Model-based development of neuroprosthesis for paraplegic patients.

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to the muscles is interrupted. Functional electrical stimulation applied to the lower motor neurons can replace the lacking signals. A so-called neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of functional electrical stimulation. However, the control of multiple joints is difficult due to the complexity, nonlinearity, and time-variance of the system involved. Furthermore, effects such as muscle fatigue, spasticity, and limited force in the stimulated muscle further complicate the control task. Mathematical models of the human musculoskeletal system can support the development of neuroprosthesis. In this article a detailed overview of the existing work in the literature is given and two examples developed by the author are presented that give an insight into model-based development of neuroprosthesis for paraplegic patients. It is shown that modelling the musculoskeletal system can provide better understanding of muscular force production and movement coordination principles. Models can also be used to design and test stimulation patterns and feedback control strategies. Additionally, model components can be implemented in a controller to improve control performance. Eventually, the use of musculoskeletal models for neuroprosthesis design may help to avoid internal disturbances such as fatigue and optimize muscular force output. Furthermore, better controller quality can be obtained than in previous empirical approaches. In addition, the number of experimental tests to be performed with human subjects can be reduced. It is concluded that mathematical models play an increasing role in the development of reliable closed-loop controlled, lower extremity neuroprostheses.     

Feedforward-Feedback Hybrid Control for Magnetic Shape Memory Alloy Actuators Based on the Krasnosel'skii-Pokrovskii Model

As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel''skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.     

Fuzzy control of ethanol concentration its application to maximum glutathione production in yeast fed-batch culture

A fuzzy logic controller (FLC) for the control of ethanol concentration was developed and utilized to realize the maximum production of glutathione (GSH) in yeast fedbatch culture. A conventional fuzzy controller, which uses the control error and its rate of change in the premise part of the linguistic rules, worked well when the initial error of ethanol concentration was small. However, when the initial error was large, controller overreaction resulted in an overshoot.An improved fuzzy controller was obtained to avoid controller overreaction by diagnostic determination of "glucose emergency states" (i.e., glucose accumulation or deficiency), and then appropriate emergency control action was obtained by the use of weight coefficients and modification of linguistic rules to decrease the overreaction of the controller when the fermentation was in the emergency state. The improved fuzzy controller was able to control a constant ethanol concentration under conditions of large initial error.The improved fuzzy control system was used in the GSH production phase of the optimal operation to indirectly control the specific growth rate mu to its critical value mu(c). In the GSH production phase of the fed-batch culture, the optimal solution was to control mu to mu(c) in order to maintain a maximum specific GSH production rate. The value of mu(c) also coincided with the critical specific growth rate at which no ethanol formation occurs. Therefore, the control of mu to mu(c) could be done indirectly by maintaining a constant ethanol concentration, that is, zero net ethanol formation, through proper manipulation of the glucose feed rate. Maximum production of GSH was realized using the developed FLC; maximum production was a consequence of the substrate feeding strategy and cysteine addition, and the FLC was a simple way to realize the strategy. (c) 1993 John Wiley & Sons, Inc.     

Application of a fuzzy logic control system for continuous anaerobic digestion of low buffered, acidic energy crops as mono-substrate

A fuzzy logic control (FLC) system was developed at the Hamburg University of Applied Sciences (HAW Hamburg) for operation of biogas reactors running on energy crops. Three commercially available measuring parameters, namely pH, the methane (CH4) content, and the specific gas production rate (spec. GPR = m(3)/kg VS/day) were included. The objective was to avoid stabilization of pH with use of buffering supplements, like lime or manure. The developed FLC system can cover most of all applications, such as a careful start-up process and a gentle recovery strategy after a severe reactor failure, also enabling a process with a high organic loading rate (OLR) and a low hydraulic retention time (HRT), that is, a high throughput anaerobic digestion process with a stable pH and CH4 content. A precondition for a high load process was the concept of interval feeding, for example, with 8 h of interval. The FLC system was proved to be reliable during the long term fermentation studies over 3 years in one-stage, completely stirred tank reactors (CSTR) with acidic beet silage as mono-input (pH 3.3-3.4). During fermentation of the fodder beet silage (FBS), a stable HRT of 6.0 days with an OLR of up to 15 kg VS/m(3)/day and a volumetric GPR of 9 m(3)/m(3)/day could be reached. The FLC enabled an automatic recovery of the digester after two induced severe reactor failures. In another attempt to prove the feasibility of the FLC, substrate FBS was changed to sugar beet silage (SBS), which had a substantially lower buffering capacity than that of the FBS. With SBS, the FLC accomplished a stable fermentation at a pH level between 6.5 and 6.6, and a volatile fatty acid level (VFA) below 500 mg/L, but the FLC had to interact and to change the substrate dosage permanently. In a further experiment, the reactor temperature was increased from 41 to 50 degrees C. Concomitantly, the specific GPR, pH and CH4 dropped down. Finally, the FLC automatically enabled a complete recovery in 16 days.     

Fuzzy logic control of an unstable bioreactor

A rule based fuzzy controller (FLC) is developed for stabilization of an unstable continuous stirred tank bioreactor (CSTBR) from various start-up conditions. The output variable is the reactor substrate concentration and the manipulated variable is the dilution rate. The performance of the FLC is evaluated by simulating a mathematical model of an unstable CSTBR. FLC is robust to perturbations in the specific growth rate, specific consumption rate and also to a disturbance in the feed substrate concentration. The performance of the FLC is superior to that of a conventional proportional controller.     

Improved single- and multi-contact life-time testing of dental restorative materials using key characteristics of the human masticatory system and a force/position-controlled robotic dental wear simulator

This paper presents a new in vitro wear simulator based on spatial parallel kinematics and a biologically inspired implicit force/position hybrid controller to replicate chewing movements and dental wear formations on dental components, such as crowns, bridges or a full set of teeth. The human mandible, guided by passive structures such as posterior teeth and the two temporomandibular joints, moves with up to 6 degrees of freedom (DOF) in Cartesian space. The currently available wear simulators lack the ability to perform these chewing movements. In many cases, their lack of sufficient DOF enables them only to replicate the sliding motion of a single occlusal contact point by neglecting rotational movements and the motion along one Cartesian axis. The motion and forces of more than one occlusal contact points cannot accurately be replicated by these instruments. Furthermore, the majority of wear simulators are unable to control simultaneously the main wear-affecting parameters, considering abrasive mechanical wear, which are the occlusal sliding motion and bite forces in the constraint contact phase of the human chewing cycle. It has been shown that such discrepancies between the true in vivo and the simulated in vitro condition influence the outcome and the quality of wear studies. This can be improved by implementing biological features of the human masticatory system such as tooth compliance realized through the passive action of the periodontal ligament and active bite force control realized though the central nervous system using feedback from periodontal preceptors. The simulator described in this paper can be used for single- and multi-occlusal contact testing due to its kinematics and ability to exactly replicate human translational and rotational mandibular movements with up to 6 DOF without neglecting movements along or around the three Cartesian axes. Recorded human mandibular motion and occlusal force data are the reference inputs of the simulator. Experimental studies of wear using this simulator demonstrate that integrating the biological feature of combined force/position hybrid control in dental material testing improves the linearity and reduces the variability of results. In addition, it has been shown that present biaxially operated dental wear simulators are likely to provide misleading results in comparative in vitro/in vivo one-contact studies due to neglecting the occlusal sliding motion in one plane which could introduce an error of up to 49% since occlusal sliding motion D and volumetric wear loss V(loss) are proportional.     

The study of neural network-based controller for controlling dissolved oxygen concentration in a sequencing batch reactor

The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) level of an activated sludge system by manipulating the air flow rate. The NN inverse model-based controller with the model-based scheme represents the controller, which relies solely upon the simple NN inverse model. In the IMC, both the forward and inverse models are used directly as elements within the feedback loop. The hybrid NN control consists of a basic NN controller in parallel with a proportional integral (PI) controller. Various simulation tests involving multiple set-point changes, disturbances rejection and noise effects were performed to review the performances of these various controllers. From the results it can be seen that hybrid controller gives the best results in tracking set-point changes under disturbances and noise effects.     

A computational simulated control system for a high-force pneumatic muscle actuator: system definition and application as an augmented orthosis

High-force pneumatic muscle actuators (PMAs) are used for force assistance with minimal displacement applications. However, poor control due to dynamic nonlinearities has limited PMA applications. A simulated control system is developed consisting of: (1) a controller relating an input position angle to an output proportional pressure regulator voltage, (2) a phenomenological model of the PMA with an internal dynamic force loop (system time constant information), (3) a physical model of a human sit-to-stand task and (4) an external position angle feed-back loop. The results indicate that PMA assistance regarding the human sit-to-stand task is feasible within a specified PMA operational pressure range.     

A computational simulated control system for a high-force pneumatic muscle actuator: system definition and application as an augmented orthosis

High-force pneumatic muscle actuators (PMAs) are used for force assistance with minimal displacement applications. However, poor control due to dynamic nonlinearities has limited PMA applications. A simulated control system is developed consisting of: (1) a controller relating an input position angle to an output proportional pressure regulator voltage, (2) a phenomenological model of the PMA with an internal dynamic force loop (system time constant information), (3) a physical model of a human sit-to-stand task and (4) an external position angle feed-back loop. The results indicate that PMA assistance regarding the human sit-to-stand task is feasible within a specified PMA operational pressure range.     

Analysis of control strategies for VIVA OpenHBM with active reflexive neck muscles

Modeling muscle activity in the neck muscles of a finite element (FE) human body model can be based on two biological reflex systems. One approach is to approximate the Vestibulocollic reflex (VCR) function, which maintains the head orientation relative to a fixed reference in space. The second system tries to maintain the head posture relative to the torso, similar to the Cervicocolic reflex (CCR). Strategies to combine these two neck muscle controller approaches in a single head-neck FE model were tested, optimized, and compared to rear-impact volunteer data. The first approach, Combined-Control, assumed that both controllers simultaneously controlled all neck muscle activations. In the second approach, Distributed-Control, one controller was used to regulate activation of the superficial muscles while a different controller acted on deep neck muscles. The results showed that any muscle controller that combined the two approaches was less effective than only using one of VCR- or CCR-based systems on its own. A passive model had the best objective rating for cervical spine kinematics, but the addition of a single active controller provided the best response for both head and cervical spine kinematics. The present study demonstrates the difficulty in completely capturing representative head and cervical spine responses to rear-impact loading and identified a controller capturing the VCR reflex as the best candidate to investigate whiplash injury mechanisms through FE modeling.

     

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to ‘re-tune’ the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.     

Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments

In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.     

Frequency response of the human controller for various processes and stochastic testsignals

The adaptive behaviour of the human controller in a closed loop system with stochastic testsignals is investigated. For several typical simulated processes functional models for the human controller are derived. These models describe the adaptability of the human controller to processes which are free of dead-time, assuming simple control tasks. Finally the results for processes with dead-time will be discussed.The investigations were done on a hybrid computer of the Deutsche Forschungsgemeinschaft     

Hybrid control strategies for a five-finger robotic hand

This paper presents a hybrid controller of soft control techniques, adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL), and hard control technique, proportional-derivative (PD), for a five-finger robotic hand with 14-degrees-of-freedom (DoF). The ANFIS is used for inverse kinematics of three-link fingers and FL is used for tuning the PD parameters with 2 input layers (error and error rate) using 7 triangular membership functions and 49 fuzzy logic rules. Simulation results with the hybrid of FL-tuned PD controller exhibit superior performance compared to PD, PID and FL controllers alone.     

Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises

During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.     

A physiological basis for control of a prosthetic hand

Recent surveys from upper limb amputees indicate the sentiment that prosthetic hands do not function in a life-like manner and are not intuitively controlled. Thus, two methods of control for a prosthetic hand are presented. A proportional derivative (PD) force controller is compared to a novel biomimetic application of sliding mode control. The biomimetic sliding mode (BSM) controller was designed to map human muscle signals into prosthesis motor command signals in a physiologically expected manner.The BSM and PD controllers were evaluated analytically and subjectively by one amputee and nine nonamputee test subjects. The posture of the hands of the nonamputee test subjects were measured with a CyberGlove and used to determine if the position of the prosthesis (when driven by both controllers) was highly correlated to the posture of the human hands. Force tracking experiments were also performed by all test subjects with both controllers to evaluate the ability to control the applied force. Finally, a dual object lifting task was performed by all test subjects to determine if the mapping of electromyogram (EMG) signals with the BSM controller resulted in physiologically expected motions. A nonparametric Mann–Whitney U-test was performed on the subjective evaluations to determine the statistical significance of the evaluations.The BSM controller was shown to replicate the posture of the human hand much more accurately than the PD force controller. The BSM controller also enabled better average force tracking results and higher success rates with the dual object lifting experiment while the same task was nearly impossible to perform with the PD controller. Finally, the BSM controller was subjectively rated to be more similar to control in comparison to the human hand with respect to position and force.     

Control of constraint forces and trajectories in a rich sensory and actuation environment

A simple control strategy is proposed and applied to a class of non-linear systems that have abundant sensory and actuation channels as in living systems. The main objective is the independent control of constrained trajectories of motion, and control of the corresponding constraint forces. The peripheral controller is a proportional, derivative and integral (PID) controller. A central controller produces, via pattern generators, reference signals that are the desired constrained position and velocity trajectories, and the desired constraint forces. The basic tenet of the this hybrid control strategy is the use of two mechanisms: 1. linear state and force feedback, and 2. non-linear constraint velocity feedback - sliding mode feedback. The first mechanism can be envisioned as a high gain feedback systems. The high gain attribute imitates the agonist-antagonist co-activation in natural systems. The strategy is applied to the control of the force and trajectory of a two-segment thigh-leg planar biped leg with a mass-less foot cranking a pedal that is analogous to a bicycle pedal. Five computational experiments are presented to show the effectiveness of the strategy and the performance of the controller. The findings of this paper are applicable to the design of orthoses and prostheses to supplement functional electrical stimulation for support purposes in the spinally injured cases.