Research Article  Open Access
Sergio AlvarezRodríguez, Francisco Gerardo Peña Lecona, "th Order Sensor Output to Control DoF Serial Robot Arms", Journal of Sensors, vol. 2021, Article ID 8884282, 14 pages, 2021. https://doi.org/10.1155/2021/8884282
th Order Sensor Output to Control DoF Serial Robot Arms
Abstract
Currently, zeroorder sensors are commonly used as positioning feedback for the closedloop control in robotics; thus, in order to expand robots’ control alternatives, other paths in sensing should be investigated more deeply. Conditions under which the th order sensor output can be used to control DoF serial robot arms are formally studied in this work. In obtaining the mentioned control conditions, the PickardLindeloff theorem has been used to prove the existence and uniqueness of the robot’s mathematical model solution with order sensory systems included. To verify that the given conditions and claims guarantee controllability for both continuousbased and variable structurebased systems, two types of control strategies are used in obtaining simulation results: the conventional PID control and a secondorder Sliding Mode control.
1. Introduction
Currently, sensors of order zero are the most extensively used positioning detectors in closedloop control systems in industrial robotics. As a matter of fact, nowadays, robotic systems nearly exclusively use incremental and absolute encoders, resolvers, and high precision potentiometers. This is due to the fact that robotic controllers require delayfree information to achieve the desired control goals: accuracy, precision, repetitiveness, avoidance of tracking error, reduction of speed error, and so on.
An extended paradigm considers that positioning sensors of higher order than zero are not reliable at all. This consideration demotivates engineers for designing sensors of order . Nevertheless, the authors of this work consider that the theme should be sufficiently investigated to eliminate restrictive considerations which could lead in the future, to improve sensor systems in robotics.
In this work, the performance of a DoF serial robot arm with dynamical inclusion of linear order sensors is investigated, showing that robot’s properties with linear order sensors inclusion are invariant with respect to robot’s theoretical dynamics, provided that the solutions of the considered linear order sensors exist and are unique.
Interest in studying the use of higherorder sensor devices in the robotic industry has increased due to the need to implement more efficient, faster, and optimal control systems in both types of sensor systems concentrated in a robotic arm or systems with distributed sensors connected in a network and which may also be subject to adverse environmental conditions. However, the use of higherorder sensor systems inevitably introduces time delays in robotic control systems, including those delays from the controller to the actuator and the delays from the sensor to the controller.
On the other hand, some control methodologies have been proposed, ranging from sliding mode and adaptive techniques to manage uncertainties related to the knowledge of external dynamics and disturbances [1, 2]. However, these approaches always work well in lagfree environments.
To try to compensate for these delays, low pass filterbased mechanisms have been implemented to obtain highquality signals and be able to build control loops for control mechanisms [3].
Mathematical scaffolding to ensure unique solutions in robotic device control algorithms including higherorder sensor systems is not widely studied.
Even more, alternative solutions to deal with nonlinear systems with unmeasurable states have been proposed, in [4] where an adaptive fuzzy outputfeedback control scheme is designed and in [5] where a fuzzy logic systembased switched observer is constructed to approximate the unmeasurable states.
1.1. Motivation
Sensors can be designed using a very wide variety of physical principles (e.g., inductive, capacitive); however, many phenomena must be modelled using the order differential equations, making them unreliable in the world of robotics.
Motivated by the fact that currently sensory systems of order are not included either in industrial or in other areas of robotics, the authors are interested in expanding the study of the control of serial mechanisms using sensors that may have the potential to improve in some way the current ones.
As such, the first question is: under which conditions a serial robot mechanism can be controlled by positioning sensors of order different from zero?
If this first question is satisfactorily solved, a second one comes to mind: how can we take advantage of the sensor output for control purposes?
1.2. Contribution of This Work
The main contribution of this work is to study conditions to make possible the control of serial robot arms, taking as the feedback for the closedloop control the output of the th order sensory systems. Also, a technique to obtain the mentioned feedback signal from the sensor’s output is proposed.
As formal proofs for claims presented in the text are provided, the methodology of this work is rigorous.
As a proof of concept, an application example is focused on the trajectory tracking control problem (for simplicity); however, using the first time derivative of the position, the robot’s joint speed control follows the same rules given herein.
1.3. Problem Statement
Figure 1 shows a block control diagram with a negative feedback summing a reference signal (Ref) to get the tracking error () as the input for the control block which is the controller for the system RobotSensors. In Figure 1, is the control torque, represents the actual robot’s position identified by (i.e., ), while represents the sensor’s output. If sensors are of order zero, i.e., , where matrix includes the sensor’s parameters of sensitiveness, the feedback of states can be taken directly from the sensor’s output. Nevertheless, sensors of order can be designed and/or included to control robotic systems. In such a case, the problem consists of recovering from the information provided by sensors, using a processing method for this task. In the following section, this problem is addressed formally.
The remainder of the document is organized as follows: in Section 2, conditions and assumptions to control serial robots using the output of order sensory systems are given, and formal proofs for the given claims are presented; in Section 3 an implementation example for a 3DoF serial robot arm is performed, along with simulation results; and finally, concluding remarks are given in Section 4.
2. Robot’s Model with Sensor System Inclusion
Let us consider the dynamic differential equation where with represents the functional diagonal matrices, i.e., each element of denoted as for all ; is the sensor’s response vector; is the vector for the actual positions of robot joints; represents a position and/or time dependent possible function; and is the number of DoF of the robot.
For the case of linear sensors, (1) becomes
Now, let us consider the following nonlinear robot dynamics where vector represents the states of the electromechanical plant, i.e., ; vector is a time and/or statespossible dependent function; and is the initial condition value at .
The inclusion of (2) in (3) is studied in this work, in order to obtain control conditions, according to the following claim:
Claim 1. It is possible the control of serial robots using positioning sensors of the th order for the closedloop feedback of the states if the mathematical structure of the solution of the differential equations that model sensor’s behaviour (2) exists and is unique for all , under the following assumptions:
Assumption 1. Sensors provide the dynamic solution of (2) for all , i.e., it is assumed that the value for the signal is available to be used at time by the closedloop control system.
Assumption 2. It is assumed that the diagonal matrices with in (2) are known.
2.1. Existence and Uniqueness
Herein, the existence and uniqueness of the solution for the robot’s dynamics are verified for the case in which the solution of the sensor’s dynamics exists and is unique.
Definition 1 (Lipschitz). The function is locally Lipschitz if it is continuous, and for all subset compact exists such that if and , then, where is called the Lipschitz constant. (See [6] among many others.)
Theorem 1 (PicardLindeloff approach). For function locally Lipschitz, and also for the set let be a bound of in , the Lipschitz constant of in , and . Then, problem (6) with initial conditions has a solution, and it is unique in the interval (in the PicardLindeloff approach). where , , and and are the initial values for position and velocity, respectively, at .
Proof. It is assumed that exists, then, also the set exists, where is a Banach space with uniform norm and is closed in .
According to (2) and control concept expressed in Figure 1, the identification process of is where is the result of processing and is an identification error given by the Euclidian norm . Observe that , for simplicity and , then, , and the problem is reduced to the existence of the set as the norm is bounded by .
Let us define such that the image of the function is defined by where we accomplished both
Then, is a contraction defined in a closed subset of a complete metric space, which implies that there exists a unique such that , according to the fixedpoint theorem of Banach.
Thus, exists and is a unique solution of (6) for all .
2.2. Dynamics Invariance
The validity of Claim 1 is conditioned to result in the transformation of the robot’s dynamics when the sensor’s dynamics are included. To solve this problem, the following theorem is presented:
Theorem 2. Dynamic properties of which are the dynamic robot’s model with actual output , remain invariant with respect to the transformed form which is the robot with the th order sensor inclusion, where is the torque vector, is the inertia matrix, matrix represents the Coriolis and centripetal enforces, is the gravitational vector, and , can be obtained by any parametric identification method, and each of them is the sum of viscous, Coulomb, and static friction forces.
The transformation method is given by the sensor inclusion in the robot’s joints, represented by which are the sensor’s gain matrices, the articular positions, and the articular velocities, respectively.
Proof. The proof starts with , but the intermediate results are given to make evident it is the extension to , and by induction conclude generalization to (13)–(15), i.e., generalization for all .
Let us consider a serial 3DoF anthropomorphic robot arm as defined in [7] and as illustrated by Figure 2 with positioning sensors of order .
As dynamics is a consequence of kinematics, let us consider the form of direct and inverse kinematics with th order sensor inclusion.
Direct kinematics. The DenavitHartenberg (DH) convention (see [8, 9]) is a commonly used method for selecting frames of reference and the relationship between them to obtain the kinematics mathematical model for robotic plants. This method uses arrays as those given by (16) to represent the orientation and the positioning of the end effector (i.e., the tool of the robot) [10, 11]. where is a matrix for the orientation, is a array for the position of the tool, and is the socalled homogeneous transformation matrix (see [12]).
The DH convention claims that it is possible to obtain both position and orientation, from the ()th to the th link, through two translation and two rotation movements, as is presented in the following homogeneous transformation matrix,
where , , , and are the DH parameters, and their values come from specific aspects of the geometric relationship between the coordinate frames for the mechanical configuration of the robot under study and the way to select each of the coordinate frames; , , and are the axes of the th coordinate frame; represents the rotation matrix with respect to ; is a translation matrix with respect to ; is a translation matrix with respect to ; and is a rotation matrix with respect to (see [8–13] for a detailed procedure in obtaining (17)).
To feed equation (17) with the values of , , and , Table 1 was constructed with the DH parameters of the robot under consideration, taking into account Figure 3 which shows the way in which the author has selected each of the coordinate frames (see [13] for more details).

Table 1 shows the articular plant positions , , and , which represent angular displacements as is shown in Figure 2; are the link lengths, which are illustrated in the right side of Figure 3.
A 3DoF robot arm has , as such, substituting DH parameters in (17), three homogeneous transformation matrices are obtained: , , and . To go from system 0 to system 3, we compute , obtaining the final position and orientation of the tool
with
Thus, from the last column in (18) and including (2), direct kinematics is obtained for the robot with order sensor inclusion, as follows where , , and are the spatial Cartesian coordinates of the end effector.
Inverse kinematics. Solving equation (20) for , , and , inverse kinematics (see [14]) is obtained. This can be done involving the MoorePenrose pseudoinverse Jacobian matrix and taking into account the chain rule , for the first time derivative of , given by , where is the Jacobian matrix (see [12]). Carrying out this procedure and involving (2), the inverse kinematics for the robot is given by
Attitude in the orientation of the end effector. Three elements of the submatrix inside (16) are needed to obtain , , and which represent the roll, pitch, and yaw movements of the end effector, respectively (see [13]):
Now, taking (18) as the master matrix and solving for , , and , the orientation of the end effector of 3DoF anthropomorphic robot arms with any order linear positioning sensors is obtained as
The same results presented in (20)–(23) are obtained including directly in (18) the DH parameters with sensor system inclusion given by Table 2, concluding that kinematics is invariant to sensor system inclusion.

2.2.1. Robot’s Dynamics
Let us represent the total torque vector () of the robot under consideration according to the EulerLagrange approach, as where is the inertia matrix, is the potential energy vector, and is the friction force vector.
Using the methodology detailed in [15–17] to obtain the torque for the th link, (24) is transformed to the following form where is the Lagrangian given by with and as the kinetic and potential energies, respectively, which according to the referenced methodology are calculated for this work as where , , and are the inertia, mass, and length vectors, respectively, for the th centroid, and .
Separating each element of vector (25) and calculating where
Defining the Coriolis forces and gravitational vectors, respectively, as and including (2) in (24), the torque vector takes the form
Rearranging results presented in (29) and (30) and also including (2), the elements of (32), (, , and ), are obtained and given in the following form where where
Additionally, the friction vector symbolic form must be included in the dynamic model.
Even more, the same results presented in (33)–(39) are obtained, when (2) is directly included in the EulerLagrange (24) and in (31), as follows where
which proves dynamics invariance to sensor inclusion.
2.2.2. Extensive Considerations
Even more, for a serial robot with 2DoF, from Tables 1 to 2, line must be removed and (16) becomes with
Also, when a 1DoF is considered, from Tables 1 to 2, line must be removed and (16) becomes with
Since procedures (16)–(40) have intermediated outcomes, it is easy to compute results for a 1DoF and for a 2DoF robot, to obtain the same conclusions.
Further, invoking the induction method, these results can be extended to serial robots with DoF for all .
In order to deal with sensor inclusion mathematical complexity, the following remarks are useful for practical implementations:
Remark 4. For the case to consider matrix as a constant, the following identity should be used, in order to simplify the algebraic complications,
And for the case to consider matrix ,
Remark 5. For initial conditions at the origin , the Laplace transform for sensors of the th order with , such that
2.3. Control Using th Order Sensors’ Output
Now, in order to take advantage of Claim 1, the following control conclusions are given:
Assumption 3. It is assumed that sensors are previously well characterized, i.e., both (the order of every sensor) and sensor’s dynamics are known; this implies that .
Claim 2. Supported by Theorems 2 and 3, and if , it can be claimed that equation (2) becomes the method to identify from to control (14), which represents the robot with th order sensor inclusion. Thus,
where is the tracking error, and represents the reference signal.
The method is depicted in Figure 4, where the dashed squared block of the negative feedback represents (2).
Note 1. Solution of (2) cannot be directly used to control (6).
Extension of Claim 2. In [18], the Lasalleinvariance principle (generally used to show asymptotic stability in the Lyapunov approach) is extended to nonautonomous switching systems. This implies that Claim 2 is valid using both continuousbased and discontinuousbased controllers, e.g., Proportional Integral Derivative (PID) control and Sliding Mode (SM) controllers.
Even more, in [19], an extensive study to show the control of MIMO systems by the twistingalgorithm (a secondorder SM control) is presented, showing asymptotic convergence of the system trajectories to the selected sliding manifold.
Further, in order to review the stability for biorder SM (twistingalgorithm included) control with variable PID gains, [20] can be consulted.
3. Implementation Example
To verify the validity of Claims 1 and 2, this example is designed to solve the trajectory tracking control of the anthropomorphic robot with 3DoF (see Figures 2, 3, and 5) when th order sensors are included on it and when conditions (including assumptions) are accomplished.
Selecting , and linear sensors, with constant coefficients of order two, zero, and one, are included in joints one, two, and three, respectively, as
Reference signal to track for the robot is selected as