Robust Tracking Control of Wheeled Mobile Robot Based on Differential Flatness and Sliding Active Disturbance Rejection Control: Simulations and Experiments.

This paper proposes a robust tracking control method for wheeled mobile robot (WMR) against uncertainties, including wind disturbances and slipping. Through the application of the differential flatness methodology, the under-actuated WMR model is transformed into a linear canonical form, simplifying the design of a stabilizing feedback controller. To handle uncertainties from wheel slip and wind disturbances, the proposed feedback controller uses sliding mode control (SMC). However, increased uncertainties lead to chattering in the SMC approach due to higher control inputs. To mitigate this, a boundary layer around the switching surface is introduced, implementing a continuous control law to reduce chattering. Although increasing the boundary layer thickness reduces chattering, it may compromise the robustness achieved by SMC. To address this challenge, an active disturbance rejection control (ADRC) is integrated with boundary layer sliding mode control. ADRC estimates lumped uncertainties via an extended state observer and eliminates them within the feedback loop. This combined feedback control method aims to achieve practical control and robust tracking performance. Stability properties of the closed-loop system are established using the Lyapunov theory. Finally, simulations and experimental results are conducted to compare and evaluate the efficiency of the proposed robust tracking controller against other existing control methods.

Model Predictive Control with Integrated Model Reduction for a Continuous Lactide Ring-Opening Polymerization Process.

Poly(lactic acid) production has received increasing attention, mainly due to its inherent biodegradable thermoplastic properties and to its renewable-resource-based composition. This process is affected by changes in the operating conditions and by raw material impurities which influence the reaction rate and degrade the polymer properties. As the system model is multivariable with coupled dynamics and constraints, linear model predictive control (LMPC) is employed here. A model reduction technique is proposed to obtain an approximate linear representation of the nonlinear system around the operating point to minimize the calculation cost of the controller. The proposed LMPC approach is validated by simulation and is compared to a proportional-integral controller and a nonlinear model predictive control. It is found that LMPC has a superior performance in terms of off-spec time when a disturbance occurs in the feed, and it can restore the target conditions better and faster.

A New Mathematical Force Model that Predicts the Force-pulse Amplitude Relationship of Human Skeletal Muscle.

Current functional electrical stimulation (FES) systems vary the stimulation intensity to control the muscle force in order to produce precise functional movements. However, mathematical model that predicts the intensity effect on the muscle force is required for model-based controller design. The most previous force model designed by Ding et al was validated only for a standardized stimulation pulse amplitude (intensity). Thus, the purpose of this study was to adapt the Ding et al model to be able to predict the force-pulse amplitude relationship. The experimental results tested on quadriceps femoris muscles of healthy subjects (N=5) show that our adapted model accurately predicts the force response for trains of a wide range of stimulation intensities (30-100 mA). The accurate predictions indicate that our adapted model could be used for designing model-based control strategies to control the muscle force through FES.

An approach to a muscle force model with force-pulse amplitude relationship of human quadriceps muscles.

BACKGROUND: Recent advanced applications of the functional electrical stimulation (FES) mostly used closed-loop control strategies based on mathematical models to improve the performance of the FES systems. In most of them, the pulse amplitude was used as an input control. However, in controlling the muscle force, the most popular force model developed by Ding et al. does not take into account the pulse amplitude effect. The purpose of this study was to include the pulse amplitude in the existing Ding et al. model based on the recruitment curve function. METHODS: Quadriceps femoris muscles of eight healthy subjects were tested. Forces responses to stimulation trains with different pulse amplitudes (30-100 mA) and frequencies (20-80 Hz) were recorded and analyzed. Then, specific model parameter values were identified by fitting the measured forces for one train (50 Hz, 100 mA). The obtained model parameters were then used to identify the recruitment curve parameter values by fitting the force responses for different pulse amplitudes at the same frequency train. Finally, the extended model was used to predict force responses for a range of stimulation pulse amplitudes and frequencies. RESULTS: The experimental results indicated that our adapted model accurately predicts the force-pulse amplitude relationship with an excellent agreement between measured and predicted forces (R2=0.998, RMSE = 6.6 N). CONCLUSIONS: This model could be used to predict the pulse amplitude effect and to design control strategies for controlling the muscle force in order to obtain precise movements during FES sessions using intensity modulation.

Truncation effects on muscular fatigue indexes based on M waves analysis.

In this paper, we investigate muscular fatigue. We propose a new fatigue index based on the continuous wavelet transform (CWT) and compare it with the standard fatigue indexes from literature. Fatigue indexes are all based on the electrical activity of muscles (electromyogram) acquired during an electrically stimulated contraction (ES). The stimulator and electromyogram system, which were presented in a previous work, allows real-time analysis. The extracted fatigue parameters are compared between each other and their sensitivity to noise is studied. The effect of truncation of M waves is then investigated, enlightening the robustness of the index obtained using CWT.