propulsion of shield machines.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

The electro-hydraulic proportional position servo system is a kind of highly non-linear and time-varying system as well as parameter uncertainties. It is often difficult to achieve satisfactory control results with the traditional control methods. Considering that the adaptive neural network PID control method has the characteristics of online learning and adjustment PID control parameters adaptively, this paper puts forward to adopt BP-PID control method for the position servo control of electro-hydraulic proportional system. An online adaptive BP-PID controller is designed. The simulation model of the system is established based on Simulink platform. The step response and Sinusoidal tracking characteristics are researched for electro-hydraulic proportional position servo system by using traditional PID and BP-PID control algorithms through simulations and experiments. The results show that BP-PID has a strong ability of online learning and self-tuning PID parameters. Compared with the traditional PID control method, the BP-PID control has strong self-adaptability and robustness, and can significantly improve the step response speed and tracking control precision.

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

<p>Task scheduling is a key and challenging problem in cloud computing systems, requiring decisions regarding resource allocation to tasks to optimize a perfor mance criterion. This problem has required researchers and developers to over come significant challenges. Our goal in this study aims to minimize both the makespan and energy consumption in cloud computing systems by efficiently scheduling workflows. To achieve this, we first proposed a dynamic multi objective model, which wasthensimplified into a single-objective problem using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) and a dynamic particle swarm optimization algorithm (DPSO) to address the prob lem. To deal with the situation where the makespan is uncertain and not exact, we present a fuzzy model, treating each value as a fuzzy number and we utilize both possibility and necessity metrics. The results are contrasted with the Het erogeneous earliest finish time (HEFT) algorithm and Considerably lowered the total energy consumption, especially for DGA.</p>

A change severity degree-based dynamic multi-objective optimization algorithm with adaptive response strategy