Multiobjective optimal TCSC placement using multiobjective grey wolf optimizer for power losses reduction.

This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

Reductive Sn2+ Compensator for Efficient and Stable Sn-Pb Mixed Perovskite Solar Cells.

Tin-lead (Sn-Pb) mixed perovskite with a narrow bandgap is an ideal candidate for single-junction solar cells approaching the Shockley-Queisser limit. However, due to the easy oxidation of Sn2+, the efficiency and stability of Sn-Pb mixed perovskite solar cells (PSCs) still lag far behind that of Pb-based solar cells. Herein, highly efficient and stable FA0.5MA0.5Pb0.5Sn0.5I0.47Br0.03 compositional PSCs are achieved by introducing an appropriate amount of multifunctional Tin (II) oxalate (SnC2O4). SnC2O4 with compensative Sn2+ and reductive oxalate group C2O4 2- effectively passivates the cation and anion defects simultaneously, thereby leading to more n-type perovskite films. Benefitting from the energy level alignment and the suppression of bulk nonradiative recombination, the Sn-Pb mixed perovskite solar cell treated with SnC2O4 achieves a power conversion efficiency of 21.43%. More importantly, chemically reductive C2O4 2- effectively suppresses the notorious oxidation of Sn2+, leading to significant enhancement in stability. Particularly, it dramatically improves light stability.

Enhancing the Luminescence of La3Mg2NbO9:Mn4+ Phosphor through H3BO3 and Charge Compensator Co-Doping for Use in Plant Growth Lamps.

Mn4+-doped red-light-emitting phosphors have become a research hotspot that can effectively enhance photosynthesis and promote morphogenesis in plants. Herein, the red phosphor La3Mg2NbO9:Mn4+ was synthesized through the solid-state reaction method. The effects of adding H3BO3 and a charge compensator R+ (R = Li, Na, K) on the crystal structure, morphology, quantum efficiency, and luminous performance of the La3Mg2NbO9:Mn4+ phosphor were systematically analyzed, respectively. The results showed that adding H3BO3 flux and a charge compensator improved the quantum efficiency and luminescence intensity. The emission intensity of the phosphor was enhanced about 5.9 times when Li+ was used as the charge compensator, while it was enhanced about 240% with the addition of H3BO3 flux. Remarkably, it was also found that the addition of H3BO3 flux and a charge compensator simultaneously improved the thermal stability at 423 K from 47.3% to 68.9%. The prototype red LED fabricated using the La3Mg2NbO9:Mn4+,H3BO3,Li+ phosphor exhibited a perfect overlap with the phytochrome absorption band for plant growth. All of these results indicate that the La3Mg2NbO9:Mn4+,H3BO3,Li+ phosphor has great potential for use in agricultural plant lighting.

Merging cross-border flow optimization techniques for performance maximization.

Both FARCROSS and TRINITY EU research projects aim to increase cross-border electricity flow and regional cooperation. The integration of SmartValve and T-SENTINEL systems offers benefits such as enhancing grid security and reliability, managing thermal constraints, and maximizing utilization of existing infrastructure. The combined system can achieve a more efficient and less costly coordinated network security process, increase cross-border capacities, and promote regional electricity market integration, benefiting the local communities with significant CO2 emissions avoidance and reduced electricity prices. Overall, the integration of SmartValve and T-SENTINEL can provide significant improvements in flexibility, making cross-border connections more robust and adaptive to the evolution of the electrical power industry.

Design and Control of a Single-Leg Exoskeleton with Gravity Compensation for Children with Unilateral Cerebral Palsy.

Children with cerebral palsy (CP) experience reduced quality of life due to limited mobility and independence. Recent studies have shown that lower-limb exoskeletons (LLEs) have significant potential to improve the walking ability of children with CP. However, the number of prototyped LLEs for children with CP is very limited, while no single-leg exoskeleton (SLE) has been developed specifically for children with CP. This study aims to fill this gap by designing the first size-adjustable SLE for children with CP aged 8 to 12, covering Gross Motor Function Classification System (GMFCS) levels I to IV. The exoskeleton incorporates three active joints at the hip, knee, and ankle, actuated by brushless DC motors and harmonic drive gears. Individuals with CP have higher metabolic consumption than their typically developed (TD) peers, with gravity being a significant contributing factor. To address this, the study designed a model-based gravity-compensator impedance controller for the SLE. A dynamic model of user and exoskeleton interaction based on the Euler-Lagrange formulation and following Denavit-Hartenberg rules was derived and validated in Simscape™ and Simulink® with remarkable precision. Additionally, a novel systematic simplification method was developed to facilitate dynamic modelling. The simulation results demonstrate that the controlled SLE can improve the walking functionality of children with CP, enabling them to follow predefined target trajectories with high accuracy.

Deep optimization design of 2D repetitive control systems with saturating actuators: An adaptive multi-population PSO algorithm.

This paper presents an optimization design method for a two-dimensional (2D) modified repetitive control system (MRCS) with an anti-windup compensator. Using lifting technology, a 2D hybrid model of the MRCS considering actuator saturation is established to describe the control and learning of the repetitive control. A linear-matrix-inequality (LMI)-based sufficient condition is derived to ensure the stability of the MRCS. Two tuning parameters, the selection of which is critical to the system design, are used in the LMI to adjust the control and learning, and hence the reference-tracking performance. A new cost function, developed through time domain analysis, directly evaluates the control performance of the system without calculating control errors, thus reducing the optimization time. Based on this cost function, an adaptive multi-population particle swarm optimization algorithm is presented to select an optimal pair of tuning parameters in which multiple populations cooperatively search in non-intersecting search intervals. An anti-windup term is added between the low-pass filter and the time delay in the modified repetitive controller to mitigate the undesirable effect of actuator saturation on system performance and stability. Simulations and experiments on the speed control of a rotation control system demonstrate the validity of the approach.

A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system.

Background and purpose: A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods: A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results: For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions: A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system.

A Phantom Study on Feasibility of Manual Field-in-Field Clinical Implementation for Total Body Irradiation and Comparison of Midplane Dose with Different Bilateral TBI Techniques.

Objective: The aim of this study is to implement a new treatment technique in total body irradiation (TBI) using the manual field-in-field-TBI (MFIF-TBI) technique and dosimetrically verifying its results with respect to compensator-based TBI (CB-TBI) and open field TBI technique. Materials and Methods: A rice flour phantom (RFP) was placed on TBI couch with knee bent position at 385 cm source to surface distance. Midplane depth (MPD) was calculated for skull, umbilicus, and calf regions by measuring separations. Three subfields were opened manually for different regions using the multi-leaf collimator and jaws. The treatment Monitor unit (MU) was calculated based on each subfield size. In the CB-TBI technique, Perspex was used as a compensator. Treatment MU was calculated using MPD of umbilicus region and the required compensator thickness was calculated. For open field TBI, treatment MU was calculated using MPD of umbilicus region, and the treatment was executed without placing compensator. The diodes were placed on the surface of RFP to measure the delivered dose and the results were compared. Results: The MFIF-TBI results showed that the deviation was within ± 3.0% for the different regions, except for the neck for which the deviation was 8.72%. In the CB-TBI delivery, the dose deviation was ± 3.0% for different regions in the RFP. The open field TBI results showed that the dose deviation was not within the limit ± 10.0%. Conclusion: The MFIF-TBI technique can be implemented for TBI treatment as no TPS is required, and laborious process of making a compensator can be avoided while ensuring that the dose uniformity in all the regions within the tolerance limit.

Iterative neural network adaptive robust control of a maglev planar motor with uncertainty compensation ability.

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM.

Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning.

PURPOSE: A novel compensator-based system has been proposed which delivers intensity-modulated radiation therapy (IMRT) with cobalt-60 beams. This could improve access to advanced radiotherapy in low- and middle-income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low- and middle-income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS). METHODS: The novel treatment device considered here employs a cobalt-60 source and nine compensators. Each compensator is produced by 3-D printing a thin plastic mold which is then filled on-demand within the machine with reusable 2-mm-diameter spherical tungsten balls. This system was commissioned in the Eclipse TPS and validation tests were conducted with Monte Carlo using Geant4 Application for Tomographic Emission for percentage depth dose, in-plane profiles, penumbra, and IMRT dose validation. And the American Association of Physicists in Medicine Task Group 119 benchmarking testing was performed. Additionally, compensator-based cobalt-60 IMRT plans were created for 46 head-and-neck cancer cases and compared to the linac-based volumetric modulated arc therapy (VMAT) plans used clinically, then dosimetric parameters were evaluated. Beam-on time for each field was calculated. In addition, the measurement was also performed in a limited environment and compared with the Monte Carlo simulations. RESULTS: The differences in percent depth doses and in-plane profiles between the Eclipse and Monte Carlo simulations were 0.65% ± 0.41% and 1.02% ± 0.99%, respectively, and the 80%-20% penumbra agreed within 0.46 ± 0.27 mm. For the Task Group 119 validation plans, all treatment planning goals were met and gamma passing rates were >95% (3%/3 mm criteria). In 46 clinical head-and-neck cases, the cobalt-60 compensator-based IMRT plans had planning target volume (PTV) coverages similar to linac-based VMAT plans: all dosimetric values for PTV were within 1.5%. The organs at risk dose parameters were somewhat higher in cobalt-60 compensator-based IMRT plans versus linac-based VMAT plans. The mean dose differences for the spinal cord, brain, and brainstem were 4.43 ± 1.92, 3.39 ± 4.67, and 2.40 ± 3.71 Gy, while those for the rest of the organs were <1 Gy. The average beam-on time per field was 0.42 ± 0.10 min for the 6 MV multi-leaf-collimator plans while those for the cobalt-60 compensator plans were 0.17 ± 0.01 and 0.31 ± 0.01 min at the dose rates of 350 and 175 cGy/min. There was a good agreement between in-plane profiles from measurements and Monte Carlo simulations, which differences are 1.34 ± 1.90% and 0.13 ± 2.16% for two different fields. CONCLUSIONS: A novel compensator-based IMRT system using cobalt-60 beams was commissioned and validated in a commercial TPS. Plan quality with this system was comparable to that of linac-based plans in all test cases with shorter estimated beam-on times. This system enables reliable, high-quality plans with reduced cost and complexity and may have benefits for underserved regions of the world. This system is being integrated into the RPA, a web-based platform for auto-contouring and auto-planning.

Luminescent Properties and Charge Compensator Effects of SrMo0.5W0.5O4:Eu3+ for White Light LEDs.

The high-temperature solid-phase approach was used to synthesize Eu3+-doped SrMo0.5W0.5O4 phosphors, whose morphological structure and luminescence properties were then characterized by XRD, SEM, FT-IR, excitation spectra, emission spectra, and fluorescence decay curves. The results reveal that the best phosphor synthesis temperature was 900 °C and that the doping of Eu3+ and charge compensators (K+, Li+, Na+, NH4+) had no effect on the crystal phase change. SrMo0.5W0.5O4:Eu3+ has major excitation peaks at 273 nm, 397 nm, and 464 nm, and a main emission peak at 615 nm, making it a potential red fluorescent material to be used as a down converter in UV LEDs (273 nm and 397 nm) and blue light LEDs (464 nm) to achieve Red emission. The emission spectra of Sr1-yMo0.5W0.5O4:yEu3+(y = 0.005, 0.01, 0.02, 0.05, 0.07) excited at 273 were depicted, with the Eu3+ concentration increasing the luminescence intensity first increases and then decreases, the emission peak intensity of SrMo0.5W0.5O4:Eu3+ achieves its maximum when the doping concentration of Eu3+ is 1%, and the critical transfer distance is calculated as 25.57 Å. When various charge compensators such as K+, Li+, Na+, and NH4+ are added to SrMo0.5W0.5O4:Eu3+, the NH4+ shows the best effect with the optimal doping concentration of 3wt%. The SrMo0.5W0.5O4:Eu3+,NH4+ color coordinate is (0.656,0.343), which is close to that of the ideal red light (0.670,0.333).

Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN-LSTM fusion network.

This paper is concerned with the autonomous effective collision avoidance strategy for multiple unmanned aerial vehicles (multi-UAV) in limited airspace under the framework of proximal policy optimization (PPO) algorithm. An end-to-end deep reinforcement learning (DRL) control strategy and a potential-based reward function are designed. Next, the CNN-LSTM (CL) fusion network is constructed by fusing the convolutional neural network (CNN) and the long short-term memory network (LSTM), which realizes the feature interaction among the information of multi-UAV. Then, a generalized integral compensator (GIC) is introduced into the actor-critic structure, and the CLPPO-GIC algorithm is proposed by combining CL and GIC. Finally, we validate the learned policy in various simulation environments by performance evaluation. The simulation results show that the introduction of the LSTM network and GIC can further improve the efficiency of collision avoidance, and the robustness and accuracy of the algorithm are verified in different environments.

Cobalt compensator-based IMRT device: A treatment planning study of head and neck cases.

PURPOSE: Our goal is to develop a novel cobalt-compensator-based IMRT device for low- and middle-income countries that is reliable and cost-effective while delivering treatment plans of equal quality to those from linac-MLC devices. The present study examines the quality of treatment plans using this device. METHODS: A commercial treatment planning system (TPS; RayStation v.8B) was commissioned for this device using Monte Carlo simulations from the Geant4 toolkit. Patient-specific compensators were created as regions-of-interest. Thirty clinical head & neck cases were planned and compared to clinical plans with a 6MV linac using IMRT. The mock head and neck plan from TG-119 was used for further validation. RESULTS: PTV objectives were achieved in all 30 plans with PTV V95% >95 %. OAR sparing was similar to clinical plans. There were 14 cases where OAR dose limits exceeded the recommended QUANTEC limits in the clinical plan in order to achieve target coverage. OAR sparing was better in the cobalt compensator plan in 8 cases and worse in 3 cases, in the latter cases exceeding the clinical plan doses by an average of 8.22 % (0.0 %-13.5 %). Average field-by-field gamma pass-rate were 93.7 % (2 %/2mm). Estimated treatment times using the Co-60 compensator device were 1 min 27 s vs 1 min 2 s for the clinical system. CONCLUSION: This system is the first of its kind to allow for IMRT with a Co-60 device. Data here suggests that the delivery meets plan quality criteria while maintaining short treatment times which may offer a sustainable and cost-low option for IMRT on the global scale.

Implementation of a fully analog feedback loop with a Carbon-Black-based fractional order controller.

Fractional calculus is a mathematical framework that has attracted considerable interest from mathematicians, physicists, and engineers. Among its applications, the use of fractional calculus in the automatic control field has led to interesting results, such as more robust controllers, compared to their integer-order counterparts. The proposed work utilizes the physical realization of a solid-state fractional-order capacitor for the implementation of a fractional-order lead compensator. The proposed capacitor is realized using a carbon black-based dielectric. Therefore, a fully analog closed-loop system implementation is realized. A suitable case study is conducted to validate the controller performance, both from simulations and experimentally. The obtained results further confirm the possibility of realizing and applying a fully analog fractional-order controller.

Evaluation of Surface Dose and Commissioning of Compensator-Based Total Body Irradiation.

Purpose: The aim of the current study is to commission compensator-based total body irradiation (TBI) and to compare surface dose using percentage depth dose (PDD) while varying the distance between beam spoiler and phantom surface. Materials and Methods: TBI commissioning was performed on Elekta Synergy® Platform linear accelerator for bilateral extended source to surface distance treatment technique. The PDD was measured by varying the distance (10 cm, 20 cm, 30 cm, and 40 cm) between the beam spoiler and the phantom surface. Beam profile and half-value layer (HVL) measurement were carried out using the FC65 ion-chamber. Quality assurance (QA) was performed using an in-house rice-flour phantom (RFP). In-vivo diodes (IVD) were placed on the RFP at various regions to measure the delivered dose, and it was compared to the calculated dose. Results: An increase in Dmax and surface dose was observed when beam spoiler was moved away from the phantom surface. The flatness and symmetry of the beam profile were calculated. The HVL of Perspex and aluminum is 17 cm and 8 cm, respectively. The calculated dose of each region was compared to the measured dose on the RFP with IVD, and the findings showed that the variation was <4.7% for both Perspex and Aluminum compensators. Conclusion: The commissioning of the compensator-based TBI technique was performed and its QA measurements were carried out. The Mayneord factor corrected PDD and measured PDD values were compared. The results are well within the clinical tolerance limit. This study concludes that 10 cm -20 cm is the optimal distance from the beam spoiler to phantom surface to achieve prescribed dose to the skin.

Deep learning fuzzy immersion and invariance control for type-I diabetes.

In this study, a novel approach is proposed for glucose regulation in type-I diabetes patients. Unlike most studies, the glucose-insulin metabolism is considered to be uncertain. A new approach on the basis of the Immersion and Invariance (I&I) theorem is presented to derive the adaptation rules for the unknown parameters. Also, a new deep learned type-II fuzzy logic system (T2FLS) is proposed to compensate the estimation errors and guarantee stability. The suggested T2FLS is tuned by the singular value decomposition (SVD) method and adaptive tuning rules that are extracted from stability investigation. To evaluate the performance, the modified Bergman model (BM) is applied. Besides the dynamic uncertainties, the meal effect on glucose level is also considered. The meal effect is defined as the effect of edibles. Similar to the patient activities, the edibles can also have a major impact on the glucose level. Furthermore, to assess the effect of patient informal activities and the effect of other illnesses, a high random perturbation is applied to glucose-insulin dynamics. The effectiveness of the suggested approach is demonstrated by comparing the simulation results with some other methods. Simulations show that the glucose level is well regulated by the suggested method after a short time. By examination on some patients with various diabetic condition, it is seen that the suggested approach is well effective, and the glucose level of patients lies in the desired range in more than 99% h.

Collision Localization and Classification on the End-Effector of a Cable-Driven Manipulator Applied to EV Auto-Charging Based on DCNN-SVM.

With the increasing popularity of electric vehicles, cable-driven serial manipulators have been applied in auto-charging processes for electric vehicles. To ensure the safety of the physical vehicle-robot interaction in this scenario, this paper presents a model-independent collision localization and classification method for cable-driven serial manipulators. First, based on the dynamic characteristics of the manipulator, data sets of terminal collision are constructed. In contrast to utilizing signals based on torque sensors, our data sets comprise the vibration signals of a specific compensator. Then, the collected data sets are applied to construct and train our collision localization and classification model, which consists of a double-layer CNN and an SVM. Compared to previous works, the proposed method can extract features without manual intervention and can deal with collision when the contact surface is irregular. Furthermore, the proposed method is able to generate the location and classification of the collision at the same time. The simulated experiment results show the validity of the proposed collision localization and classification method, with promising prediction accuracy.

Fractional-order sliding mode control with a predefined-time observer for VTVL reusable launch vehicles under actuator faults and saturation constraints.

For vertical takeoff vertical landing (VTVL) reusable launch vehicle (RLV) with actuator faults and saturation constraints, this paper presents a composite control system including a high-order predefined-time extended state observer (HO-PTESO), a predefined-time anti-saturation compensator (PTASC), and a fractional-order practical predefined-time sliding mode control law (FO-PPTSMC). The HO-PTESO accomplishes the precise estimation of disturbance in a time interval predefined by only one design parameter, resulting in a simple and weakly conservative parameter tuning process for temporal demands. Moreover, the peaking value problem is well addressed. The PTASC serves to ensure the stability of the saturated system. Auxiliary variables of the PTASC remain bounded during the saturation process and vanish within the predefined time interval after the saturation process ends, avoiding long-term impacts on the attitude tracking that are common concerns for existing ASCs. Using the estimated disturbance, the FO-PPTSMC enforces unsaturated system states to a predefined residual set of the origin in a chattering-alleviated manner within the predefined time interval. Two parameters respectively predefine the convergence range and time, resulting in a considerably simplified synthesis procedure. Ultimately, numerical simulations on the double integrator system and VTVL RLV model validate the efficiency of the proposed control system.

Optimal sliding mode control for cutting tasks of quick-return mechanisms.

A solution of the constant cutting velocity problem of quick-return mechanisms is the main concern of this paper. An optimal sliding mode control in the task space is used to achieve uniform and accurate cuts throughout the workpiece. The switching hyperplane is designed to minimize the position error of the slider-dynamics in an infinite horizon. A Jacobian compensator is used to exploit the mechanical advantage and ensure controllability. The velocity profile is constructed in terms of the mechanism and workpiece geometric properties. Stability of the closed-loop dynamics is verified with the Lyapunov stability theory. Experiments are carried out in a quick-return mechanism prototype to validate the proposal.