**Acknowledgements**

This work was supported by the Spanish MEC under Projects TEC2013-48102-C2-1/2-P, TEC2016-78028-C3-1-P and grant BES-2011-051305, the Mexican CONACYT under fellowship 410742, and the Unidad de Excelencia Maria de Maeztu MDM-2016-0600 financed by the AEI, Spain.


*2.10. Numerical Simulation in MATLAB/SIMULINK*

Validating simulations were performed in MATLAB/SIMULINK Release R2021a with Euler integration solver (ode1) and a fixed time step of 0.01 s, whose results are presented in Section 3, while this subsection displays the SIMULINK models permitting the reader to duplicate the results presented here. Sensor noise was added per Section 2.8. The classical feedback subsystem is displayed in Figure 4a. The optimal open loop subsystem implements Equation (31), and is elaborated in Figure 4b,c. The real time optimal subsystem implements Equations (42) and (31) augmented by feedback decoupling as in Equation (42). The "switch to open loop" subsystem switches when the matrix inverted in Equation (39) is singular indicated by a zero valued determinant and is elaborated in Figure 4d. The quadratic cost calculation computes Equation (3) and is elaborated in Figure 4b, while the cross-product motion feedback implements the cross product of Equation (42). The P + V subsystem and PD/PI/PID subsystems depicted in Figure 4a implement classical methods not re-derived here, but whose computer code is presented in Appendix B, Algorithms A1 and A2.

Figure 5 displays the SIMULINK subsystems used to implement the three instantiations of real-time optimization (labeled RTOC from provenance in optimal control) where the switching displayed in Figure 3b permits identical simulation experiments to be performed with all conditions fixed, varying only the proposed implementation. The subsystems execute Equation (39) with three variations of matrix inversion: (1) MATLAB's backslash "\", (2) Moore-Penrose pseudoinverse (*pinv*), (3) *LU-inverse*.

**Figure 5.** SIMULINK subsystems; (**a**) real-time optimal decision switching topology; (**b**) real-time optimal calculation of Equation (39) using *backslash*\*inverse*; (**c**) real-time optimal calculation of Equation (39) using *pinv inverse*; (**d**) real-time optimal calculation of Equation (39) using *LU inverse*.

Section 2.10 presented SIMULINK subsystems used to implement the equations derived in the section. Table 1 displays the software configuration used to simulate the equations leading to the results presented immediately afterwards in Section 3.

**Table 1.** Software configuration for simulations reported in Section 3.

#### **3. Results**

Section 2 derived several options for estimating state, rate, and control simultaneously as outputs of Pontryagin's treatment of the problem formulated as Hamiltonian systems. Section 2.9 described implementation of sensor noise narratively, while Figure 3 illustrated the topological elaboration using SIMULINK including state and rate sensor with added Gaussian noise whose noise power was set in accordance with Section 2.9. SIMULINK subsystems were presented to aid repeatability (with callback codes in the Appendix B). Those subsystems were used to run more than ten-thousand simulations: a nominal simulation run for each technique with the remainder utilized to evaluate vulnerability to variations as described in Section 2.9. In Section 3.1, benchmarks of performance are established using classical methods for state and rate errors and optimum cost calculated in Section 2. Sections 3.2–3.4 describe real-time optimal utilization of feedback to establish online estimates of the solution of the modified boundary value problem described in Section 2. Each section respectively evaluates the three methods compared: *backslash*\*inverse*, *pinv inverse*, and *LU inverse*.

General lessons from the results include:

#### *3.1. Benchmark Classical Methods*

Classical methods as presented in [41,42] with nonlinear decoupling loops as proposed in Equation (42) depicted in Figure 3b were implemented in SIMULINK according to Figure 4a. Computer code implementing these classical methods is presented in Appendix B, Algorithm 1. Estimation was executed by feedback of proportional plus integral plus derivative (PID), proportional plus derivative (PD), and also proportional plus velocity, and the results displayed in Figure 6, establishing the benchmark for state and rate tracking. Table 2 displays quantitative data corresponding to Figure 6's qualitative displays. Notice the optimal estimation of state, rate, and decision criteria is also included in Figure 6 and Table 2, since the optimal cost benchmark is established by Pontryagin's treatment in Equation (31).

**Figure 6.** Comparison of classical methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: thinner solid blue line is manually tuned PID, dashed line is linear quadratic optimal PD, dotted line is proportional plus velocity tuned to performance specification, thicker solid black line is open loop optimal per Pontryagin (Equation (31) provided for anticipated comparison). (**a**) States, (**b**) rates, and (**c**) decision criteria). Notice the solid black line representing the optimal open loop solution in subfigure (**c**) is initially positive to create movement in the desired direction, and the control switches exactly at the halfway point to a negative input, slowing progress towards the final desired end states (position and rate states). The ranges of the zoomed view in the inset are indicated by the respective scales.

#### *3.2. Real-Time Optimal Methods with Backslash*

This section displays the results of real-time optimal estimation using the *backslash*\*inverse* depicted in Figure 5b with and without singular switching displayed in Figure 4d to inverse the [*T*] in Equation (39). The results are compared to open loop optimal results per Equation (31) displayed in Figure 4c. Figure 7 reveals real-time optimal state estimation performs relatively poorly using the MATLAB *backslash*\*inverse*, but performance is restored to nearoptimal performance when augmented with singular switching. State and rate errors are restored to essentially optimal values, while cost is restored to very near the optimal case as evidenced by the quantitative results displayed in Table 3.

**Figure 7.** Comparison of real-time optimal methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: solid line is open loop optimal (benchmark), dashed line is real-time optimal using *backslash*, thick dotted line is real-time optimal using *backslash* with singular switching. (**a**) States, (**b**) rates. Notice the solid black line representing the optimal open loop solution in subfigure (**c**) is initially positive to create movement in the desired direction, and the control switches exactly at the halfway point to a negative input, slowing progress towards the final desired end states (position and rate states). The ranges of the zoomed view in the inset are indicated by the respective scales.

Figure 8 and Table 4 reveal the estimation performance of the constants of integration solving the modified two-point boundary value problem (BVP) using state and rate feedback to reset the initial conditions of the BVP. Oddly, despite relatively superior performance estimating the state and rates when using singular switching augmentation, parameter estimation is far inferior.

**Figure 8.** Comparison of real-time optimal methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: dashed line is real-time optimal using *backslash*, dotted line is real-time optimal using *backslash* with singular switching. (**a**) Estimates of *a*ˆ, (**b**) estimates of ˆ *b*. The ranges of the zoomed view in the inset are indicated by the respective scales.

**Table 4.** Comparison of real-time optimal decision methods using *backslash* matrix inversion.

Section 3.1 presented the results of classical and optimal methods as benchmarks for performance. Meanwhile, Section 3.2 presented the results of implementing real-time optimal estimation with *backslash*\*inverse* with and without singular switching compared to the optimal benchmark. Next, Section 3.3 presents results using *pinv inverse* with and without singular switching.

#### *3.3. Real-Time Optimal Methods with Pinv*

Inversion of the [*T*] matrix in Equation (39) was also accomplished by the Moore– Penrose pseudoinverse equation: [*T*] −1 ∼= [*T*] † ≡ [*T*] *T*[*T*] −1 [*T*] *T*. All other facets of the problem are left identical, while only the method of matrix inversion is modified resulting in state and rates estimates and comparison of control in Figure 9 with corresponding quantitative results in Table 5. Parameter estimation accuracy is displayed in Figure 10 and Table 6.

**Figure 9.** Comparison of real-time optimal methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: solid line is open loop optimal (benchmark), dashed line is real-time optimal using backslash, thick dotted line is real-time optimal using *pinv* with singular switching. (**a**) States, (**b**) rates, and (**c**) decision criteria or control. The ranges of the zoomed view in the inset are indicated by the respective scales.

**Table 5.** Comparison of real-time optimal decision methods using *pinv* matrix inversion.

**Figure 10.** Comparison of real-time optimal methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: dashed line is real-time optimal using backslash, dotted line is real-time optimal using *pinv* with singular switching. (**a**) Estimates of *a*ˆ, (**b**) estimates of ˆ *b*. The ranges of the zoomed view in the inset are indicated by the respective scales.

**Table 6.** Comparison of real-time optimal decision methods using *p-inv* matrix inversion.

#### *3.4. Real-Time Optimal Methods with Lu-Inverse*

Inversion of the [T] matrix in Equation (39) was also accomplished by the *LU-inverse,* which first creates a pivoted version *Tp* and then inverts the product of a lower triangular matrix, [*L*] and an upper triangular matrix [*U*] based on [*T*]: [*T*] −1 ∼= *Tp* −1 ≡ ([*L*][*U*])−1 . All other facets of the problem are left identical, while only the method of matrix inversion is modified resulting in state and rates estimates and comparison of control in Figure 11 with corresponding quantitative results in Table 7. Parameter estimation accuracy is displayed in Figure 12 and Table 8.

**Figure 11.** Comparison of real-time optimal methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: solid line is open loop optimal (benchmark), dashed line is real-time optimal using backslash, thick dotted line is real-time optimal using *LU-inverse* with singular switching. (**a**) States, (**b**) rates, and (**c**) decision criteria or control. The ranges of the zoomed view in the inset are indicated by the respective scales.

**Table 7.** Comparison of real-time optimal decision methods using *LU-inverse* matrix inversion.

**Table 8.** Comparison of real-time optimal decision methods using *LU-inverse* matrix inversion.

**Figure 12.** Comparison of real-time optimal methods with scaled time on the abscissae and respective ordinates titled in the subplot captions: dashed line is real-time optimal using backslash, dotted line is real-time optimal using *LU-inverse* with singular switching. (**a**) Estimates of *a*ˆ, (**b**) estimates of ˆ *b*. The ranges of the zoomed view in the inset are indicated by the respective scales.

### *3.5. Monte Carlo Analysis Using Pinv (with Singular Switching) and Open Loop Optimal with Cross-Product Deoupling*

Over ten-thousand simulation runs were performed with 10% uniformly random variations in plant parameters (mass and mass moment of inertia). Noise was added to state and rate sensors with zero mean and standard deviation 0.01, and the results are displayed in the "scatter" plots in Figure 13 with corresponding quantitative results displayed in Table 9. Feedback implemented by resetting the initial condition of the reformulated boundary value problem (when implemented with singular switching) yielded optimal results when augmented with cross-product decoupling.

**Figure 13.** Comparison of the impacts of system variations on real-time optimal using (**a**) open loop optimal, (**b**) *pinv* without switching, (**c**) *pinv* with switching. Scaled state on the abscissae and scaled rate on the ordinates.

**Table 9.** Impact of variations with real-time optimal decision methods using *pinv* inversion.

#### *3.6. Comparison of Results*

Section 3.1 presented the benchmark results produced by classical methods and open loop optimal mathematical solutions. Section 3.2 presented results utilizing MATLAB's *backslash*\*inversion*, while Section 3.3 included results using Moore–Penrose pseudoinverse, *pinv*. Section 3.4 presented results using *LU-inverse*. Section 3.5 revealed robustness to variations in plan parameters with both state and rate sensor noise. This section consolidates the results into a single table of raw data depicted in Table 10. This data will be used to produce percent performance improvement as figures of merit in the Discussion (Section 4).

**Table 10.** Comparison of real-time optimal decision methods.

#### **4. Discussion**

State and rate estimation algorithms fused with noisy sensor measurements using several of the proposed methodologies achieve state-of-the art accuracies with optimality that is analytic and deterministic rather than stochastic, and therefore use very simple equations with necessarily low computational burdens. Simple relationships with small numbers of multiplications and additions are required to be comparable to the simplicity of classical methods, but optimum results are produced that exceed the modern notion of linear-quadratic optimal estimation. Implementation of non-standard feedback achieves robustness with the additional computational cost of a matrix inverse, and therefore three optional inversion methods were compared. General lessons taken with manually tuned PID as a benchmark for state and rate estimation errors, while optimal loop optimal cost is the benchmark for the cost of utilization of state estimations for guidance and control:

	- a. Linear-quadratic optimal estimation achieved 87% better state estimates, but over 400% poorer rate estimates compared to classical PID with costs over 2000% open-loop optimal costs.
	- b. Classical position plus velocity estimation achieved 90% improved state estimation with over 30% better rate estimation, but cost of implementation remains high (over 400% higher than the optimal benchmark).
	- a. Singular switching with *backslash*\*inverse* produced 69% improvement in state estimation and 29% improvement in rate estimation with roughly optimal costs.
	- b. Singular switching with *LU-inverse* produced 72% improvement in state estimation and 33% improvement in rate estimation with roughly optimal costs (3% better than optimal . . . a numerical curiosity).
	- c. Singular switching with *pinv inverse* produced 69% improvement in state estimation and 29% improvement in rate estimation with roughly optimal costs

(approximately identical improvement percentages to *LU-inverse* with singular switching).

#### *4.1. Notes on Percentages of Performance Improvements*

The choice of benchmarks for establishing percentage performance improvements leads to seemingly exaggerated numbers. The current selection of benchmarks emphasizes the strengths of the respective methods: classical feedback estimation methods are designable to achieve high accuracy but suffer from high effort by the decision criteria associated with their use. Optimal methods as instantiated here emphasize minimization of decision effort, so the benchmark for control effort is selected as optimal open loop rather than classical feedback (e.g., manually tuned PID). Percent degradation over thirty thousand percent results when compared to the optimal value (of six) as a benchmark. If the calculation had instead used the manually tuned classical PID as a benchmark, the optimal effort would exhibit an improvement over ninety-nine percent.

The final line in Table 11 illustrates the extreme penalty of not using feedback decoupling of the vector cross-products in Equation (1) representing translation due to the rotating reference. The penalty embodies the deleterious effects of neglecting treatment of the nonlinear, coupled, full six-degree-of-freedom system of equations.

### *4.2. Notes on Executability*

Table 12 displays simulation run time for the eleven methods compared in the manuscript. Run-time was established by establishing the run start-time with the *tic* command, while simulation stop-time was the very first command (*toc*) following each simulation run. Simply neglecting the cross-product terms results in the fastest calculation. All the methods evaluated are of the same order of magnitude of computational burden. Simulations are depicted graphically in Figures 2–5 and alphanumerically in Appendix B.

**Table 12.** Simulation run-time comparison.

#### *4.3. Future Research*

Notice in Figure 3c sinusoidal wave input is coded using the identical time-index of the rest of the simulation. The next stages of future research will utilize this identical simulation to investigate efficacy of the proposed virtual sensoring amidst unknown wave actions. Secondly, hardware validation of key facets of this research is a logical next step.


**2. Electrochemical codeposition of MMC coatings**

Electroplating or electrodeposition is an electrochemical process that occurs at the interface between a conductive material (electrode) and a conductive solution (electrolyte). As a whole, the process can be explained as the result of applying an electrical potential to the electro‐ chemical system by an external source, and the consequent flow of electric current through the electrode-electrolyte interface can be measured (chronoamperometry) [29–31]. On the contra‐ ry, an electric current can also be applied, and the generated potential difference is then the measured variable (chronopotentiometry) [32]. In the electrolytic cell of an electrodeposition process, the cathode is generally the working electrode, which will be coated during the procedure, whereas the anode is the counter-electrode. The third electrode is the reference electrode used to monitor the working electrode potential [30,33–35].

**Deposition process**

150 Electrodeposition of Composite Materials

**Substrate Metallic**

**matrix**

**Table 1.** Examples of MMC coatings produced by different deposition processes.

**2. Electrochemical codeposition of MMC coatings**

reliability to prevent corrosion.

**Second phase Main features Ref.**

to the manual one;

adjusted

Although all of the aforementioned processes may be used to produce functional MMC coatings, the most widely used production method is electrochemical codeposition, also known as electrocodeposition, which consists of incorporating nanoparticles or nanowires (generally nonmetallic ones) intentionally added to the electrolyte to the metallic matrix during the electrodeposition process. This technique has been under investigation for several decades, and some authors have proposed models to explain the codeposition phenomenon of the particles during the formation of a cathodic deposit by electrodeposition [6,8,11,23–25]. This topic is still up to date because the process is more complex than the traditional electrodepo‐ sition and no commercial electrochemical baths have been developed so far for industrial production of these types of composite coatings. The main parameters that affect the process (e.g., solution pH, stirring speed, and current density) were related; however, there is no consensus in the literature concerning their effects in the nanoparticle content in the coating and in the anticorrosive performance of the coating/substrate system [5,6,8,11,12,17,23,26–28]. It is also necessary that the nanoparticles be maintained suspended and dispersed (nonag‐ glomerated) in the electrolyte during the deposition process; otherwise, the precipitation of the nanoparticles occurs and causes the loss of control of the electrochemical codeposition process. Therefore, it is important to present a more fundamental review of the parameters that involve the electrodeposition process of these coatings to obtain a better understanding of the electrochemistry codeposition phenomenon and its consequence on the anticorrosive properties of the composite coatings. Consequently, it will lead to an improvement of the processes for the development of new functional nanocomposite coatings and increase their

Electroplating or electrodeposition is an electrochemical process that occurs at the interface between a conductive material (electrode) and a conductive solution (electrolyte). As a whole, the process can be explained as the result of applying an electrical potential to the electro‐ chemical system by an external source, and the consequent flow of electric current through the

with the automatic process compared

The main advantage of this technique is the production of coatings with thickness varying from a few layers up to 40 μm, relatively free of pores. Compared to the plasma processes (PVD or CVD), it is a less expensive technique and can be conducted at room temperature, normal pressure, and high deposition rate [34,36,37]. In addition, it is economically important because even thin-layer coatings produced by electrodeposition can offer adequate protection to the substrate, avoiding excess of electrodeposited metal [29,34]. The most common coatings produced by this process are the metallic ones: nickel, chromium, copper, zinc, tin, brass, silver, and cadmium [31].

The electrodeposition technique also allows the production of coatings composed by a second phase dispersed in a metallic matrix, producing MMC coatings. This second phase can be an organic or inorganic compound or even a metal particle suspended in the solution [8,34]. This is the main difference between the usual electrodeposition of metallic ions and the electrode‐ position of MMC coatings (electrochemical codeposition): instead of using a pure ionic soluble solution as the electrolyte, particles of nonconductive, semiconductive, and/or conductive nature are suspended in electrolyte solution [11,26,38–40]. Thus, during the codeposition process, both the metal matrix and the particles are deposited on the substrate, producing the MMC coatings [26,38,39].

The greatest challenge faced by those who study the codeposition of nanoparticles in MMC coatings by electrochemical codeposition seems to be the development of a methodology to deposit a sufficient amount of particles to promote the desired improvements in the properties (anticorrosive characteristics, mechanical resistance, etc.) of coatings compared to those obtained with the pure metallic coating. Additionally, it is also necessary to prevent the agglomeration of the particles in the electrolyte solution [23].

A better understanding of the electrocodeposition process is obtained by the knowledge of the process and mechanisms that involve the metal/particle codeposition. Several parameters influence the deposition process, such as applied current density (or potential), concentration of particles in the bath, size of the particles, stirring speed of the suspension, time of previous stirring, solution pH, bath temperature, and electrical nature of the particles. The stability of the suspension of particles to be added to metallic matrix also affects the codeposition process. Although these parameters will be presented in this revision separately, it is important to point out that several of them usually present mutual interactions and these effects can also influence the final results (the amount of codeposited particles, for example) as well as the final prop‐ erties of the coating.


**4. Conclusions**

The current work evaluated the use of POME as an agro-industrial waste for landfill leachate treatment. Biological treatment using an aeration process for different mixing ratios between leachate and POME was performed. The optimization of the removal efficiencies for COD, TSS, color, and NH3-N for the aeration treatment process of old leachate was investigated in this study. The optimum operational conditions were obtained at 188.32 mL of POME added to 1 L of leachate for 21 days of aeration time. The respective percentage removal efficiencies were 89.83%, 66.7%, 91.7%, and 94% for COD, TSS, color, and NH3-N. Moreover, the treatment process reported an efficient removal of some heavy metals from landfill leachate. The results revealed that POME-based agricultural waste can be effectively used for organic removal from stabilized leachate.

**Author Contributions:** Formal analysis, T.J.H.B.; writing—original draft preparation, T.J.H.B.; writing—review and editing, M.M.H., S.S.A.A., A.F.M.A., and M.H.; supervision, M.M.H. and S.S.A.A.; funding acquisition, M.M.H. All authors read and agreed to the published version of the manuscript.

**Funding:** This research was partly funded by UKM research grants (DIP-2019-001 and MI-2020-005). **Acknowledgments:** Marlia M. Hanafiah was supported by the Ministry of Education Malaysia (FRGS/1/2018/WAB05/UKM/02/2) and a UKM research grant (DIP-2019-001).

**Conflicts of Interest:** The authors declare no conflict of interest.


**3.2 Meteorological and agricultural drought**

From the reviewed papers, 37.5% was covered meteorological drought analysis and monitoring studies, and agricultural drought studies were covered 12.5% (**Table 4**). Ethiopia is highly dependent on rainfed agriculture; so, meteorological and

*Review of Hydrological Drought Analysis Status in Ethiopia DOI: http://dx.doi.org/10.5772/intechopen.102763*

agricultural drought analysis, monitoring, and early warning system development are crucial. But still, there is no well-adopted drought analysis technique for a nationwide or a regional level. As a result, the development of drought early warning system has lacked. At the same time, hydrological drought analysis and monitoring is also key point for river basin development and water resource management. But due to its large input data requirement, hydrological drought study is not further investigated.


**6.2. System architecture**

A robot skin can be seen as a complex system formed by a large number of spatially distributed sensing elements with embedded local processing electronics [30]. The robotic skin should be designed in order to be independent from and compliant to different robotic platforms. Small/large scale-tailor-made robotic skin could require different transduction principles (capacitive, piezoelectric, etc.) for the multimodal transduction of contact features.

The robot skin design should focus on the following topics:

630 Smart Actuation and Sensing Systems – Recent Advances and Future Challenges

the sole normal component is shown in Figure 13.

**Figure 13.** Algorithm output: normal traction on the outer surface.

context, can be also retrieved within a certain approximation.

data processing into embedded electronics systems.

data set analysis.

**6.2. System architecture**

an elliptical area on the outer surface. The model was previously evaluated by a FEM

The algorithm described above has been applied to retrieve the 3 traction components on the loading area, having the sensor stress values as inputs (*b* vector). The result concerning

The shape of the distribution as well as its peak value appears to be in very good accord with the assigned data. The tangential components, which are not shown in the present

A crucial feature of the present research is that the proposed algorithm should be efficiently implemented on digital hardware. This in turn allows for real-time implementation of tactile

In a wider perspective, this work is intended as a first step towards the integration of different techniques for tactile information processing (e.g. computational intelligence), possibly implemented at different levels of the transmission line towards the robot central processor. In its difference from statistical approaches such as Machine Learning, the advantage of the present algorithm is that it does not require time-consuming training and

A robot skin can be seen as a complex system formed by a large number of spatially distributed sensing elements with embedded local processing electronics [30]. The robotic

computation and the normal stress at the center of each sensor was calculated.

Despite innovative designs, a large number of sensors have been rendered "bench top," as the emphasis has been on the sensors, and the system (in particular the underlying embedded electronic system) has largely been ignored [11]. Only few tactile sensing arrays with electronic circuitry on chip with sensors have been presented so far. Those having any possess circuitry with minimal complexity, e.g., a single MOS transistor associated with each transducer.

To overcome the limits of current implementations, i.e. to design and develop large area tactile sensing arrays to be flexible, conformable, and stretchable and at the same time to be intimately integrated with the embedded electronic system and with functional (i.e. PVDF in our case) and structural materials, researchers need to address the development of *the tactile embedded electronic system* which intimately copes and integrates with technology and devices on one side and with system features and constraints on the other one.

The functions to be implemented by the *tactile embedded electronic system* are various and demand covering the whole signal chain: sensors biasing, signal conditioning (e.g. low noise amplification, low pass filtering, etc.), matrix readout, signal acquisition (i.e. Analog to Digital conversion), local digital signal processing, communication bus interface, etc. For instance one of the tasks of the tactile embedded electronic system is interfacing heterogeneous sensors, with different read out circuit modes, to the robot skin electronic infrastructure.

Based on the assessment of the sensing devices performance and on the system requirements, researchers need to address a proper and cost effective partitioning of the

*tactile embedded electronic system* between dedicated and COTS implementation. The design methodology and the partitioning must take into account the high number and types of sensors to be read, the high sampling bandwidth for some of them, the high expected data throughput, the limited communication bus bandwidth, the need for a low complexity implementation i.e. a small number of devices and interconnections, etc. Due to the complexity and diversity of tasks to be implemented, the digital core and part of the signal conditioning/data acquisition blocks need to be implemented by dedicated silicon electronic circuits (i.e. Application Specific Integrated Circuit, ASIC).

Dedicated communication strategies are needed to transmit the large amount of data (due to the high sampling rate as in the case of the PVDF sensors, i.e. at least 2 kSamples/s per sensor, and due to the large number of sensors in each array) collected by the tactile sensors arrays distributed over the whole body [31]. In this context, the hierarchical architecture of the communication bus and the local data processing (for a number of tasks e.g. feature extraction, data compression, etc.) is explored. Going from lower levels (i.e. skin) to higher levels (i.e. central processing unit) protocols are different. The desired operation speed, noise and number of wires put a constraint on the type of communication channel used for interaction with higher levels. Serial communication buses are used (e.g. I2C, Can bus, Flexray, Ethernet, etc.) to decrease wiring. The buses using CAN protocol are generally a preferred choice mostly due to the real-time capabilities, high reliability, and readily availability on most microcontrollers. But, the CAN bus suffers from a moderate transmission bandwidth (up to 1 Mbits/s) which will either slow down transmission of tactile data from a large number of sensors or put a cap on the number of touch sensors on the body. These issues can be solved either by using buses with higher transmission bandwidth (e.g. up to 10 Mbits/s can be achieved with FlexRay) or using more buses in parallel - which is anyway undesirable.

Due to the requirement of real time needed to use the tactile feedback in the control loop, deterministic protocols are mandatory. Going from periphery upwards, bandwidth of the bus increases in order to accommodate an increasing amount of data; protocol complexity increases as well. At lower levels high speed, lower connectivity and short distance wiring buses are preferred (e.g. I2C). Moving up in the hierarchy, more complex protocols and longer wiring buses are preferred (e.g. CAN, Flexray, real-time Ethernet like Ethercat).

**Figure 14.** Block diagram of the system architecture with a triangle made of PVDF transducers.

The tactile sensing system based on PVDF transducer arrays proposed in this chapter [30] is based on a conformable mesh of sensor patches having a triangular shape [32]. On the bottom of the triangular substrate – which is in contact with the robot structure – the blocks of the interface and the local data acquisition/processing electronics are embedded (see Figure 14). The top surface hosts an array of 12 sensors/detectors covered by a PDMS protective layer. Each triangle is interconnected to each other to create a networked structure. Each patch is implemented on a flexible substrate allowing the system to conform to smooth curved surfaces of the robot body (i.e. upper limbs, torso, back, etc.). The PVDF film transducer is provided of two electrodes connected to wires which transfer the generated charge to the interface electronics. The PVDF sensor is used to detect tactile stimuli in the 1 Hz to 1 kHz range (corresponding to the human tactile sensing bandwidth). In the architecture shown in Figure 14, the 12 output signals from the triangle array are in input to the interface electronics (see Section 4). The interface electronics outputs the signals to three ADCs (MAX116136) - each one manages 4 channels – and subsequently, through I2C buses, the signals are routed at 11kSample/s to the microcontroller (PIC24FJ64GB004 Family7). At present, skin patches (i.e. triangles) are interconnected through a CAN bus (the reference robotic platform is iCub8).

**Figure 15.** Tactile sensing system. iCub picture is printed by courtesy of IIT (Italian Institute of Technology).

632 Smart Actuation and Sensing Systems – Recent Advances and Future Challenges

circuits (i.e. Application Specific Integrated Circuit, ASIC).

*tactile embedded electronic system* between dedicated and COTS implementation. The design methodology and the partitioning must take into account the high number and types of sensors to be read, the high sampling bandwidth for some of them, the high expected data throughput, the limited communication bus bandwidth, the need for a low complexity implementation i.e. a small number of devices and interconnections, etc. Due to the complexity and diversity of tasks to be implemented, the digital core and part of the signal conditioning/data acquisition blocks need to be implemented by dedicated silicon electronic

Dedicated communication strategies are needed to transmit the large amount of data (due to the high sampling rate as in the case of the PVDF sensors, i.e. at least 2 kSamples/s per sensor, and due to the large number of sensors in each array) collected by the tactile sensors arrays distributed over the whole body [31]. In this context, the hierarchical architecture of the communication bus and the local data processing (for a number of tasks e.g. feature extraction, data compression, etc.) is explored. Going from lower levels (i.e. skin) to higher levels (i.e. central processing unit) protocols are different. The desired operation speed, noise and number of wires put a constraint on the type of communication channel used for interaction with higher levels. Serial communication buses are used (e.g. I2C, Can bus, Flexray, Ethernet, etc.) to decrease wiring. The buses using CAN protocol are generally a preferred choice mostly due to the real-time capabilities, high reliability, and readily availability on most microcontrollers. But, the CAN bus suffers from a moderate transmission bandwidth (up to 1 Mbits/s) which will either slow down transmission of tactile data from a large number of sensors or put a cap on the number of touch sensors on the body. These issues can be solved either by using buses with higher transmission bandwidth (e.g. up to 10 Mbits/s can be

achieved with FlexRay) or using more buses in parallel - which is anyway undesirable.

**Figure 14.** Block diagram of the system architecture with a triangle made of PVDF transducers.

Due to the requirement of real time needed to use the tactile feedback in the control loop, deterministic protocols are mandatory. Going from periphery upwards, bandwidth of the bus increases in order to accommodate an increasing amount of data; protocol complexity increases as well. At lower levels high speed, lower connectivity and short distance wiring buses are preferred (e.g. I2C). Moving up in the hierarchy, more complex protocols and longer wiring buses are preferred (e.g. CAN, Flexray, real-time Ethernet like Ethercat).

6 http://datasheets.maxim-ic.com/en/ds/MAX11612-MAX11617.pdf

7 http://ww1.microchip.com/downloads/en/devicedoc/39940c.pdf

8 www.icub.org

To be able to integrate the interface and the data acquisition/local processing electronics onto the bottom of each triangle, and due to the small space available, an applicationspecific integrated circuit (ASIC), see Figure 15, which embeds: a) the interface electronics; b) data acquisition; c) dedicated signal processing; d) communication bus interface should be pursued. The feasibility and integration of the tactile sensing system on the robot will mainly depend on the design of the architecture of the ASIC.