Predictive Force Control Research Articles

Explicit Model Predictive Cascade-Free Direct Force and Torque Control of Quadrotors

High-performance control of quadrotors' force and torques is the basis for achieving excellent trajectory tracking. However, the conventional method indirectly controls the force and torques by controlling four permanent magnet synchronous motors' speeds, resulting in poor dynamic performance and static deviations when pneumatic parameters mismatch. Dedicated to achieving high-dynamic and high-accuracy control of force and torques, an explicit model predictive (EMP) cascade-free direct force and torque control method is proposed. Firstly, defining force current and torque currents, whereby a new structure is proposed. Then, dynamics of the quadrotor and motor are fused to design the linear robust predictive models of force and torques. Accordingly, the EMP controller is constructed to replace the traditional cascaded speed solution module and four speed controllers uniformly. The EMP controller generates the optimal current commands directly from the force and torque commands, realizing direct control without the speed loop. The new structure makes it possible to solve the pneumatic parameter mismatch problem with an auxiliary extended state observer, obtaining offset-free control. The proposed method optimizes the dynamic performance and steady-state accuracy of force and torques and improves the system's robustness. Finally, the proposed method's feasibility is verified by experiments.

Safe Machine-Learning-Supported Model Predictive Force and Motion Control in Robotics

Many robotic tasks, such as human-robot interactions or the handling of fragile objects, require tight control and limitation of appearing forces and moments alongside sensible motion control to achieve safe yet high-performance operation. We propose a learning-supported model predictive force and motion control scheme that provides stochastic safety guarantees while adapting to changing situations. Gaussian processes (GPs) are used to learn the uncertain relations that map the robot’s states to the forces and moments. The model predictive controller uses these Gaussian process models to achieve precise motion and force control under stochastic constraint satisfaction. As the uncertainty only occurs in the static model parts — the output equations — a computationally efficient stochastic MPC formulation is used. analysis of recursive feasibility of the optimal control problem and convergence of the closed loop system for the static uncertainty case are given. Chance constraint formulation and back-offs are constructed based on the variance of the Gaussian process to guarantee safe operation. The approach is illustrated on a lightweight robot in simulations and experiments.

Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function

Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function

The effect of type 2 diabetes and diabetic peripheral neuropathy on predictive grip force control.

This study investigated the impact of type 2 diabetes and diabetic peripheral neuropathy on grip force control during object manipulation. The study included three age-matched groups: type 2 diabetes alone (n = 11), type 2 diabetes with neuropathy (n = 13), and healthy controls (n = 12). Grip force control variables derived from lifting and holding an experimental cup were the ratio between grip force and load forces during lifting (GFR), latency 1 and latency 2, which represented the time between the object's grip and its lift-off from the table, and the period between object's lift-off and the grip force peak, respectively; time lag, which denoted the time difference between the grip and load force peaks during the lifting phase, and finally static force, which was the grip force average during the holding phase. Grip force control variables were compared between groups using one-way ANOVA and Kruskal-Wallis test. Post-hoc analysis was used to compare differences between groups. GFR and latency 1 showed significant differences between groups; the type 2 diabetes with neuropathy group showed larger GFR than the type 2 diabetes alone and healthy control groups. The latency 1was longer for the group with neuropathy in comparison with the health control group. There were no significant differences between groups for latency 2, time lag, and static force. Our results showed impaired GFR and latency 1 in participants with type 2 diabetes with neuropathy while the time lag was preserved. People with type 2 diabetes alone might not have any deficits in grip force control. Higher grip forces might expose people with type 2 diabetes and diabetic peripheral neuropathy to the risk of fatigue and injuring their hands. Future studies should investigate strategies to help people with type 2 diabetes with neuropathy adjust grip forces during object manipulation.

Thrust Coordinated Assignment and Ripple Suppression of a Multiple-Modular Permanent Magnet Linear Synchronous Motor Based on Model Predictive Thrust Control

This paper presents a model predictive thrust force control (MPTFC) method for a multiple-modular permanent magnet synchronous linear motor (PMSLM). It focuses on the thrust assignment and thrust ripple of the motor drive system with a multiple-branch inverter. A discrete time model of the PMSLM is established, and the driving system structure and operation principle of the motor are studied. A multi-mode cost function is designed according to the requirements of the different load conditions, and the optimal voltage vector action time is determined. The operation mode is analyzed to determine the distribution factor, so as to reduce the thrust pulsation during operation and improve the performance of the drive system. The results indicate that the proposed MPTFC method is effective in different operating modes, and the drive system has high efficiency and safer performance compared to a conventional drive system.

Three-Vector Model Predictive Suspension Force Control for Bearingless Permanent Magnet Slice Motor

Aiming at the defects of signal detection and transmission delay leading to the reduction of control accuracy in the digital control of bearingless permanent magnet slice motor(BPMSM), a three-vector model predictive suspension force control(MPSFC) strategy is proposed. Firstly, the suspension force prediction model of the BPMSM is established and the dead-beat principle is used to calculate two active voltage vectors and a null vector duty cycle. Then, according to the principle of the minimum switching state in one control cycle, simplifying the candidate voltage vector combinations, which effectively reduces the calculation amount and inverter switching frequency. Finally, the simulation and experimental results show that the proposed three-vector MPSFC strategy of the BPMSM can not only effectively improve the control accuracy, but also implement the stable suspension of the rotor. In addition, it has good static and dynamic response characteristics.

Model predictive force control in milling based on an ensemble Kalman filter

Process force determines productivity, quality, and safety in milling. Current approaches of process design often focus on a priori optimization. In order to enable online optimization, the establishment of active force controllers is required. Due to fast-changing engagement conditions of the tool in conjunction with the slower machine dynamics, classic control is not suited. A promising approach is the application of model predictive control (MPC) for force control, which is proposed in this contribution. The model predictive force controller (MPFC) explicitly takes into account a model to predict the immediate future. It consists of a model of the machine tool and a separate model of the process. The process model describes the relation between feed velocity of the tool, force, and geometric properties of the tool, such as the radial deviation, and of the tool/workpiece engagement. The feedback loop of the controller is closed by an online identification of the process model to account changes in the material properties or of the tool wear state. For this identification an ensemble Kalman filter (EnKF) is applied. The MPFC solves an optimization problem on the future behavior in each sampling step to determine the optimal controller output enabling high dynamic control. The proposed control system is validated experimentally and compared with a conventionally designed process with constant feed. It can be shown that the manufacturing time is reduced by 50%. The system enables a paradigm shift in the design of milling processes operating the manufacturing process at its technological limit.

Model predictive direct suspension force control of a bearingless induction motor based on sliding mode observer

PurposeThe purpose of the control method proposed in this paper is to address the problem of the poor anti-interference of the suspension winding current in the traditional bearingless induction motor (BL-IM) direct suspension force control process.Design/methodology/approachA model predictive direct suspension force control of a BL-IM based on sliding mode observer is proposed in this paper. The model predictive control (MPC) is introduced to the traditional direct suspension force control to improve the anti-interference of the suspension current. A sliding mode flux linkage observer is designed and applied to the MPC system, which reduces the error of the parameter observation and improves the robustness of the system. The strategy is designed and implemented in the MATLAB/Simulink and the two-level AC speed regulation platform.FindingsThe simulation and experimental results show that the performance of the BL-IM under the control method proposed in this paper is better than that under the traditional direct suspension force control, and the suspension performance of the motor and the anti-interference of the control system are improved.Originality/valueThis study helps to improve the suspension performance of the motor and the anti-interference of the control system.

Model Predictive Thrust Force Control for Linear Motor Actuator used in Active Suspension

As the core mechanism of active suspension, the control strategy of linear motor actuator is the key factor to affect ride comfort. This paper presents a model predictive thrust force control (MPTFC) method with switching frequency term in the evaluation function for linear motor applied in the vehicle active suspension system. MPTFC selects seven voltage vectors that the inverter produces into the motor predictive model. The motor operating status parameters of the control cycle are obtained with the effect of different voltage vectors. Based on the evaluation function to select the optimal voltage vector, the optimal vector is used for motor in the next control period. The sky-hook controller of active suspension system and the model predictive thrust controller of linear motor are consisted of the control system of the active suspension. A two-degree-of-freedom quarter-car model with active suspension system was established. The MPTFC and the direct thrust force control (DTFC) for linear motor are experimentally tested by dSPACE. The whole active suspension performance is simulated. Results prove that the suspension using linear motor with MPTFC has faster dynamic response of speed, less thrust force and stator flux linkage ripple, which produces the desired electromagnetic force according to the vibration of the bodywork.

Visual Feedback of Object Motion Direction Influences the Timing of Grip Force Modulation During Object Manipulation.

During manipulation, object slipping is prevented by modulating the grip force (GF) in synchrony with motion-related inertial forces, i.e., load force (LF). However, due to conduction delays of the sensory system, GF must be modulated in advance based on predictions of LF changes. It has been proposed that such predictive force control relies on internal representations, i.e., internal models, of the relation between the dynamic of the environment and movement kinematics. Somatosensory and visual feedback plays a primary role in building these internal representations. For instance, it has been shown that manipulation-dependent somatosensory signals contribute to building internal representations of gravity in normal and altered gravitational contexts. Furthermore, delaying the timing of visual feedback of object displacement has been shown to affect GF. Here, we explored whether and the extent to which spatial features of visual feedback movement, such as motion direction, may contribute to GF control. If this were the case, a spatial mismatch between actual (somatosensory) and visual feedback of object motion would elicit changes in GF modulation. We tested this hypothesis by asking participants to generate vertical object movements while visual feedback of object position was congruent (0° rotation) or incongruent (180° or 90°) with the actual object displacement. The role of vision on GF control was quantified by the temporal shift of GF modulation as a function of visual feedback orientation and actual object motion direction. GF control was affected by visual feedback when this was incongruent in the vertical (180°), but not horizontal dimension. Importantly, 180° visual feedback rotation delayed and anticipated GF modulation during upward and downward actual movements, respectively. Our findings suggest that during manipulation, spatial features of visual feedback motion are used to predict upcoming LF changes. Furthermore, the present study provides evidence that an internal model of gravity contributes to GF control by influencing sensory reweighting processes during object manipulation.

Simplified Three-Vector-Based Model Predictive Thrust Force Control With Cascaded Optimization Process for a Double-Side Linear Vernier Permanent Magnet Motor

In order to reduce thrust force and stator flux ripples, this article proposes a simplified three-vector-based model predictive thrust force control method for a double-side linear vernier permanent magnet motor. First, a simplified control set is designed to select two active voltage vectors (VVs) in the prediction stage, instead of evaluating redundant VVs. Null VVs are applied to adjust the amplitude of the modified VV, which can realize a full-range modulation by the predefined cost function. To simplify the tuning work of the weighting factor, the variables in the cost function only consider the stator flux term. Then, the duration time of the selected VVs is distributed reasonably through a novel cascaded optimization process. Also, the deadbeat principle is adopted to calculate the duty cycle. With the proposed method, the thrust force and stator flux ripples can be considerably minimized. The simulated and experimental results are conducted to verify the proposed control method.

Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.

Model Predictive Thrust Force Control of a Linear Flux-Switching Permanent Magnet Machine With Voltage Vectors Selection and Synthesis

To reduce the thrust force ripple of a complementary and modular linear flux-switching permanent magnet machine, a model predictive thrust force control (MPTFC) is developed and implemented in this paper based on the active voltage vectors selection (AVVS) and two-voltage-vector synthesis (TVVS). First, the active voltage vectors are screened and assigned to the specific sector, instead of traversing all the possible vectors, and consequently, significantly decrease prediction workload. Second, for the elimination of the weighting factor in the conventional MPTFC, the control object is transformed from the thrust force and stator flux to the stator flux only by using the load angle. Third, to achieve the steady-state performance improvement, the TVVS approach is applied in each control period, where the optimal TVVS and its dwell time are simultaneously obtained by only one cost function. Both simulated and experimental results verify that the steady-state performance improvement can be realized by the developed MPTFC through comparing with normal hysteresis current control and conventional MPTFC strategies.

Model Predictive Force Control in Grinding based on a Lightweight Robot

The extensive effort in manual labour for mould manufacturing is the main reason for outsourcing manufacturing resources to low-wage countries. Furthermore, the international mould and die sector faces a lack of skilled workers due to the unattractive working conditions, for example the stressing, monotonous work and a partly hazardous working atmosphere (e.g. nickel dust). Especially, the finishing process (e.g. grinding, lapping, polishing) of freeform surfaces is still less automated in metal processing. Therefore, many efforts are initiated in order to fully automate the finishing process, but the today’s approaches are usually limited on prototypal implemented and cost-intensive machine solutions.This work introduces a finishing control approach for freeform surfaces using a lightweight robot. The robot is used as an actuator as well as a sensor for closed-loop force control. The optimal force trajectory is determined by a CAD-model which describes the geometry of the workpiece. The noisy measured force signal is filtered by a Kalman filter which also estimates the system state for the model predictive controller (MPC). Finally, a simple position controller controls the position of the tool center point (TCP) and the MPC controls the force which occur on the tool. The results show that the force can be controlled with a higher accuracy than with a conventional impedance control, which is commercially provided.

Comparative study on optimization algorithms for online identification of an instantaneous force model in milling

Mechanistic force models allow an estimation of the force components in cutting technology. This is essential for an accurate simulation of the force, an analysis of the tool load, or a model-based predictive force control. The model coefficients do not only depend on the material and the tool but also on the engagement condition. This requires an online identification of those coefficients. To determine those coefficients, this paper uses curve fitting based on the instantaneous uncut chip thickness, a method that has only recently gained momentum in milling. This work applies this technique to the well-established, nonlinear Kienzle force model. The paper reviews a broad range of nonlinear, derivative-free optimization algorithms for this least-squares curve-fitting problem evaluating accuracy and runtime. The evaluation is conducted on 121experiments with distinct process conditions resulting in a statistically verified conclusion. The results indicate that in spite of a heterogeneous optimization space, local constrained algorithms are most suited for model identification.

Model predictive force control of hardware implementation for electro-hydraulic servo system

This manuscript introduces the application of Model Predictive Control (MPC) for high force control precision in a real industrial electro-hydraulic servo system (EHSS). Moreover, it presents a fractional order control (FOC) and conventional controllers (CC) based on genetic algorithm (GA). The GA technique has been used to tune the parameters of FOC and CC approach. In order to verify the ability of the proposed controller applied to the hydraulic press machines emulator using EHSS, a hardware implementation of a test press system is also suggested and setup to be used in this research. As a result, the study has been investigated using a simulation model then verified via the experimental implementation. In fact, the EHSS plays an important role in many industrial applications, especially in flight simulators, aircraft landing gear system, material testing machine and hydraulic press machines for which the high accuracy and fast response of the force or pressure control are exceedingly necessary. Real-time experiments on the EHSS are carried out to evaluate the proposed control approach in a large system parameters variation of working environments. Considerable improvement in the performance generated by the designed MPC controller is compared with the traditional and fractional order controllers. Moreover, the results show that the performance criteria in terms of settling, rise times, system overshoots, system parameters variation and applying different test signals are good values in case of applying MPC over using FOC and CC in this study. As a general conclusion, one can conclude that the MPC has the priority of applying it in the field of the industrial EHSS. The obtained results are promising in the field of mechatronic.

Sensorless Model Predictive Force Control with a Novel Weight Coefficients for 3-Phase 4-Switch Inverter Fed Linear Induction Motor Drives

The sensorless model predictive force control (SMPFC) is a strong method for controlling the drives of three-phase 4(6)-switch inverter linear induction motors. This kind of inverter can be employed for fault tolerant control in order to solve the problem of open/short circuit in 6-switch inverters (B6). This paper proposed a method for the SMPFC of a linear induction motor (LIM) with a 4-switch inverter fed along with DC-link voltage offset suppression. Numerical simulations have been performed on several different LIMs in MATLAB software for determine the weight coefficients of the stator flux and the reduced of the capacitor voltage offset. Then, a relationship has been extracted for determining the weight coefficients of the stator flux and DC-link voltage in the cost function of the SMPFC. Using weight coefficients in the cost function, the B4 inverter voltage vectors under the DC-link capacitor voltage swing were obtained for the exact prediction. The balance among the currents was improved by determining the appropriate value for the weight coefficient of the reduced of the capacitor voltage offset. Simulation results were provided for the validation of the suggested control method.

Fuzzy Predictive Force Control (FPFC) for Speed Sensorless Control of Single-side Linear Induction Motor

In this paper a model fuzzy predictive force control (FPFC) for the speed sensorless control of a single-side linear induction motor (SLIM) is proposed. The main purpose of of predictive control is minimizing the difference between the future output and reference values. This control method has a lower force ripple and a higher convergence speed in comparison to conventional predictive force control (CPFC). In this paper, CPFC and FPFC are applied to a linear induction motor and their results are compared. The results show that this control method has better performance in comparison to the conventional predictive control method.

Fuzzy Predictive Force Control (FPFC) for Speed Sensorless Control of Single-side Linear Induction Motor

Fuzzy Predictive Force Control (FPFC) for Speed Sensorless Control of Single-side Linear Induction Motor

Perceptual influences of error size on voluntary force control during a compound sinusoidal force task

Visual feedback that provides error information is critical to task quality and motor adjustments. This study investigated how the size of perceived errors via visual feedback affected rate control and force gradation strategy of a designate force task. Fourteen young adults coupled force exertions to a compound sinusoidal signal (0.2 Hz and 0.5 Hz) that fluctuated around a mean level of 30% of maximal voluntary contraction, when the size of execution errors were differently scaled with the error amplification factors. In the low (LAF) and high (HAF) amplification factor conditions, the execution errors in the visual display half and double of the real errors, respectively. The visualized error was the real errors in the medium amplification factor (MAF) condition. In addition to a phase-lead of force output, the LAF condition that virtually reduced the size of error feedback associated with a poorer task accuracy than the MAF and HAF conditions. Virtual increase in error size of visual feedback selectively suppressed the fast target force at 0.5 Hz. In addition, complexity and high-frequency components (>0.75 Hz) of force outputs multiplied progressively with increasing error size. Error-enhancing feedback suppressed fast target force, accentuating the use of error information to tune force output, whereas error-reducing feedback enhanced fast target force in favor of predictive force control.