Research Article  Open Access
Márcio Roberto Covacic, Marcelo Carvalho Minhoto Teixeira, Aparecido Augusto de Carvalho, Rodrigo Cardim, Edvaldo Assunção, Marcelo Augusto Assunção Sanches, Henrique Shuiti Fujimoto, Maxwell Simões Mineo, Anderson Ross Biazeto, Ruberlei Gaino, "Robust TS Fuzzy Control of Electrostimulation for Paraplegic Patients considering NormBounded Uncertainties", Mathematical Problems in Engineering, vol. 2020, Article ID 4624657, 28 pages, 2020. https://doi.org/10.1155/2020/4624657
Robust TS Fuzzy Control of Electrostimulation for Paraplegic Patients considering NormBounded Uncertainties
Abstract
This manuscript presents a Takagi–Sugeno fuzzy control for a mathematical model of the knee position of paraplegic patients using functional electrical stimulation (FES). Each local model of the fuzzy system is represented considering normbounded uncertainties. After obtaining the model of FES with normbounded uncertainties, the fuzzy control strategy is designed through the solution of linear matrix inequalities (LMIs) using the conditions available in the literature, which consider these normbounded uncertainties. The strategy considers decay rate and constraints on the input signal. The model is simulated in the Matlab environment using the numerical parameters measured by experimental tests from a paraplegic patient.
1. Introduction
Several researchers have used functional electrical stimulation (FES) to restore some motion activities of people with injured spinal cord [1]. However, FES is not yet a regular clinical method because the amount of effort involved in using actual stimulation systems still outweighs the functional benefits they provide. One serious problem of using FES is that artificially activated muscles fatigue at a faster rate than those activated by the natural physiological processes. Due to this problem, a considerable effort has been directed toward developing FES systems based on closedloop control. The movement is measured in real time with several types of sensors, and the stimulation pattern is modulated accordingly [1]. The dynamics of the lower limb is represented by a nonlinear secondorder model, which considers the gravitational and inertial characteristics of the anatomical segment as well as the damping and stiffness properties of the knee joint.
In this paper, we present a Takagi–Sugeno nonlinear system with the aim of controlling the position of the leg of a paraplegic patient. The controller was designed in order to change the angle of the knee joint from to when electrical stimulation is applied in the quadriceps muscle.
The authors considered the leg mathematical model proposed by Ferrarin and Pedotti [1], with the parameter values given in [2, 3]. The parameters (viscous coefficient), (inertial moment), (time constant), and (static gain) of the shankfoot complex model have the nominal values given in [2, 3], but with a 20% tolerance range around these nominal values, that is, these values are in the range between 80% and 120% of their nominal values. The minimum and the maximum values of the nonlinear term are computed considering the angle variation from to , that is, . The range of values of , , , and are considered as normbounded uncertainties, whose analysis requires a lower number of linear matrix inequalities (LMIs), compared to polytopic uncertainty analysis, obtaining a lower computational cost. For the case studied in this manuscript, with two local models and four uncertain parameters, the control design methods that consider polytopic uncertainty analysis require the solution of a set of 49 LMIs, while, for the normbounded uncertainty analysis, only 4 LMIs are required. In this paper, the proposal for the knee position control design of paraplegic patients with functional electrical stimulation (FES) considers that the parameters of the mathematical model of the system are uncertain, whose uncertainties are bounded in norm. To the authors’ knowledge, the Takagi–Sugeno (TS) fuzzy control considering normbounded uncertainties, applied to the knee joint movement of the paraplegic patient, was not published yet.
The simplest design technique to obtain a design model for nonlinear plants is its linearization at an interest point. However, this linearized design described is not adequate when the system operates far from the operation point. A possible solution for this problem is the nonlinear plant representation by TS fuzzy models, whose idea consists on the description of the nonlinear system as a combination of a certain number of local linear models. So, the global model is obtained by the fuzzy combination of these local linear models.
The TS fuzzy methodology can also be applied if the nonlinear model contains polytopic uncertainties, as in [4]. The representation of the uncertainties considers that the uncertain system structure varies according to a convex combination of some vertices that limit the polytope, where each vertex is described as a fuzzy combination of local linear models.
Fuzzy control theory is useful because the fuzzy systems can approximately represent real systems with a precision that can be specified by the designer. Furthermore, there are several types of models, suitable for different applications, since linguistic models for modeling a given system, even the TS models, whose structure is suitable for control applications. The systematic procedure to design fuzzy control systems involves the fuzzy model construction for nonlinear systems [5].
The parallel distributed compensation (PDC) [6] in fuzzy regulator design can be used to stabilize nonlinear systems described by fuzzy models. The idea is to design a compensator for each fuzzy rule. For each rule, there exists an associated controller. The resulting global fuzzy regulator, which is nonlinear in general, is a fuzzy combination of each individual linear regulator. The PDC offers a procedure to design a regulator for each TS fuzzy model, where each control rule is designed from the correspondent plant TS model rule.
An important fact that motivates the use of the representation of a broad class of nonlinear plants by Takagi–Sugeno fuzzy models for designing suitable controllers is that they usually allow a design procedure, with PDC approach, based on LMIs. LMIbased designs can offer conditions for the stability of the equilibrium point, and also, it is possible to specify other performance indexes, such as decay rate, constraints of the input and/or output, and the minimization of the cost, even for plants with uncertain parameters. Furthermore, the procedure for finding a feasible solution, when there exists one, becomes a convex optimization problem, and there exist easy methods such as polynomial time algorithms for solving this class of problem. With the aforementioned relevant facts, many researchers are using Takagi–Sugeno fuzzy models for obtaining adequate controllers for solving complex nonlinear control problems.
Muscle is a highly complex nonlinear system [7], capable of producing the same output for a variety of inputs. A property exploited by the physiologically activated muscle is its effort to minimize fatigue [8]. Considering that when the quadriceps is electrically stimulated, its response is nonlinear, we used TS fuzzy models in order to design a controller for the knee angle variation.
Ferrarin and Pedotti [1] showed that, for the conditions considered in their experiments, a simple onepole transfer function was able to model the relationship between stimulus pulse width and active muscle torque. The nonlinear term is analyzed in a TS fuzzy representation. So, the mathematical model of the functional electrical stimulation of the knee angle of the paraplegic patient is represented as a TS fuzzy combination of two local models, considering the minimum and the maximum values of the nonlinear term .
In [9], the nonlinear term is considered as the uncertain parameter, and the parameters , , , and of the shankfoot complex model have definite values. However, these parameters assume different values each day, depending on the health conditions of the patient at each moment. Fatigue can also change these values. So, these parameters have unknown values, but these can be considered in determinate ranges, bounded by the minimum and the maximum values of each parameter [10].
According to Santos et al. and Gaino [9, 11], the application of FES on the quadriceps muscle, more particularly on the motor neurons of a person, causes an involuntary contraction of the muscle of the leg, that is, causes an action potential (AP). FES is also known by neuromuscular electrical stimulation (NMES) [11].
In order to obtain the muscle contraction, the amplitude (or intensity) and duration of the electrical stimulus must be inside specific bounds. Then, the AP is generated and propagates in both directions of the nerve fiber [9]. Complex mechanisms of electrochemical stimuli occur in the neuromuscular structure causing the process of excitationcontraction coupling responsible for the movement of the leg [11]. The modulation of the force, by the number of muscle fibers recruited, and the speed of fiber recruitment depend on several parameters. Some of these parameters include the proximity of the nerve fiber and the electrode, the electrode diameter, and the variation of the number of active states of the fibers by the variation of the amplitude or pulse duration [9, 11]. As can be seen in Figure 1, the degree of muscle activation () is a nonlinear function that depends on the duration of the stimulus .
By the knowledge of the authors, scientific studies on the application of TS controllers to control the leg position of paraplegic patients are interesting, relevant, and challenging research topics. Recently, few articles have been published in this area, as, for instance, [12, 13]. In [11] and subsequent articles [2, 3, 14–17], published by our research group, some theories have been employed to control knee joint movement using neuromuscular electrical stimulation. In [18], an article was published describing control application in paraplegic patients.
The TS fuzzy strategy for control of the knee joint angle of a paraplegic position using LMIs and considering polytopic uncertainties was studied in [19], but other papers involving TS fuzzy control with polytopic uncertainties were not found in the literature. The TS fuzzy control using LMIs and considering normbounded uncertainties was not found in the literature. In [20], LMI stabilization conditions considering polytopic and normbounded uncertainties were presented for fractionalorder systems, but the method does not consider fuzzy models.
The list of the variables used in this manuscript is presented in Appendix.
2. General Takagi–Sugeno Fuzzy Representation
Certain classes of nonlinear systems can be exactly represented with TS fuzzy models, using the method described in [5]. According to this construction method, the local models are obtained in function of the operation region.
For , the th rule of the continuoustime TS fuzzy model is described as
In the fuzzy model (1), , is the fuzzy set of rule , and are the premise variables. Let be the membership function of the fuzzy set , and define
Since , one has, for ,
The resulting fuzzy model is the weighted mean of the local models, given bywhere
2.1. Regulators with Takagi–Sugeno Fuzzy Models
The PDC [6] offers a procedure to design a regulator for the TS fuzzy model, where each control rule is designed from the correspondent plant TS model rule. The designed fuzzy regulator shares the local controllers, each one given by
The fuzzy global regulator is given bywhere .
So, the closedloop system is composed as follows:
3. NormBounded Uncertainties
According to [21], a control system with normbounded uncertainties is described bywith , , and . Matrices , , and are composed by the nominal values of the uncertain parameters.
The uncertain matrices and can be represented aswhere
So, matrix is given bywithfor . Then, considering (13), one obtains equations that are given byfor and .
Similarly, matrix is given bywherefor and . Then, considering (16), one obtains equations that are given byfor , , and .
4. Knee Joint Model
The mathematical model of the leg employed in this manuscript was proposed in [1]. This model relates the applied pulse width to the torque generated on the knee joint. In the modeling [1], the leg was considered as an open kinematic system composed of two stiff segments: the thigh and the shinfoot complex, as shown in Figure 2. From [1], the equilibrium equation around the knee joint iswhere is the inertial moment of the shinfoot complex is the knee angle between the shin and the thigh on the sagittal plane is the knee angular velocity is the mass of the shinfoot complex is the gravitational acceleration is the distance between the knee and the shinfoot complex mass center is the viscous friction coefficient is the torque due to the stiffness component is the knee active torque generated by electrical stimulation
The stiffness moment is defined aswhere and are the coefficients of the exponential terms and is the elastic rest angle of the knee. In [1], it was observed that the torque applied to the muscle () and the electrical stimulation pulse width () can be adequately related by the transfer function described in (20), where and are positive constants obtained from parametric identification, when a stimulus is applied on the quadriceps and angular variation is read, as mentioned in [1]:
So, from [1], the knee joint mathematical model is given bywithwhere , , and are the knee angle, the knee angular velocity (), and the active torque at the operation point. The input is the pulse width of the electrical stimulation.
The nonlinear term is given bywhere
4.1. Local Fuzzy Models of the Knee Joint considering NormBounded Uncertainties
At the system described by (21), the uncertain parameter given in (24) belongs to the interval . Then, the number of rules is , that is, the TS fuzzy system has two local models. So, appropriate functions and are defined as
The nonlinear function is exactly represented by the convex combination of these two local models: [2, 3, 5, 11, 13]. Note that, from (26), , , and .
So, considering two local models, matrices and arewith described in (24) and (25), which belongs to the interval
Functions and are given by (17), and the matrices that describe the local models arewherewith
So,withknowing that
So,
Replacing (35)–(38) in and , one haswhere
So, matrices and are decomposed according to (10), where
Following (13), the elements of are functions of , , , and , given byfor , where are the elements of matrix in (43).
From (16), the elements of are also the function of , , , and , given byfor (), where are the elements of matrix in (44).
According to (14) and (17), the elements of , , and are solutions of the following equations:where , , , and are given in (45)–(48).
So, the solution of the set of equations (52) is
For simplicity, one can choose . Therefore, matrices , and arewith , and given in (45)–(48).
Similarly, replacing (39)–(42) in and , one haswhere
So, doing the same previous calculations, matrices and are decomposed according to (10), where
For simplicity, one can choose . Therefore, matrices and arewith , , , and given in (57)–(60).
5. Numerical Model of the Paraplegic Patient
The mathematical model of the system, which relates the pulse width applied for the muscle to the torque generated at the knee joint, is presented in Section 4. The parameters of the system, presented in [2, 3], were obtained experimentally. The parameters related to the shankfoot complex and their numerical values are given in Table 1.

The values of these parameters were experimentally measured, for a 45yearold paraplegic patient [2, 3]. However, several factors, such as changes of temperature, fatigue, and spasm, cause physiological changes on musculature that must be considered on process control. Furthermore, for other people, the physiological characteristics may be completely different since these characteristics depend on several factors, for instance, the age, weight, physical activities, and health conditions. So, calibration is needed before the beginning of the tests. The adjustments to perform a desired movement are made after the identification of each patient at a specific current and frequency.

The nonlinear term is described in (24) and (25). Considering the parameters of Table 1, knowing that = 9.8 m/s^{2} and taking the operation point angle as rad, these values are replaced in (25), obtaining N·m. For this operation point, the term belongs to the interval , where
The new input of the system, , is defined from the system input, , and is known as the unreferenced pulse width [11, 22–24]. It is given by
Since the input is the pulse width which is applied on the skin of the patient, its value must be positive, that is, . So,
5.1. NormBounded Uncertainties
From the parameters presented in Table 1, a ±20% variation is considered on the values of J, B, τ, and G, that is,
Given the minimum and the maximum values of , one has the system with two local models, each one described by (8), whereand matrices and are described by (10), wherewith , for , and matrices and are described by (10), wherewith , for .
6. System Control with NormBounded Uncertainties
For the TS fuzzy system given in (4), where each local model is described in (8), the objective is to obtain a fuzzy control law, given in (7), such that the controlled system is stable.
Theorem 1, given in [25], gives a sufficient condition for the stabilization of system (4) by the fuzzy control law (7), with the uncertain matrices described in (6).
Theorem 1 (see [25]). Consider continuoustime TS fuzzy system (4), with q local models, where each local model is described as in (9) and (10), that is, each matrix, and , , is decomposed as and , respectively, with , and .
So, continuoustime TS fuzzy system (4) is asymptotically stabilizable via the TS fuzzy modelbased statefeedback controller (7) if there exist a symmetric positive definite matrix, some matrices, and some scalars