Optimal Control Parameters Research Articles

Analysis of control algorithms for water quality testing robots

The research background of the water quality detection robot lies in effectively monitoring and assessing the quality of water bodies to address the increasingly severe issue of water pollution. This technology, employing advanced sensors and machine learning algorithms, facilitates automated acquisition of water quality data, enabling real-time analysis and reporting of various indicators in the water bodies. Consequently, it provides crucial evidence for safeguarding the environment and human health. This article investigates the problem of posture control in water quality detection robots operating in complex underwater environments. It compares three control algorithms: the traditional PID fuzzy control algorithm, and a BP neural network-based PID algorithm. Through experimental simulation, these algorithms are evaluated based on their differences in system dynamic performance. The results indicate that the utilization of the BP neural network PID algorithm significantly enhances both stability and control precision of the robot when dealing with non-linear environments characterized by uncertainties. This algorithm combines traditional PID control with neural network technology, allowing for adaptive optimization of PID controller parameters through training the neural network. As a result, it overcomes limitations associated with traditional PID and fuzzy control algorithms while improving overall performance.

Pedicle drilling force control of a robotic surgical system via spine-soft tissue coupling model and parameters optimization

Bone drilling is a crucial operation in spinal fusion surgery that requires precise control of the applied force to ensure surgical safety. This manuscript aims to enhance the force servo performance of the orthopedic robot during automatic bone drilling operations. Firstly, an analytical model is introduced to describe the spinal mobility of the spine-soft tissue coupling structure. Then, the model is calibrated using force data obtained from stress relaxation tests. Next, optimal force controller parameters are determined through drilling force control simulations based on the identified model. The dynamic performance and robustness of the closed-loop control system are analyzed to ensure safe drilling procedures. Finally, bone drilling experiments are conducted in a force control mode to verify the effectiveness of the proposed method. The step drilling force response's steady-state error is less than 0.15 N, the relative control error is less than 3 %, and there is no noticeable force overshoot. The amplitude of the sinusoidal force response decays to −3 dB when the target force frequency is up to 3.49 rad/s, indicating a wide control bandwidth. These results demonstrate that the proposed method can rapidly and safely provide an adequate force servo to carry out automatic bone drilling.

Research on Optimal Fast Terminal Sliding Mode Control of Horizontal Vibration of High-speed Elevator Car System

An optimal fast terminal sliding mode control strategy is proposed to suppress effectively the horizontal vibration of the high-speed elevator car system is caused by uncertainties such as rail unevenness, elevator load variation, and component friction and wear. Firstly, considering the elevator's composition structure and vibration characteristics, a 4-degree-of-freedom car system horizontal vibration active control model with a symmetric distribution of the control center is established. Secondly, considering the nonlinear factors of the rolling guide shoe and the external excitation, an optimal fast terminal sliding mode controller (PFTSMC) based on the sliding mode variable structure control is designed to eliminate the horizontal vibration of the car, define the nonsingular terminal sliding mode surface, and introduce the fast terminal convergence law based on the fast terminal attractor to ensure the accessibility of the sliding mode motion and reduce the jitter vibration. In addition, optimization of controller parameters using random weight particle swarm algorithm (RNW-PSO) to improve the controller's vibration suppression performance and robustness. Finally, the proposed controller can achieve more than 51.2% attenuation of horizontal vibration acceleration and displacement, showing that PFTSMC can effectively reduce the horizontal vibration of high-speed elevator car systems and improve ride comfort.

Coordination control between vibration suppression and energy regeneration for vehicles driven by in-wheel motors under regenerative braking condition

In this paper, focusing on regenerative braking condition, a coordination control method is proposed to achieve the vibration suppression and braking energy recovery of vehicles driven by In-Wheel Switch Reluctance Motor (IWSRM). Firstly, a vehicle vertical dynamic model, including SRM drive system, vehicle braking system, and quarter SRM-suspension system, is established based on the analysis of the generating and vibration characteristics of IWSRM. Then, after analyzing the effects of eccentricity and electromagnetic excitation, the vertical unbalanced radial force is used as control signal in design of the control method. Moreover, for ensuring the recovered energy and reduce vertical vibration, the optimal key control parameters varying with the vehicle speed and braking strength are obtained by genetic algorithm. To achieve verification of the control method, the several vehicle performance indexes are selected as observation objects, which are analyzed and compared to other control methods, and the results indicate the control method can effectively weaken the vertical acceleration and improve the vehicle ride comfort without significantly decreasing regenerative braking energy. Finally, a hardware in the loop experiment is conducted, and the effectiveness and real-time ability of the proposed method can be verified.

Designing Parameters to Reshape the Inverter Output Impedance Based on the D-Split Method under Weak Grid Conditions

Novel energy generation technologies, such as high-permeability wind power and photovoltaic systems, exhibit inherent intermittency, randomness, and volatility. During periods of energy production from these sources, the impedance of the electrical grid frequently experiences significant fluctuations. Consequently, the grid manifests characteristics akin to a weak grid, highlighting the challenges associated with integrating renewable energy sources with variable output profiles. These fluctuations lead to a shift in resonance spikes of the LCL-type filter towards the lower frequency band, consequently impacting the stability of inverter grid connections. To mitigate this issue, the present study introduces a novel approach integrating the D-split method and a proportional–differential feedforward strategy. This combination aims to reshape the inverter’s output impedance, ensuring its consistent stability even amidst significant fluctuations in grid impedance. In this research, the optimal controller parameters for systems operating under robust grid conditions are initially determined. This selection process involves the application of the D-split method, supported by graphical visualization. Additionally, the amplitude and phase angle margin tester is employed while considering the system bandwidth as a limiting constraint. Moreover, accounting for the influence of grid impedance, the D-split method is reapplied to ascertain the most suitable values for the proportional–differential feedforward parameters. This adjustment aims to enhance the adaptability of the grid-connected inverter specifically in scenarios characterized by a weak grid. The hardware-in-the-loop (HIL) simulation results demonstrate that despite the presence of harmonics within the grid and a short circuit impedance ratio (SCR) of 3, the inverter maintains grid stability and effectively suppresses harmonics. Moreover, the total harmonic distortion factor (THD) of the grid-connected current remains below 5%.

Integrated optimization of the in-wheel motor driving system considering the ride comfort of the electric vehicle under multiple operating conditions

In this paper, a multi-objective optimization scheme integrating structural parameters with control parameters is proposed to address the adverse effects of an unbalanced magnetic pull of the wheel motor on vehicle dynamics. Firstly, the unbalanced magnetic pull under the air gap eccentricity of a switched reluctance motor is analyzed using both Maxwell’s stress method and the principle of virtual displacement. Then, a current chopper control strategy for the in-wheel motor is developed, and the influence of relevant control parameters on the output characteristics of the motor driving system under different driving conditions is discussed. Finally, a multi-objective optimization model of a motor under multiple operating conditions is developed with the aid of multidisciplinary optimization software. Integration of structural and control parameters for optimization. The results show that the motor design parameters determined by the integrated optimization scheme can improve the vehicle’s response characteristics under a wide range of operating conditions. In particular, the negative effect caused by an unbalanced magnetic pull during the operation of the vehicle is greatly suppressed.

A deterministic robust control with parameter optimization for uncertain two-wheel driven mobile robot

The uncertainty in mobile robot greatly affects control accuracy. This makes it difficult to apply to more rigorous high-precision engineering fields. Therefore, the fuzzy set theory is introduced to describe the uncertainty. Based on that, the fuzzy mobile robot system is established. The virtual speed controller using backstepping method is designed. Then, a robust control method is proposed to guarantee the uniform boundedness and uniform ultimate boundedness of the controlled system. Furthermore, the balance optimization problem of the performance and cost of the controlled system is explored. By minimizing the performance index containing fuzzy numbers, the optimal control parameter is obtained. Compared with the linear quadratic regulator algorithm, which is the representative optimal robust controller, the proposed control method and optimization strategy based on fuzzy set theory are verified to be effective. The control accuracy is further improved.

Numerical Study on the Automatic Ballast Control of a Floating Dock

Abstract The ballast control of a floating dock mainly relies on manual operations, which can be time-consuming and requires skilled workers. This study proposes an automatic ballast control system for floating docks, which improves operational efficiency and safety during the vessel docking process. A numerical model is developed to simulate the dynamic process of the floating dock's operations, which includes a six degrees-of-freedom (6DOF) model, a hydrostatic force model, a hydrodynamic force model, and a hydraulic model. The hydrostatic force model is developed using the Archimedes law and a strip theory along the longitudinal direction. The hydrodynamic model is made based on the effects of added mass and dynamic damping. The hydraulic model is proposed to deal with the hydraulic calculation of the ballast water system. The present automatic ballast control is designed based on a modified proportional controller (P-controller) to control the valve opening angles when the pitch or roll angles are larger than the corresponding threshold values. Without using controllers, the roll angles of the dock can reach 8.9 deg and 13 deg during the ballasting and de-ballasting operations, respectively. The present modified P-controller with optimized control parameters can stabilize the dock during the de-ballasting operation and keep the maximum pitch and roll angles no larger than 0.016 deg and 0.0783 deg, respectively. During the ballasting operation with the same control parameters, the roll and pitch are below 0.0604 deg and 0.0145 deg, respectively. The present automatic control will be further implemented in the vessel docking cases and can significantly improve the stability of the dock.

Integrated Controller Design and Application for CNC Machine Tool Servo Systems Based on Model Reference Adaptive Control and Adaptive Sliding Mode Control.

In order to reduce the effect of nonlinear friction and time-varying factors on the servo system of a computer numerical control (CNC) machine tool and improve its motion control accuracy, this paper uses an adaptive sliding mode control (ASMC) method based on model reference adaptive control (MRAC). The method adopts ASMC in the control outer loop and obtains the optimal control parameters by making the sliding mode control (SMC) law continuous and adaptively estimating the control parameters. At the same time, MRAC is used in the control inner loop to enhance the "invariance" of the controlled object so that the switching gain of SMC can satisfy the disturbance matching condition even under lesser conditions. Simulation and experimental results show that compared with the traditional SMC, the ASMC based on MRAC proposed in this paper effectively reduces the influence of nonlinear friction on the system performance, and the reduction in following error reaches 71.2%, which significantly improves the motion control accuracy of the control system. The spectral analysis of the following errors shows that the maximum magnitude reduction rate of the high-frequency chattering is 89.02%, which significantly reduces the effect of the high-frequency chattering and effectively improves the stability performance of the control system.

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Wavelet neural networks have been widely applied to dynamical system identification fields. The most difficult issue lies in selecting the optimal control parameters (the wavelet base type and corresponding resolution level) of the network structure. This paper utilizes the advantages of Legendre multiwavelet (LW) bases to construct a Legendre multiwavelet neural network (LWNN), whose simple structure consists of an input layer, hidden layer, and output layer. It is noted that the activation functions in the hidden layer are adopted as LW bases. This selection if based on the its rich properties of LW bases, such as piecewise polynomials, orthogonality, various regularities, and more. These properties contribute to making LWNNs more effective in approximating the complex characteristics exhibited by uncertainties, step, nonlinear, and ramp in the dynamical systems compared to traditional wavelet neural networks. Then, the number of selection LW bases and the corresponding resolution level are effectively optimized by the simple Genetic Algorithm, and the improved gradient descent algorithm is implemented to learn the weight coefficients of LWNN. Finally, four nonlinear dynamical system identification problems are applied to validate the efficiency and feasibility of the proposed LWNN-GA method. The experiment results indicate that the LWNN-GA method achieves better identification accuracy with a simpler network structure than other existing methods.

Improvement of ride quality for a wheel loader with semi-active cab isolation system via fuzzy self tuning of PID controller

Improving driver ride comfort of a wheel loader is important to avoid potential health hazards from the whole-body vibration. A fuzzy self-tuning of PID controller is designed to control the damping coefficient of Semi-active cab isolation system (SCIS) for a wheel loader. A dynamic model of a wheel loader is establish under survey conditions for analysis and evaluation. The r.m.s acceleration responses of the vertical driver's seat and pitch angle of vehicle body according to ISO2631-1 (1997-E) are chosen as objective functions. The effectiveness of the proposed control strategy is analyzed through simulations involving excitations for a random road profile in time domain. The proposed SCIS is simulated and analyzed by Matlab/Simulink software in comparison with a passive cab isolation system (PCIS) under survey conditions. The achieved results indicate that ride comfort effectiveness of a wheel loader with SCIS is better than PCIS under survey conditions. In addition, the study results are the initial basis for optimizing controller parameters.

Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer.

Optimal Design of Robotic Character Kinematics

The kinematic motion of a robotic character is defined by its mechanical joints and actuators that restrict the relative motion of its rigid components. Designing robots that perform a given target motion as closely as possible with a fixed number of actuated degrees of freedom is challenging, especially for robots that form kinematic loops. In this paper, we propose a technique that simultaneously solves for optimal design and control parameters for a robotic character whose design is parameterized with configurable joints. At the technical core of our technique is an efficient solution strategy that uses dynamic programming to solve for optimal state, control, and design parameters, together with a strategy to remove redundant constraints that commonly exist in general robot assemblies with kinematic loops. We demonstrate the efficacy of our approach by either editing the design of an existing robotic character, or by optimizing the design of a new character to perform a desired motion.

Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation

Fermentation plays a pivotal role in the industrialization of bioproducts, yet there is a substantial lag in the fermentation process regulation. Here, an artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative lysine fermentation control. Firstly, the strategy of coupling GA with ANN was established. Secondly, specific lysine formation rate (qp), specific substrate consumption rate (qs), and specific cell growth rate (μ) were predicted and optimized by ANN-GA. The optimal ANN model adopts a three-layer feed-forward back-propagation structure (4:10:1). The optimal fermentation control parameters are obtained through GA. Finally, when the carbon to nitrogen ratio, residual sugar concentration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g·L−1, [1.0, 2.0] g·L−1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 ± 5.10 g·L−1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts.

Fractional-Order Extremum Seeking Method for Maximum Torque per Ampere Control of Permanent Magnet Synchronous Motor

Maximum torque per ampere (MTPA) control of internal permanent magnet synchronous motors (IPMSM) has become integral to high-efficiency motor drives. To minimize the influence of the traditional model-based analytical solution method on the MTPA control strategy due to the parameter variations during the motor operation, an online search MTPA method without model-based fractional-order extremum seeking control (FO-ESC) is proposed. Compared with the traditional integer-order ESC method, the Oustaloup approximation-based fractional-order calculus provides additional factors and possibilities for optimizing controller parameters to improve control performance. At the same time, the proposed approach does not require machine parameters and is thus not influenced by machine and drive nonlinearities. Simulation results show that the proposed method can ensure robust MTPA control under different loading conditions in real-time and improve the system’s dynamic response speed and steady-state accuracy.

Application of cell mapping to control optimization for an antenna servo system on a disturbed carrier

Abstract. The cell-mapping method, due to its global optimality, has been applied to solve multi-objective optimization problems (MOPs) and optimal control problems. However, the curse of dimensionality limits its application in high-dimensional systems. In this paper, the multi-parameter sensitivity analysis is investigated to reduce the parameter space dimension, which broadens the application of cell mapping to MOPs in high-dimensional parameter space. A post-processing algorithm for MOPs is introduced to help choose proper control parameters from the Pareto set. The proposed scheme is applied successfully in the control parameter optimization of an adaptive nonsingular terminal sliding-mode control for an antenna servo system on a disturbed carrier. Moreover, as the existing global optimal tracking control with an adjoining cell-mapping method may generate tracking-phase differences, an optimal-sliding-mode combined-control strategy is proposed. By using the combined-control strategy, the azimuth and pitch angles of the antenna system are controlled to catch up to a target trajectory with the minimum cost function and to keep high-precision tracking after that.

Control parameter optimization of underwater gliders for turbulence observation

Turbulent mixing plays an important role in shaping the circulation and distribution of heat and carbon within the climate system. Underwater gliders are ideal turbulence observation platforms, but appropriate control parameters are required for them to achieve high-precision and large-scale continuous turbulence measurement, for which speed and endurance are two important indicators. Taking these indicators as the optimization objectives, this study establishes an optimization model for the control parameters of underwater gliders, which integrates two gliding strategy models, a dynamic model, and an energy consumption model. The multi-objective optimization of both conventional gliding strategy and our pitch adjustment strategy is performed with non-dominated sorting genetic algorithm-II to obtain the optimal control parameters. After the optimization, both gliding strategies can achieve an ideal gliding speed for turbulence observation, with the gliding range increased by more than 6% in the diving phase and more than 10% in the climbing phase. The proposed optimization model is verified to be effective with a sea trial performed by Petrel-L underwater glider. After proper modifications, the proposed method is expected to be further applied to other types of gliders.

Boundary proportional control of thermoacoustic instability in a Rijke tube

Thermoacoustic instability has always been a critical concern of low pollution combustion chambers and combustion components of ground gas turbines. It is also a severe challenge for dynamic system design. In order to reveal the thermoacoustic instability, a typical Rijke tube based on partial differential equations (PDEs) cascaded with the Heckl heat release is studied. A boundary proportional feedback control to stabilize the thermoacoustic instability in the Rijke tube is proposed. First of all, one-dimensional gas dynamic equations coupled with the Heckl heat release are presented to describe the thermoacoustic unstable dynamic system in the Rijke tube. Then by using the small deviation linearization and characteristic coordinate transformation, the thermoacoustic coupling inside the domain is transferred into a group of 4 × 4 PDEs with the boundary coupling. Next, a boundary proportional feedback control, characterized by its practical implementation feasibility, is proposed to ensure the exponential stabilization of the thermoacoustic unstable system. A solvability condition is also provided to theoretically design the boundary proportional controller. Furthermore, the optimal control parameters are given by solving the nonlinear programming problem to achieve the fastest convergence rate of the system. Finally, the effectiveness of the boundary proportional feedback control is verified by simulations, and its robustness to the uncertain time delay and the nonlinearity of the heat release is also discussed.

Joint energy and spectral optimization in Heterogeneous Vehicular Network

With the latest developments in both the automotive and communications industries, especially concerning the emerging 5G networks, IoV, and the adoption of Vehicle-to-Everything (V2X) connectivity, there has been a shift towards the establishment of Heterogeneous Vehicular Networks (HetVNets) environments. The rapid growth of data traffic and the drastic expansion of heterogeneous network infrastructure have resulted in a significant increase in energy consumption within wireless communication systems. Balancing energy efficiency and spectral efficiency has become a major challenge in Heterogeneous Vehicular networks, particularly concerning energy optimization, making the design of network systems considerably more challenging. Therefore, this paper attempts to optimize the energy utilized for each packet transmission, considering its stochastic nature and the optimized control parameters of two meta-heuristic algorithms-Particle Swarm Optimization and Artificial Bee Colony Optimization. The optimization process is executed using the Particle Bee Colony Swarm algorithm. Subsequently, a comparison is made with other proposed algorithms, namely LDOD, FO, RO, and MATO, in terms of energy efficiency and spectral efficiency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating a 30.32% increase in spectral efficiency and 73.25% increase in energy efficiency.

On the Rod Heating Problem by Sources on the Class of Zonal Controls Using the Current and Past State at Measurement Points

We study an optimal feedback control problem for the rod heating process by means of lumped sources. The control actions are the powers of the sources, the values of which are defined on the class of zonal controls. The values of the parameters of zonal control actions are determined by subsets of the state space, to which belong the values of the process state at the measurement points at the current and past time moments. The posed problem is reduced to a parametric optimal control problem on determining a finite-dimensional vector of values of the parameters of zonal control actions. We have obtained optimality conditions for the values of the parameters of zonal control actions. These conditions contain formulas for the gradient of the objective functional with respect to the optimizable parameters. They make it possible to solve the reduced problem numerically with the application of efficient first-order optimization methods.