Recently, an adaptive control approach has been proposed. This approach, named adaptive control, involves the insertion of a low-pass filter at the input of the Model Reference Adaptive Control (MRAC). This controller has been designed to overcome several limitations of classical adaptive controllers such as (i) the initialization of estimated parameters, (ii) the stability problems with high adaptation gains, and (iii) the appropriate parameter excitation. In this paper, a new design of the filter is presented, used for adaptive control, for which the desired performances are guaranteed (appropriate values of the control during start-up, a high filtering of noises, a reduced time lag, and a reduced energy consumption). Parameters of the new proposed filter have been optimised by genetic algorithms. The proposed adaptive fractional control is applied to a polyarticulated robotic system. Simulation results show the efficiency of the proposed control approach with respect to the classical adaptive control in the nominal case and in the presence of a multiplicative noise.

Fractional order Controllers have been used in several industrial cases to achieve better performance of the systems. This paper proposes a Fractional Order Proportional Integral Derivative (FOPID) controller. It is synthesized using Oustaloup approximation, and its parameters are tuned using the Genetic Algorithm (GA) optimization method. The aim is to minimize the error, the energy and the startup torques using two objective functions to improve the control performances and the robustness. The validity of the proposed controller is shown via simulation by controlling a two-link exoskeleton for children's gait rehabilitation, and the results are compared to an Integer order PID (IOPID) controller. Simulation results clearly indicate the superiority of the optimized FOPID in terms of trajectory tracking and the used torques. Moreover, the FOPID controller is tested with parameter uncertainties. Its robustness is proven against thigh and shank masses variation. Both controllers are simulated under the same frequency conditions using Simulink MATLAB R2018a.

Rehabilitation robots, particularly lower-limb exoskeletons, are transforming healthcare by assisting individuals with mobility impairments. This study introduces a novel Sliding Mode Control (SMC) system based on a fractional-order reaching law, designed to enhance control performance and robustness. The proposed approach effectively manages the exoskeleton’s dynamic behavior, particularly during the transient regime, by reducing initial torque energy demand during start-up, ensuring precise trajectory tracking, and prioritizing patient safety and comfort. The method’s effectiveness is validated through MATLAB simulations and supported by a rigorous dual stability analysis, demonstrating asymptotic and finite-time convergence of the system in the reaching and sliding phases. A Comparison study with traditional SMC techniques proves that the FO-RL-SMC significantly improves energy efficiency during the transient phase and the overall dynamical behavior of the system. These results highlight the potential of the proposed FO-RL-SMC system to advance the performance of rehabilitation robots, emphasizing its value in addressing complex control challenges and improving patient outcomes.

This study proposes an approach to synthesize a three-impulse sequence input shaper with a negative impulse, known as Unity Magnitude (UM) shaper. The corresponding analytic model has been already achieved for undamped and low-damped systems. In this paper, the analytic design of UM shaper is demonstrated for the generalized case of damped systems for both types: integer and fractional orders. Hence, the UM shaper model has been designed for second-order systems with damped dynamics, associating a graphical fitting and an analytical procedure; then, it has been extended to explicit fractional derivative systems. Moreover, the feasibility and the effectiveness of the proposed on-off profile prefilter applied on a second-generation controller have been substantiated by experimental results on an instrumented DC motor bench.