Advanced adaptive feed control for CNC machining

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field.

The effects of velocity difference changes with memory on the dynamics characteristics and fuel economy of traffic flow

In energy harvesting wireless sensor networks (EH-WSNs), clustering techniques have been studied for prolonging their network lifetime. The adaptive cluster control scheme (ACCS) has been proposed for the EH-WSNs under geographical non-uniform energy harvesting conditions and targets homogeneous EH-WSNs configuring the nodes with the same battery capacity. In actual networks, the nodes generally equip various battery capacities, and therefore clustering schemes should be developed for the EH-WSNs with heterogeneous battery capacity. However, the conventional ACCS is hard to be applied to the heterogeneous EH-WSNs (EH-HetWSN s) in its current form due to no consideration of the different battery capacities at the nodes. This paper proposes an advanced adaptive cluster control scheme (AACCS), which has the beacon transmission power control modified from the conventional ACCS so as to achieve well-suited operation in the EH-HetWSNs. The evaluation results show that the proposed scheme has higher network performance, compared with the conventional one.

This paper presents an advanced Adaptive Sliding Mode Control (ASMC) strategy, specifically developed for a hydrogen production system based on a Proton Exchange Membrane electrolyzer (PEM electrolyzer). This work utilized a static model of the PEM electrolyzer, characterized by its V-I electrical characteristic, which was approximated by a linear equation. The ASMC was designed to estimate the coefficients of this equation, which are essential for designing an efficient controller. The primary objective of the proposed control strategy is to ensure the overall stability of the integrated system comprising both an interleaved buck converter (IBC) and PEM electrolyzer. The control framework aims to maintain the electrolyzer voltage at its reference value despite the unknown coefficients while ensuring equal current distribution among the three parallel legs of the IBC. The effectiveness of the proposed approach was demonstrated through numerical simulations in MATLAB-SIMULINK and was validated by the experimental results. The results showed that the proposed ASMC achieved a voltage tracking error of less than 2% and a current distribution imbalance of only 1.5%. Furthermore, the controller exhibited strong robustness to parameter variations, effectively handling fluctuations in the electrolyzer’s ohmic resistance (Rohm) (from ±28.75% to ±40.35%) and in the reversible voltage (Erev) (from ±28.67% to ±40.19%), highlighting its precision and reliability in real-world applications.

The rapid evolution of modern missile threats, particularly in the hypersonic regime, has driven the need for highly integrated defensive missile systems capable of operating autonomously and reliably under extreme aerodynamic, thermal, and structural conditions. This review presents a comprehensive examination of adaptive and additive structures technology for defensive missiles, emphasizing the interdependent roles of aerodynamics, structural optimization, guidance and control, materials, and additive manufacturing. Missile classifications based on trajectory, function, and geometry are first reviewed to establish the operational context, followed by an in-depth discussion of aerodynamic and structural optimization strategies, including noncircular forebody designs, folding arc-wing configurations, aeroelastic effects, and nose cone trade-offs in hypersonic flow regimes. The review further analyzes advanced adaptive guidance and control architectures, highlighting the limitations of conventional reactive laws and the growing importance of nonlinear adaptive, vision-based, and AI-assisted guidance systems for intercepting highly maneuverable targets. Material considerations for hypersonic environments are discussed, with particular focus on carbon–carbon composites, ultra-high temperature ceramics, and honeycomb sandwich structures that balance thermal resistance, structural integrity, and weight efficiency. Finally, the strategic role of additive manufacturing is evaluated, demonstrating its impact on rapid prototyping, modular hypersonic testing infrastructure, and the fabrication of mission-critical components with reduced cost and lead time. Overall, this review underscores that future defensive missile development requires a tightly coupled, multidisciplinary systems approach, where aerodynamic efficiency, structural resilience, intelligent guidance, and advanced manufacturing technologies are co-optimized to meet the demands of next-generation hypersonic defense systems.

This paper presents a VLSI coprocessor: fuzzy and digital signal processor (FDSP) in a single core and its software support application development system. The FDSP offers economic solution to implement adaptive fuzzy digital signal processing. The fuzzy acts as the adaptive element to DSP systems such as filters, noise cancellers, estimators, channel equalizers, etc. The FDSP core can be used as the internal coprocessor for 8-16 bit microcontrollers. This system allows one to build advanced and economic adaptive filters and control systems to support high resolution real-time applications in such systems as hard disk drive controllers, image filters and multimedia communications.

The authors present an adaptive velocity controller for a permanent magnet synchronous motor without using a shaft sensor. Two line-to-line voltages and two stator currents are sensed to produce the flux position. The design part of this work is concerned with the formulation of control algorithms for a current-regulated pulse width modulated inverter and a vector control strategy for the speed loop. Under the vector control framework, self-tuning, model following, and model reference adaptive control are applied to the design for the speed loop controllers. The implementation part of the work integrates the control of current loop and speed loop using microprocessor-based controllers. Experimental results validate the theoretical development. A novel approach for designing an advanced adaptive controller for a sensorless AC drive is provided. >

This paper investigates the performance of two different adaptive control schemes for controlling the angular position of an electronic throttle (ET) plate. The adaptive backstepping controller and adaptive sliding mode backstepping controller are the controllers under consideration. The control design based on these adaptive controllers is firstly addressed and the stability analysis of each controller has been presented and the convergence of both position and estimation errors for both controllers have been proved. A comparison study of the performance of both controllers has been conducted in terms of system transient characteristics and the behavior of their associated adaptive gain. The simulation has been implemented within the environment of the MATLAB package.

In order to overcome the problems of low temperature monitoring accuracy and high control error rate existing in traditional cutting temperature control methods, a new adaptive temperature control method for CNC machine tools is proposed in this paper. In order to improve the adaptive control accuracy of cutting temperature of CNC machine tools, a fuzzy monitor of cutting temperature is designed to realize the real-time monitoring of cutting temperature of CNC machine tools. Based on the real-time monitoring results of cutting temperature of CNC machine tools, Gauss heat source model is used to describe the heat flow distribution of cutting heat source. In two-dimensional space, through the heat source density and boundary conditions, the cutting temperature adaptive control function is constructed to complete the cutting temperature adaptive function of CNC machine tool. The experimental results show that, compared with the traditional control method, this method can accurately monitor the cutting temperature of CNC machine tools, and can accurately carry out the adaptive control of cutting temperature, and the maximum control error rate is less than 3%.

The demand of the industry for high performance electric motors has significantly increased nowadays. This has boosted the usage of permanent magnet brushless synchronous motors (BLSM) in many applications where the accuracy and performance requirements are high. Further improvement of the BLSM drive systems performance can be achieved by providing them with adaptive control capabilities. The relative complexity of adaptive control schemes and algorithms and the computational load that they impose have prevented until recently their practical implementation into the industrial servo systems. In this investigation, a neuro-adaptive control scheme where the rule for parameter adaptation is designed by taking into account the variable structure control (VSC) concepts and Lyapunov stability, is proposed and embedded into an inexpensive, available on the market, position control system for brushless synchronous servomotors. The experimental tests have been carried on using compact and flexible, based on open hardware and software concept, dual-axis motion controllers PMC201/PMC202 manufactured by the PicoMotion Inc. The applied software has been written using the Motion Control Framework software platform, provided together with the above controllers. The results obtained with the proposed neuro-adaptive control scheme have been compared to those obtained using the originally built into the system PI controller. The experiments have shown that the implemented advanced adaptive control approach is practically viable and can be embedded into the industrial motion control systems which will lead to their improved performance.

The Non-Linear Threshold Accepting (NLTA) algorithm has been successfully applied in electrical and electronic systems, particularly in optimizing power distribution and voltage regulation. However, its application in mechatronic systems remains largely unexplored. Given that most Unmanned Ground Vehicles (UGVs) and robotic systems used in logistics and industrial environments integrate electrical, electronic, and mechanical subsystems, an advanced adaptive control strategy is essential to ensure optimal performance in dynamic environment be it in engineering/manufacturing or last-mile delivery environments. Traditional Proportional-Integral-Derivative (PID) controllers, while widely adopted, can still be improve for real-time adaptability, leading to improved efficiency in trajectory control, response time, and energy consumption. These possibilities necessitate needs for a more robust control framework with better and improved capabilities of dynamically adjusting to operational uncertainties. In this research a comparative performance evaluation was conducted using Mathematical models and MATLAB/Simulink simulations, benchmarking the NLTA algorithm against conventional PID controllers and other heuristic optimization techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE). Unlike some of the traditional PID controllers that require manual tuning and often fail to adapt to non-linear system variations, NLTA employs an adaptive threshold mechanism to iteratively optimize control gains, ensuring improved operational stability, energy efficiency, and trajectory accuracy. The NLTA algorithm’s ability to better self-adjust in real time provides a significant advantage over existing control methods, making it a better and more viable alternative for enhancing the performance and reliability of mechatronics or other systems in need of optimization. A comparative analysis was conducted using MATLAB/Simulink simulations to benchmark NLTA against conventional PID controllers and other heuristic-based optimization methods, including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE). The results demonstrate that NLTA outperforms traditional control strategies by achieving faster response times, reduced settling time, enhanced robustness against environmental disturbances, and improved overall system efficiency. While the NLTA model also holds promise for warehouse layout optimization where dynamic reconfiguration could enhance operational efficiency this research focuses solely on evaluating its effectiveness as a control strategy for mechatronic systems compared to existing PID-based approaches. The findings reinforce the potential of NLTA as an advanced control framework for system and operational optimization also, bridging the gap between electrical, electronic, and mechanical control integration. Future work will explore real-world deployment, AI-driven predictive modeling for enhanced adaptability, and the extension of NLTA’s capabilities to logistics facility layout optimization. By validating NLTA’s effectiveness against traditional PID controllers, this research is aimed to contribute to the ongoing evolution of intelligent control mechanisms in logistics engineering especially autonomous last-mile delivery systems.

CNC machine tools are essential in determining the quality of machined components and improving the manufacturing process. This article discusses geometric errors, structural rigidity, thermal effects, motion accuracy, 2D and 3D trajectories and motion precision. Modern solutions such as automatic spindle calibration, error compensation through advanced algorithms, adaptive control and the use of intelligent sensors are presented in this article. The need for a general approach, vibration tuning and dynamic error compensation is emphasised in the paper. The results show that artificial intelligence and advanced control technologies can significantly improve the accuracy and reliability of CNC machines, enabling the production of high quality parts. This is due to the stringent requirements of the industry. In addition, the analysis highlights that optimisation of positioning systems and real-time monitoring can significantly reduce machining inaccuracies. These solutions can improve the effectiveness of CNC machines, resulting in reduced production costs and increased process efficiency.

In glutamate fermentation, intermittent feeding is the most widely used glucose feed strategy. This feeding strategy causes severe fluctuations of glucose concentration and osmotic pressure in fermentation broth, which deteriorates the viability of the cell and reduces glutamate production in turn. In order to maintain glucose concentration at stable and constant levels, an on-line prediction and feedback control system based an empiric mass balance model was developed. However, the control system did not work properly and sometimes glucose concentration could even decline to 0 level (glucose exhaustion), as the model parameter varies in different runs. As a result, a novel model-based adaptive feedback control system incoporating with an artificial neural network (ANN) based pattern reconition unit for on-line diagnosizing the fault of glucose exhaustion was proposed and applied for glutamate fermentation. This adaptive control system could accurately detect glucose exhaustion when it occurs, and then immediately updates the control parameter based on some pre-defined rule. With the proposed control system, glucose was automatically fed, and its concentration could be maintained at desired levels constantly. As a result, glutamate concentration was 17 ~ 30% higher than that of the traditional fermentations using the intermittent glucose feed strategy.

The conventional PID pitch controller was usually used in direct-drive permanent magnet wind turbine in order to maintain the output power unchanged above the rated wind speed. However, due to the complex structure of wind turbine as well as high nonlinearity of wind speed, the effect of PID control method is often less than ideal. A new pitch control method combined with fuzzy adaptive PID and fuzzy feed forward controller is proposed in this paper. The fuzzy adaptive PID controller is able to ensure the unit having a better control result than PID controller at various wind speeds. The fuzzy feed forward controller has improved the responsiveness of pitch control system. With the simulation of a 1.5MW wind turbine, the responsiveness and stability of wind turbine can be effectively proved by the new pitch control method above the rated wind speed, the system’s overshoot is also reduced, which satisfies the demand of power control.