In numerous industries, especially in automation and industrial processes, the linear servo mechanism is used. However, the parameters of the friction and backlash models are frequently unknown for servomechanism systems, resulting in system uncertainty. High steady-state inaccuracy is caused by friction, whereas undesired vibration is caused by blowback. In servomechanism systems, friction is an issue that is still not sufficiently addressed by a realistic model. To address these challenges, this research on the linear servo system is controlled by a proportional-integral (PI-Cascaded) controller, which enables systems to respond more rapidly, reduce or reject disturbance, and arrive at a steady state more quickly. Moreover, the controller’s parameters are crucial to getting the best performance from a particular controller. As a result, the controller settings were adjusted using four different meta-heuristic optimization algorithms: Surrogate Based Optimization (SBO), Hybrid Genetic Pattern Search Algorithm (HGSPA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) with four objective functions: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Square Error (ITSE), and Integral Time Absolute Error (ITAE). Throughout the system’s experimental testing, 50 cm was employed as the reference input. Negligible overshoot, quick rise and settling times, and excellent responsiveness are all characteristics of the PSO algorithm with ITSE objective function. Moreover, to assess the system’s robustness. A 50 N force was applied to the system, and a sine wave signal is input into the system. The system shows remarkable stability and resilience throughout the 50 N load experimentation test.

Modern neuromuscular rehabilitation engineering and assistive technology research have been constantly developing in the last 20 years. The upper body exoskeleton is an example of an assistive rehabilitation device. However, in order to solve its technological problems, interdisciplinary research is still necessary. This paper presents a new three-degrees of freedom (DOF) active upper-body exoskeleton for medical rehabilitation named “SAMA”. Its mechanical structure is inspired by the geometry and biomechanics of the human body, particularly the ranges of motion (ROM) and the needed torque. The SAMA exoskeleton was manufactured and assembled into an ergonomic custom-made wheelchair in a sitting posture in order to provide portability and subject comfort during experimental testing and rehabilitation exercises. Dynamic modeling using MATLAB–Simulink was used for calculating the inverse kinematics, dynamic analysis, trajectory generation and implementation of proportional–integral–derivative (PID) computed torque control (PID-CTC). A new framework has been developed for rapid prototyping (the dynamic modeling, control, and experimentation of SAMA) based on the integration between MATLAB–Simulink and the Robot Operating System (ROS) environment. This framework allows the robust position and torque control of the exoskeleton and real-time monitoring of SAMA and its subject. Two joints of the developed exoskeleton were successfully tested experimentally for the desired arm trajectory. The angular position and torque controller responses were recorded and the exoskeleton joints showed a maximum delay of 200° and a maximum steady state error of 0.25°. These successful results encourage further development and testing for different subjects and more control strategies.

Finding a balance between adaptability for unexpected or non-standard movements and repeatability for cyclic standard movements is a known control challenge for active lower-limb prostheses. This study integrates a volitional electromyography (EMG)-driven musculoskeletal model controller with a finite-state machine impedance controller. During the stance phase, the hybrid controller is activated, and during the swing phase, it functions as a position controller. To provide a seamless transition to the swing phase of the gait, the controller first runs in hybrid mode until it validates that the foot is entirely off the ground. The Gastrocnemius and Tibialis Anterior muscles were modeled using a Hill-type muscle model to function around an ankle joint. The system uses input from ankle sensors and EMG data from antagonist muscle pairs to activate the muscle models. In addition, muscle parameters within the model are optimized using a surrogate-based optimization technique to improve the neuromuscular model controller’s performance and responsiveness. A transtibial amputation participant assessed the hybrid controller online for various activities, including ambulation on level ground, stairs, and ramps, using EMG signals from the residual limb as input. Combined with the impedance controller, the EMG control showed exceptional efficiency and repeatability, particularly for stair ascent, stair descent, and ramp descent. Moreover, according to user feedback, the user felt more involved and capable of using their muscles to control the motion, which improved their overall sensation of stability and control during these activities. The hybrid controller functioned more like an impedance controller during ramp ascents and walking on level terrain.

Lower limb amputation greatly affects quality of life by restricting functional mobility. Despite advancements in prosthetic design, powered transtibial prostheses still have limitations in user control and adaptability to dynamic environments. This research presents a comparative analysis between a novel electromyography (EMG)-driven variable impedance controller (VIC) and a hybrid controller (HC) that integrates a volitional EMG-driven musculoskeletal model with a finite-state machine impedance controller. A Hill-type muscle model was used to model the gastrocnemius and tibialis anterior muscles. Biomechanical testing was conducted with a transtibial amputee to assess the controllers’ performance across various tasks, including ambulation on level ground, stairs, and ramps, using EMG signals from the residual limb. Results demonstrated that the VIC provided more repeatable performance, perceived control, and power output. Notable effect sizes for peak power, observed in ramp ascent (Cohen’s d = -1.04) and high-speed level ground walking (Cohen’s d = -2.92), illustrate robust differences in joint-level output even when walking speeds and cadences were comparable. The greater predictability of the VIC led the user to feel more in control and comfortable throughout the various activities. On the other hand, the HC controller performed better in enabling more seamless transitions between gait subphases, particularly during stair ascent, which led to a significantly higher ROM (18.63 ± 1.53 deg vs. 12.43 ± 1.86 deg) and nearly double peak power compared to the VIC. This comparison lays the groundwork for future research into optimizing EMG-based control strategies that adapt to both biomechanical demands and user preferences.

The optimal neuroprosthetic control paradigm seeks to minimize the differences between artificial and natural physiology regarding subjective embodiment and dynamic performance. This study introduces a volitional control system integrating an electromyography (EMG)-driven musculoskeletal model with a finite state machine (FSM) impedance controller. A Hill-type muscle model is used to simulate the Gastrocnemius (GAS) and Tibialis Anterior (TA) muscles around the ankle joint. The system activates these muscles using input from ankle sensors and EMG data from antagonist muscles. To improve functionality and responsiveness, muscle parameters are optimized using a surrogate-based optimization approach. The impact of EMG control on a hybrid controller was analyzed, focusing on muscle activation during the stance phase. Two controllers were tested on a transtibial amputee for level-ground walking and stair ascent. Insights were gained into optimizing muscle activation for better gait dynamics. The model-based design technique was employed to automate validation, verification, and coding, reducing manual steps and errors. For level-ground walking, both hybrid controllers behaved more like an impedance controller than an EMG control. Disabling EMG control during plantarflexion with controller 1 prevented excessive plantarflexion, leading to a more natural gait. Controller 1, which used the TA muscle exclusively during controlled dorsiflexion, demonstrated greater repeatability. During stair ascent, both controllers allowed the user to place their toe first at each step, closely mimicking a natural gait pattern. Controller 2 exhibited better repeatability and slightly higher torque at the start of the controlled dorsiflexion phase, simplifying the control strategy and reducing computational effort.

Research efforts in neuromuscular therapy and assistive devices have been in constant progress in the last 20 years. One of the most sought-after assistive devices includes the rehabilitation of upper limb exoskeletons. This paper presents the implementation of an optimization-based optimal controller technique for the "SAMA" three-DOF active upper limb exoskeleton for rehabilitation. Proportional-integral-derivative computed torque control (PID-CTC) is the basis of the proposed controller. The hybrid genetic pattern search method is then used to optimize the ideal controller settings (HGPSA). Using HGPSA, four objective functions are selected to find the best control values. The integral absolute error (IAE), integral time absolute error (ITAE), integral square error (ISE), and integral time square error (ITSE) are these functions. The obtained results demonstrate that the optimal dynamic responses are optimized by an ideal CTC based on HGPSA, with a highest steady-state error of 0.18° and 0% overshoot. Additionally, a reaction safe interaction strategy is added to the robot model to provide compliance during trajectory planning.