This paper proposes an approach to the tuning of model-free controllers for the midcarpal joint angles, which are important finger angles that ensure the desired finger dynamics in prosthetic hand myoelectric-based control systems. The process in these control systems is characterized by fuzzy models that operate with myoelectric signals obtained from eight myoelectric sensors and past inputs and/or outputs, where the controlled outputs are five finger angles. Since the fuzzy models exhibit very good performance as shown in authors’ recent papers that produced evolving fuzzy models, they are used to simulate the process behaviour. The Multi Input-Multi Output (MIMO) control system structure consists of five separate Single Input-Single Output control loops with the most simple model-free controllers represented by intelligent Proportional (iP) controllers, separately designed and tuned for each finger. Digital simulation results are included to suggestively illustrate the very good performance of the control systems with iP controllers. The MIMO control system performance is compared with that of the same system but with Proportional-Integral controllers, which are optimally tuned in a model-based manner by a metaheuristic Grey Wolf Optimizer (GWO) algorithm. The fair comparison is ensured by the optimal tuning of the free parameters of iP controllers in a model-based manner using GWO.

This paper proposes the data-driven tuning of low-cost Proportional-Integral (PI) fuzzy control of the payload position of tower crane systems using Iterative Feedback Tuning (IFT). A back-calculation and tracking anti-windup diagram is included to prohibit integrator windup and compensate for the process's dead-zone and saturation nonlinearity. A two-stage tuning strategy is proposed. In the first stage, the parameters of the fuzzy logic part of the PI fuzzy controller and the anti-windup tracking gain are optimally tuned in a model-based manner using an original version of the hybrid Particle Filter-Particle Swarm Optimization algorithm, improved by adding an information feedback model. In the second stage, the parameters of the linear part of the PI fuzzy controller are tuned in a data-driven manner using IFT, and then mapped to the remaining PI fuzzy controller parameters using the modal equivalence principle. The efficacy of the new data-driven fuzzy control strategy is demonstrated through experimental results, and the comparison shows the performance improvement over other strategies designed using competing optimization algorithms.