Soft robotics is a new field that uses actuators that are non-standard and compatible materials. Industrial robotics is high-throughput manufacturing devices that are quick and accurate. They are built on rigid-body mechanisms. The advancement of robotic production now depends on the inclusion of staff in manufacturing processes, allowing for the completion of activities that need cognitive abilities that are now beyond the scope of artificial networks. Hydrostatic pressure is used to achieve high deflections of structures that are based on the elastomeric in Fluid Actuators (FAs). Soft actuators based on the fluid are a popular choice safe for humans and lightweight robots. However, owing to a deficiency of durable, accurate, and affordable sensors that can be combined with actuator systems that are highly deformable and that use low-cost materials and production, closed-loop management of such actuators remains difficult. Such actuators, in combination with hydrodynamic force feedback, form a series-elastic actuation (SEA), which eliminates virtually friction from all driving-point. Fuzzy control is a smart computing analysis technique that enables complex systems to be controlled independently of a mathematical model. Fuzzy logic is used to optimize the parameters of a Fuzzy Logic Controller (FLC’s) function to find the best rational controller for an automated robot. Because discontinuous endpoint friction is undetectable to the pressure of the fluid internally, feedback from traditional external force using force/tactile sensing is preferred. As a result, a fuzzy-based control using linear feedback was developed and used to test the integrated system’s response dynamically and location accuracy.
Recently, human-friendly robots are involved in many business sectors for regularizing their commercial and basic activities quick and accurate. Especially, the industrial manufacturing units the usage of robots are inevitable for increasing production process with accurate completion of activities that brings cognitive abilities that are beyond the scope of artificial systems. Thereby, a new breed of the robotic system has emerged, with architecture and control techniques based on the potential to execute stable physical human-robot encounters. However, it provides flexibility of including an advancement of modular robotic devices such as serial elastic actuators are capable for high-throughput devices have aided the advent of new structures with sufficient protection and engagement goals. Moreover, it is still rigid connection robots with control and sensing capabilities that enable them to operate quite safely in diverse societies [
Fuzzy control is a fundamental method that relates to professional systems and enables complex systems to be controlled without the use of theoretical equations. Due to this new property, fuzzy control is well suited to dynamic systems that are hard to analyze analytically, often due to a lack of understanding of the system or the inability to define the innovative technique owing to the unquantifiability of the method outputs and inputs. As a result, this method can be viewed as a method for converting a linguistic control scheme dependent on an operator’s experience into an automated control policy [
The fuzzy logic controller has been extensively used by scientists to create controllers for a broad array of applications in various fields since its inception. The smart medicine dispenser [
In soft wearable robotics, embedded sensors must endure extremely large distortion while maintaining the high adherence of the corresponding actuator. The field of expandable electronics is becoming increasingly common in this context [
In soft wearable robots, the proposed fluid actuators provide a fuzzy-based optimization and linear feedback controller. The fluid-driven technique is the most widely used form of actuation in soft industrial robotics. To tempt motion or produce force, this technique employs fluidic pressurization of complex cavities inside soft actuators. Deformable fluids are used to produce adjustable stiffness throughout complex actuators in flexible fluidic actuators (FFAs) driven systems.
Fuzzy-based optimization in the fluid actuator is considered as the main functional membership parameter, even granularity and number of rules enables us to provide the best solution to most of the challenges encountered during the development of the soft wearable robots. Fuzzy control is a smart computing analysis technique that enables complex systems to be controlled independently of a mathematical model.
Fuzzy Logic Controllers (FLC) based optimization is of different types namely FLC-PD, FLC-PI, and FLC-PID. Based on the selection of the fuzzy-based optimization the corresponding input data such as error e, change in error
Here, the proportional gain coefficient is denoted as
A Proportional-Differential (PD) FLC based optimization is enhanced by utilizing error and change in error model is represented in the following expression,
Here, the coefficient of the proportional is denoted as
Here, the proportional gain coefficient is denoted as
The FLC-PID includes the conditions such as
A PD fuzzy controller-based optimization cannot eradicate error in the static condition whereas PI-fuzzy controller-based optimization can solve the challenges. According to the integral terms in the dependent variables, it seems to have a slower response. An FLC-PID is proposed to satisfy the functional requirement including a quick rise time, lesser settling time, 0 static status errors, and minimal overshoot. Fuzzy logic controller-PID is usually four-dimensional in which it contains three inputs and one output device with error e, change in error ∆e , and a sum of errors
The evaluation effects of joint trajectory prediction in manipulator robots are contrasted with the outcomes obtained from traditional fuzzy-PID controllers, such an option enables representative changes. It should be remembered that using the second derivative error in the approved fuzzy controller-based optimization becomes unique and thus contributes to the regulation of manipulator robots. The proposed novel approach is used to monitor the joint trajectory monitoring of a 2-DoF planar robot and the corresponding simulation is produced by utilizing MatLab or Simulink tools. The performance of the fuzzy controller with the traditional PID controller is evaluated.
In the manipulator robot model, to acquire the conceptual framework of a 2-DoF planar of i = 1, 2,…, n. q generalized coordinates and joints are determined with the below-recommended notation.
Here, the generalized forces vector is denoted as
Here, the mass of the first link and second link are represented as
The two pressure-driven flexible fluid actuators (FFAs) are evenly positioned concerning the central line in the soft wearable robotic elastomeric structure of the module. A flexible fluid actuator has a dual cylinder container with an in-extensible loop inserted into the elastomeric element along the channel longitudinal wall for radial reinforcement. As of press, the chamber structure permits it to stretch longitudinal direction with limited lateral extension to the central axes. The module’s ability to fold around steady radius curves is determined by the perfect elongation of one Flexible fluid actuators (FFA) at a period. A Degree of Freedom (DoF) present within the module is in terms of kinematics, which is defined as the plane y-z with bending angle produced by the pressurized Flexible fluid actuators (FFA) at a period. Based on two Flexible fluid actuators (FFA) is enabled, the angle may be negative or positive. The two actuators are parallel pressurized at a similar time will provide an increased DoF, resulting in a 30% elongation of the module longitudinally. The power exerted in enabling the pressurization of at least one FFA at a period ignores the DoF extension module. An evenly controlled two combined small latching valve and main pressure line in between main regulated pressure line and FFA chambers are used to manipulate FFAs pressure. The integrated latching valves are enabled by an embedded wireless microcontroller along with a Printed Circuit Board (PCB) in response to the main systems commands. The pressure in the mainline is regulated by a relative pressure transducer, and wireless contact with the support of module microcontrollers using a temporized control scheme. The module consists of an inner broadcast channel for potential deployment on a multi-module soft wearable robotic design that enables path communication between the main pressure lines with other modules.
Impedance monitoring based on system Flexible fluid actuators uses a velocity trigger actuator namely fluid servo-valve, in which the torque source is obtained from an electric motor to regulate fluid flow and also friction to make the desired impedance. Flexible fluid actuators (FFAs) are engineered to have very weak mechanical impedance but still the friction in the transmission, joints, and motor shaft bearings. The force in linear feedback control in FFAs is used to optimize the mechanical impedance in a straightforward process. 1-DOF inertia-spring-damper mechanism with dynamical friction
The above equation is rearranged into an equivalent system as follows,
If there is an increment in
The passivity range for 1-DOF event of
With the conservative passivity range
Identify the conditions in which the actuator is not back-driven
If the actuator is back-driven, then the transmission resistance is greater than the moving and end friction
The force feedback internally is involved in reducing to half of the driving point friction dynamically.
Force feedback structure includes a force feedback filter that substitutes the proportionate force gain
Flexible fluid actuators have a high force density that permits a low-end point mass in serial-chain exploiters with several degrees of freedom. An intentional sequence of elasticity and damping is tuned by changing the fluid conformity and transmitting the internal geometry hoses. The joint torques can be estimated by measuring the internal pressure in the mainline of fluid actuators. Internal pressure calculations do not reveal the endpoint static friction. Exterior contact forces can be determined with high accuracy by using internal fluid pressure parameters and soft-continuum fluid actuators depending on the material elastic modulus and non-rubbing membrane closures but still, these are not required for endpoint wiring.
The outcomes of the proposed controller and actuators to stabilize the mobile robot are presented in this section. The suggested controller is used to monitor the physical dimensions of a flexible manipulator. Simulation experiments are performed for the sake of contrast. To demonstrate the controller’s robustness.
The signal of the GT method is contrasted to the measured sensor’s reaction utilizing an average curve to assess the accuracy of the calibration protocol and the appropriateness of the chosen sensors. With angles higher than 5 degrees, this contrast reinforces the flex fold sensors with higher precision and repeatability. The RMSE among these two curves is observed to be 1.3 degrees and involved in ensuring the estimated accuracy value depending on the RMSE value.
Voltage (V) | Bending angle in inflation phase (Deg) | Bending angle in deflation phase (Deg) | Average of the two phases (Mean) (Deg) |
---|---|---|---|
1.0 | 08 | 05 | 6.50 |
1.2 | 15 | 12 | 13.5 |
1.4 | 17 | 16 | 16.5 |
1.6 | 22 | 20 | 21.0 |
1.8 | 24 | 23 | 23.5 |
2.0 | 25 | 25 | 25.0 |
TIME (s) | Canonical step reference signal | Bending angle with real-system response (deg) | Bending angle with nominal linear model response (deg) |
---|---|---|---|
2 | 15 | 13.5 | 12.6 |
4 | 15 | 15 | 15 |
6 | 15 | 15 | 15 |
8 | 15 | 15 | 15 |
10 | 15 | 15 | 15 |
12 | 20 | 23.5 | 22.6 |
The above
In this paper, the soft wearable robot with fuzzy-based optimization and linear feedback-controlled fluid actuators is discussed. The activation functions of soft robot has controlled through trajectory monitoring on the kinematics and dynamic performance output of the model. The secured human friendly interaction has risen dramatically with help of soft wearable robotics that attempt to meet the need of utilizing the shock-absorbing properties of the components. Moreover, it has significantly improved the accuracy of perceptive sensing that would do from efficient linear fuzzy feedback controllers of the FFAs. The convergence of flex bend sensors into a soft bending framework is being actuated by pressure-controlled FFAs. Comparatively, Non-back-driven actuators weaken endpoint estimation in fluid-actuated models despite specific endpoint detection. However, proposed model has produced a reliable endpoint state evaluation is dependent on driving-point state input and internal pressure.