In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speeds. Moreover, the investigation shows that the algorithm can be calculated in real time by an embedded system, allowing for easy implementation on real prosthetic knees.

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The paper is concerned with a stable robust adaptive scheme for sem-iactive control of suspension system installed with MR damper, which can deal with uncertainties in both models of MR damper and suspension mechanism. The proposed scheme consists of two main adaptive controllers: One is an adaptive inverse control for compensating the nonlinear hysteresis dynamics of the MR damper, which can be realized by identifying a forward model of MR damper or by directly adjusting an inverse model of MR damper. The other is an adaptive reference control which gives the desired damping force to match the seat dynamics to a specified reference dynamics, which can also be designed by taking into account the passivity of the MR damper. The stability of the total system including the two adaptive controllers is discussed and its stability condition is explored. Validity of the proposed algorithm is also examined in simulation studies.

An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption.

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