The damping characteristic of a healthy limb changes throughout the gait cycle. However, for amputees who are wearing mechanically passive damping prosthesis, the lack of ability to change the damping values might expose them to injuries and health problems. The use of magnetorheological fluid damper in prosthetic limb, which provides wide dynamic range, seems to be able to prevent these conditions from happening, due to its response to the magnetic field. The magnetorheological fluid, a type of smart material that is capable of altering its rheological property, changes its viscosity subjected to the intensity of the external magnetic field. Thus, due to this property, magnetorheological fluid damper covers the advantages of both passive and active dampers. This work explores the implementation of magnetorheological fluid damper in transtibial (below knee) prosthetic limb utilizing adaptive control techniques via simulation studies. An experimental study was done to observe the relationship of the force generated by the damper to the applied current. In addition, fuzzy-proportional-integral-derivative controller was implemented to ensure that the damper performs well, even at varying frequencies.

Walking behaviour in amputees with lower-limb loss is absent from shock-absorbing properties. A damper can be used to reduce the impact of ground reaction force (GRF) during heel strikes. Magnetorheological fluid (MRF) damper is deemed the best option for this application as it includes the advantages of both passive and active dampers. An enhanced MRF damper is essential in supplying the appropriate current and damping force levels. Therefore, an energy-efficient design is required to prolong the battery life used by MRF dampers in prosthetic limbs. This paper investigates two fluids of different properties and magnetic particle volume content. A bypass damper was used to observe the response of both fluids. The findings highlighted that an MRF with a higher percentage of solid weight could produce a more significant damping force with a lesser amount of applied current. This work presents a simulation study on implementing the energy-efficient MRF damper utilizing a Fuzzy-PID controller in a prosthetic limb.

The nonlinearity behaviour of magnetorheological fluid (MRF) can be described using a number of established models such as Bingham and Modified Bouc-Wen models. Since these models require the identification of model parameters, there is a need to estimate the parameters' value carefully. In this paper, an optimization algorithm, i.e., the Particle Swarm Optimization (PSO) algorithm, is utilized to identify the models' parameters. The PSO algorithm distinctively controls the best fit value by minimizing marginal error through root-mean-square error between the models and the empirical response. The validation of the algorithm is attained by comparing the resulting modified Bouc-Wen model behaviour using PSO against the same model's behaviour, identified using Genetic Algorithm (GA). The validation results indicate that the application of PSO is better in identifying the model parameters. Results from this estimation can be used to design a controller for various applications such as prosthetic limbs.

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Magnetorheological fluids (MRF) are considered as smart material since the behavior of the fluid is interrelated with external magnetic field. The rheological behavior of the fluids is changed whenever the strength of magnetic field changed. One of the applications of MRF is Magnetorheological damper (MRD), a damper that can be controlled by magnetic field. Analytical model of the damper is important in order to understand the performance of MRD. Different types of model that depend on defined parameters were introduced by previous researchers, and investigation on those parameters is crucial to acquire a model with least value of error. A technique called nonlinear least squares fitting method is utilized in order to obtain those parameters. This study investigated five different MRD models and presents the result for parameter estimation for each model as a function of input current.