Balance Recoverability and Control of Bipedal Walkers with Foot Slip

Low-friction foot/ground contacts present a particular challenge for stable bipedal walkers. The slippage of the stance foot introduces complexity in robot dynamics and the general locomotion stability results cannot be applied directly. We relax the commonly used assumption of non-slip contact between the walker foot and the ground and examine bipedal dynamics under foot slip. Using a two-mass linear inverted pendulum model, we introduce the concept of balance recoverability and use it to quantify the balanced or fall-prone walking gaits. Balance recoverability also serves as the basis for the design of the balance recovery controller. We design the within- or multi-step recovery controller to assist the walker to avoid fall. The controller performance is validated through simulation results and robustness is demonstrated in the presence of measurement noises as well as variations of foot/ground friction conditions. In addition, the proposed methods and models are used to analyze the data from human walking experiments. The multiple subject experiments validate and illustrate the balance recoverability concept and analyses.

Rule extraction and optimization of fuzzy controllers in maximizing the captured power of wave energy converters

In recent years, the extraction of electrical energy from sea waves has been considered by researchers due to its advantages such as renewability and high amount of extracted energy, and in recent research studies, several scientific solutions have been presented to maximize the energy obtained from these converters. One of the major problems that is ignored in most articles is the existence of several uncertainties that occur as an uncertain parameter in the proposed models for various types of wave energy absorbers, including mechanical parts erosion, limescale deposition and algae on buoyant which causes it to change weight. Ignoring these uncertainties will reduce the accuracy of the controller performance. Therefore, in this paper, for the first time, a fuzzy control process with optimized extracted rules and parameters is applied to control the damper and spring coefficients of the mechanical model describing a power takeoff system, which not only uses fuzzy control properties but also covers uncertainties. Optimizing the number of rules and structure of membership functions provides acceptable controllable accuracy and speed as it is mentioned in simulation results.

Combined Feedback Feedforward Control of a 3-Link Musculoskeletal System Based on the Iterative Training Method

The investigation and study of the limbs, especially the human arm, have inspired a wide range of humanoid robots, such as movement and muscle redundancy, as a human motor system. One of the main issues related to musculoskeletal systems is the joint redundancy that causes no unique answer for each angle in return for an arm’s end effector’s arbitrary trajectory. As a result, there are many architectures like the torques applied to the joints. In this study, an iterative learning controller was applied to control the 3-link musculoskeletal system’s motion with 6 muscles. In this controller, the robot’s task space was assumed as the feedforward of the controller and muscle space as the controller feedback. In both task and muscle spaces, some noises cause the system to be unstable, so a forgetting factor was used to a convergence task space output in the neighborhood of the desired trajectories. The results show that the controller performance has improved gradually by iterating the learning steps, and the error rate has decreased so that the trajectory passed by the end effector has practically matched the desired trajectory after 1000 iterations.

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

Synthesis of a flight control system for such an aircraft that achieves stable and acceptable performance across a specified flying envelope in the presence of uncertainties represents an attractive and challenging design problem. This study uses the genetic self-tuning PID algorithm to develop an intelligent flight control system for the aerosonde UAV model. To improve the system's transient responses, the gains of the PID controller are improved using a genetic algorithm (GA). Simulink/MATLAB software is used to model and simulate the proposed system. The proposed PID controller integrated with the GA is compared with the classical one. Three simulation scenarios are carried out. In the first scenario, and at normal conditions, the proposed controller performance is better than the classical one. While in the second scenario, identical results are achieved from both controllers. Finally, in the third scenario, the PID controller with GA shows the robustness and durability of the system compared with the classical PID in presence of external wind disturbance. The simulation results prove the system parameters optimization.

Rapid prototyping and design of cybergenetic single-cell controllers

The design and implementation of synthetic circuits that operate robustly in the cellular context is fundamental for the advancement of synthetic biology. However, their practical implementation presents challenges due to low predictability of synthetic circuit design and time-intensive troubleshooting. Here, we present the Cyberloop, a testing framework to accelerate the design process and implementation of biomolecular controllers. Cellular fluorescence measurements are sent in real-time to a computer simulating candidate stochastic controllers, which in turn compute the control inputs and feed them back to the controlled cells via light stimulation. Applying this framework to yeast cells engineered with optogenetic tools, we examine and characterize different biomolecular controllers, test the impact of non-ideal circuit behaviors such as dilution on their operation, and qualitatively demonstrate improvements in controller function with certain network modifications. From this analysis, we derive conditions for desirable biomolecular controller performance, thereby avoiding pitfalls during its biological implementation.

Unmanned Aerial Vehicle Pitch Control under Delay Using Deep Reinforcement Learning with Continuous Action in Wind Tunnel Test

Nonlinear flight controllers for fixed-wing unmanned aerial vehicles (UAVs) can potentially be developed using deep reinforcement learning. However, there is often a reality gap between the simulation models used to train these controllers and the real world. This study experimentally investigated the application of deep reinforcement learning to the pitch control of a UAV in wind tunnel tests, with a particular focus of investigating the effect of time delays on flight controller performance. Multiple neural networks were trained in simulation with different assumed time delays and then wind tunnel tested. The neural networks trained with shorter delays tended to be susceptible to delay in the real tests and produce fluctuating behaviour. The neural networks trained with longer delays behaved more conservatively and did not produce oscillations but suffered steady state errors under some conditions due to unmodeled frictional effects. These results highlight the importance of performing physical experiments to validate controller performance and how the training approach used with reinforcement learning needs to be robust to reality gaps between simulation and the real world.

Performance improvement of decentralized control for bidirectional converters in a DC micro-grid

In this paper, using a neural controller and a genetic optimization algorithm to control the voltage as well as, control the frequency of the grid along with the management of the reactive power of the micro-grid to control the output power during islanding using Simultaneous bilateral power converters with voltage/frequency droop strategy and optimization of PI coefficients of parallel power converters by genetic-neural micro-grid algorithm to suppress AC side-current flow that increases stability and improvement of conditions frequency and voltage are discussed. Given the performance of the micro-grid in two simulation scenarios, namely transition from on-grid to off-grid, the occurrence of a step change in load in island mode as well as return to working mode is connected. The ability to detect the robust performance and proper performance of two-level neural controller. The controller performance time was also very good, indicating the appropriate features of the method used to design the controller, namely two-level neural, genetics. The main advantage of this method is its simplicity of design. The method used is also efficient and resistant to changes in the system, which results from the simulations.

Feedback control method to suppress stick-slip in drill-strings featuring delay and actuation constraints

In this work, performance of a modified-integral resonant controller with integral tracking is investigated numerically under the effects of actuator delay and actuation constraints. Actuation delay and constraints naturally limit controller performance, so much so that it can cause instabilities. A 2-DOF drill-string m with nonlinear bit–rock interactions is analysed. The aforementioned control scheme is implemented on this system and analysed under the effects of actuation delay and constraints and it is found to be highly effective at coping with these limitations. The scheme is then compared to sliding-mode control and shows to be superior in many regimes of operation. Lastly, the scheme is analysed in detail by varying its gains as well as varying system parameters, most notably that of actuation delay.